Cerebras Inference vs Baseten

Agent access

1. ai-native userPoint an agent at llms.txt or agent-oriented docs

   weight 2 · round drawn
   Cerebras Inference✓fullprobed9/10

   Cerebras hosts a live llms.txt index (verified via probe returning HTTP 200 with a structured documentation index) and individual docs pages are also served in markdown form with explicit pointers back to llms.txt, exactly matching the agent-oriented docs pattern. This is directly confirmed by hands-on probes, not just vendor claims. Missing for 10: independent/community confirmation that an agent has actually consumed llms.txt successfully in practice.

   • [probe] “PROBE llms.txt: HTTP 200 at https://inference-docs.cerebras.ai/llms.txt # Cerebras Inference - [Quickstart](https://inference-docs.cerebras…”
   • [probe] “PROBE docs-md: HTTP 200 at https://inference-docs.cerebras.ai/quickstart.md > ## Documentation Index > Fetch the complete documentation inde…”
   • [claimed-docs] “Make your first Cerebras API call in just minutes.”

   proof ↗⚑ flag

   Baseten✓fullprobed9/10

   Baseten hosts a verified llms.txt (HTTP 200, indexing docs) and an agent-setup page directing agents to install skills/MCP servers to search the docs, confirmed live via a working MCP endpoint. missing for 10: independent third-party confirmation of an agent successfully using llms.txt end-to-end beyond Baseten's own probes.

   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.baseten.co/llms.txt # Baseten - [Baseten overview](https://docs.baseten.co/overview.md): Run hoste…”
   • [probe] “PROBE docs-md: HTTP 200 at https://docs.baseten.co/overview.md > ## Documentation Index > Fetch the complete documentation index at: https:/…”
   • [claimed-docs] “Install the Baseten skill and MCP servers so your coding agent can manage your Baseten workspace and search these docs.”
   • [probe] “official MCP server documented at https://docs.baseten.co/agent-setup”
   • [probe] “PROBE mcp-endpoint (2026-09-04): POST initialize to https://docs.baseten.co/mcp answered HTTP 200 with a JSON-RPC/MCP response (event: messa…”

   proof ↗⚑ flag

2. ai-native userRun the product headlessly / in CI for automation

   weight 2 · round drawn
   Cerebras Inference✓fullcommunity7/10

   Cerebras Inference is a pure REST API with official Python/Node SDKs, OpenAI-compatible endpoints, and a Batch API for asynchronous request processing—all of which are inherently headless and scriptable for CI/automation pipelines (cerebras-docs-1, cerebras-docs-3, cerebras-docs-8, cerebras-docs-17, cerebras-gh-2). Community reports confirm real-world automated/agentic usage (coding agents, voice assistants) via API keys without needing the console UI (cerebras-comm-6, cerebras-comm-16), though some hit rate-limit friction in automated integrations (cerebras-comm-11). Missing for 10: explicit CI/CD examples (e.g., GitHub Actions), dedicated CLI tool documentation, and no first-party guidance on running in headless/CI environments specifically.

   • [claimed-docs] “Make your first Cerebras API call in just minutes.”
   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [claimed-docs] “The Batch API lets you process groups of requests asynchronously, making it perfect for workloads where you don't need immediate results”
   • [claimed-docs] “pip install --upgrade cerebras_cloud_sdk”
   • [github] “This library provides convenient access to the Cerebras REST API from server-side TypeScript or JavaScript.”
   • [community] “The DFlash2 draft model we're using was trained on a lot of code, so if you use it in a coding agent you'll probably notice it run a lot fas…”
   • [community] “Here's a video of that running, it's very speedy - used llm-cerebras plugin with an API key from cloud.cerebras.ai, no waiting list needed a…”
   • [community] “It hits the request per minute limit instantly and then you wait a minute. (API Error: 422 ... wrong_api_format when integrating with claude…”

   proof ↗⚑ flag

   Baseten✓fullclaimed7/10

   Baseten provides a CLI (truss push, Baseten CLI/Switch), config.yaml-driven deployment, OpenAI-compatible REST APIs with async/webhook/polling support, and API-key-based auth/rate limiting, all of which support scripted, non-interactive CI/automation workflows. missing for 10: explicit CI/CD pipeline examples (e.g. GitHub Actions integration), independent hands-on confirmation of headless CLI use in automation, and documented non-interactive auth flow for CI secrets.

   • [github] “You write a `config.yaml` that specifies the model, the hardware, and the engine, then `uvx truss push` builds a TensorRT-optimized containe…”
   • [github] “Deploying a model to Baseten via Truss turns a Hugging Face model into a production-ready API endpoint. You write a `config.yaml` that speci…”
   • [claimed-docs] “Async inference returns a request ID quickly and completes later through webhook or polling, which suits batch work, long documents”
   • [claimed-docs] “Async inference returns a request ID quickly and completes later through webhook or polling, which suits batch work, long documents, or any …”
   • [claimed-docs] “Call hosted models through an OpenAI-compatible API, deploy your own models on dedicated infrastructure”
   • [claimed-docs] “x-ratelimit-remaining-requests: Reports how many requests remain before you reach the request rate limit.”
   • [claimed-docs] “You can also set a workspace budget and query usage by API key or model.”
   • [claimed-docs] “a `config.yaml` can define the model, hardware, and inference engine without custom serving code”

   proof ↗⚑ flag

3. ai-native userPlug MCP servers into this product so it can use their tools

   weight 3 · round drawn
   Cerebras Inference—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

   Baseten—none0/10

   Baseten's evidence only shows it exposing its own MCP server so external coding agents (Claude Code, Codex, Pi) can call Baseten's workspace tools — the reverse relationship. There is no evidence that Baseten itself can consume/plug in third-party MCP servers to gain access to their tools during inference or agent workflows.

   • [claimed-docs] “Install the Baseten skill and MCP servers so your coding agent can manage your Baseten workspace and search these docs.”
   • [claimed-docs] “Connect Claude Code, Codex CLI, or Pi with Baseten Switch.”
   • [claimed-docs] “Use [Baseten Switch](/reference/cli/baseten-switch) to route requests from Claude Code or Codex CLI to Model APIs.”
   • [probe] “PROBE mcp-endpoint (2026-09-04): POST initialize to https://docs.baseten.co/mcp answered HTTP 200 with a JSON-RPC/MCP response (event: messa…”
   • [probe] “official MCP server documented at https://docs.baseten.co/agent-setup”

   proof ↗⚑ flag

4. ai-native userConnect an agent via an official MCP server

   weight 3 · round to Baseten
   Cerebras Inference—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

   Baseten✓fullprobed8/10

   Baseten documents and hosts an official MCP server (docs.baseten.co/agent-setup, docs.baseten.co/mcp) that lets coding agents manage the Baseten workspace and search docs, and a live probe confirms the endpoint responds to MCP initialize with valid JSON-RPC capabilities. missing for 10: independent/community hands-on validation of the MCP server beyond the vendor's own probe, and more detail on the full tool surface exposed.

   • [claimed-docs] “Install the Baseten skill and MCP servers so your coding agent can manage your Baseten workspace and search these docs.”
   • [probe] “official MCP server documented at https://docs.baseten.co/agent-setup”
   • [probe] “PROBE mcp-endpoint (2026-09-04): POST initialize to https://docs.baseten.co/mcp answered HTTP 200 with a JSON-RPC/MCP response (event: messa…”

   proof ↗⚑ flag

5. ai-native userUse an official CLI

   weight 2 · round to Baseten
   Cerebras Inference—none0/10

   Evidence only shows Python/Node SDKs and a web playground/quickstart; there is no mention of an official Cerebras CLI tool anywhere in the docs, GitHub repos, or community discussion.

   • [claimed-docs] “pip install --upgrade cerebras_cloud_sdk”
   • [github] “This library provides convenient access to the Cerebras REST API from server-side TypeScript or JavaScript.”
   • [claimed-docs] “Use the playground in the Cloud Console — no key or install needed.”

   proof ↗⚑ flag

   Baseten✓fullclaimed7/10

   Baseten ships official CLIs: Truss (`uvx truss push` to build/deploy models from a config.yaml, per baseten-gh-2/6) and Baseten Switch (a CLI referenced at /reference/cli/baseten-switch for routing coding-agent traffic, baseten-docs-34), plus one-command checkpoint deployment (baseten-docs-29). missing for 10: independent/hands-on verification of CLI behavior, and a full CLI reference/command list beyond scattered doc mentions.

   • [github] “You write a `config.yaml` that specifies the model, the hardware, and the engine, then `uvx truss push` builds a TensorRT-optimized containe…”
   • [github] “Deploying a model to Baseten via Truss turns a Hugging Face model into a production-ready API endpoint. You write a `config.yaml` that speci…”
   • [claimed-docs] “Deploy any synced checkpoint with one CLI command”
   • [claimed-docs] “Use [Baseten Switch](/reference/cli/baseten-switch) to route requests from Claude Code or Codex CLI to Model APIs.”
   • [claimed-docs] “Connect Claude Code, Codex CLI, or Pi with Baseten Switch.”

   proof ↗⚑ flag

6. ai-native userDrive the product through a documented public API

   weight 3 · round to Cerebras Inference
   Cerebras Inference✓fullprobed9/10

   Cerebras ships a well-documented public REST API (OpenAI-compatible), official SDKs (Python/Node), quickstart docs, streaming, structured outputs, tool calling, batch API, and a live API endpoint confirmed by probe (HTTP 403 auth-gated but functional/JSON). Community evidence confirms real-world usage via SDKs and integrations (Cursor, claude-code-router, llm-cerebras plugin). Missing for 10: a discoverable OpenAPI/swagger spec (probe found 404s on all candidate paths), which would round out formal API documentation.

   • [claimed-docs] “Make your first Cerebras API call in just minutes.”
   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.”
   • [claimed-docs] “The Cerebras API supports streaming responses, which send messages back in chunks and display them incrementally as the model generates them…”
   • [claimed-docs] “Structured Outputs constrains model responses to a JSON schema so applications can process generated data reliably.”
   • [claimed-docs] “Tool calling, also known as tool use or function calling, lets a model request functions that your application defines.”
   • [claimed-docs] “The Batch API lets you process groups of requests asynchronously, making it perfect for workloads where you don't need immediate results”
   • [github] “This library provides convenient access to the Cerebras REST API from server-side TypeScript or JavaScript.”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.cerebras.ai/v1/models without a key returned HTTP 403 ({"detail":"Not authenticated"}) —…”
   • [probe] “PROBE llms.txt: HTTP 200 at https://inference-docs.cerebras.ai/llms.txt # Cerebras Inference - [Quickstart](https://inference-docs.cerebras…”
   • [community] “I've been waiting on this for a LONG time. Integration with Cursor when Cerebras released their earlier models was patchy at best, even thro…”
   • [community] “Here's a video of that running, it's very speedy - used llm-cerebras plugin with an API key from cloud.cerebras.ai, no waiting list needed a…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://inference-docs.cerebras.ai/openapi.json, https://inference-docs.cerebras.ai/swagger.json, ht…”

   proof ↗⚑ flag

   Baseten✓fullprobed8/10

   Baseten exposes an OpenAI/Anthropic-compatible inference API (docs-21, docs-30), a documented CLI/Truss deployment workflow, training APIs (docs-32, docs-36), and usage/rate-limit endpoints, all backed by extensive public docs and a live, probe-verified /v1/models endpoint and MCP server. Missing for 10: a discoverable OpenAPI/swagger spec (probe-3 shows 404s across candidate paths) and independent third-party API usage reports beyond community anecdotes.

   • [claimed-docs] “They support the OpenAI Chat Completions API and the Anthropic Messages API in beta, so you can use familiar client SDKs.”
   • [claimed-docs] “Call hosted models through an OpenAI-compatible API, deploy your own models on dedicated infrastructure”
   • [claimed-docs] “Async inference returns a request ID quickly and completes later through webhook or polling, which suits batch work, long documents”
   • [claimed-docs] “Fine-tuning or running RL on a supported base model: Loops provisions a dedicated trainer and paired sampler, and each training step is an A…”
   • [claimed-docs] “Model APIs bill by token and enforce request and token rate limits. You can also set a workspace budget and query usage by API key or model.”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://inference.baseten.co/v1/models without a key returned HTTP 401 (No Authorization header prov…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.baseten.co/openapi.json, https://docs.baseten.co/swagger.json, https://docs.baseten.co/…”
   • [github] “You write a `config.yaml` that specifies the model, the hardware, and the engine, then `uvx truss push` builds a TensorRT-optimized containe…”

   proof ↗⚑ flag

7. ai-native userIssue scoped/least-privilege API credentials for an agent

   weight 2 · round drawn
   Cerebras Inference—none0/10

   No evidence of scoped, least-privilege API key/credential issuance (e.g., role-based keys, permission scopes, or per-agent restricted tokens) — docs only mention basic API key usage for authentication, not fine-grained credential scoping. Missing for 10: any mention of scoped/permissioned API keys, role-based access control, or credential restriction features for agents.

   • [claimed-docs] “Make your first Cerebras API call in just minutes.”
   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.cerebras.ai/v1/models without a key returned HTTP 403 ({"detail":"Not authenticated"}) —…”

   proof ↗⚑ flag

   Baseten—none0/10

   Baseten documents API keys, usage monitoring by API key, and workspace budgets, but no evidence describes scoped/least-privilege credential issuance (e.g., role-based permissions, restricted-scope keys, or per-agent credential minting). Monitoring usage by key is not the same as issuing scoped credentials.

   • [claimed-docs] “Model APIs bill by token and enforce request and token rate limits. You can also set a workspace budget and query usage by API key or model.”
   • [claimed-docs] “To monitor token and request consumption by API key or model, see Usage.”
   • [claimed-docs] “You can also set a workspace budget and query usage by API key or model.”
   • [claimed-docs] “To raise a Basic account's limits, request email verification. You can also use that form to move to Pro or Enterprise.”

   proof ↗⚑ flag

8. ai-native userBuild against official SDKs

   weight 2 · round to Cerebras Inference
   Cerebras Inference✓fullcommunity8/10

   Cerebras ships official Python and Node/TypeScript SDKs (pip install cerebras_cloud_sdk, cerebras-cloud-sdk-node on GitHub) plus OpenAI-compatible client support, with docs covering streaming, tool calling, structured outputs, and batch APIs—clearly agentic-workflow-friendly. Community evidence confirms real-world SDK/agent integration (coding agents, Cursor support) though with some rough edges like rate-limit friction. Missing for 10: independent quality assessment of SDK docs/API reference completeness and broader language SDK coverage beyond Python/Node.

   • [claimed-docs] “pip install --upgrade cerebras_cloud_sdk”
   • [github] “This library provides convenient access to the Cerebras REST API from server-side TypeScript or JavaScript.”
   • [github] “This SDK has a mechanism that sends a few requests to `/v1/tcp_warming` upon construction to reduce the TTFT.”
   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.”
   • [claimed-docs] “Tool calling, also known as tool use or function calling, lets a model request functions that your application defines.”
   • [claimed-docs] “The Cerebras API supports streaming responses, which send messages back in chunks and display them incrementally as the model generates them…”
   • [claimed-docs] “Structured Outputs constrains model responses to a JSON schema so applications can process generated data reliably.”
   • [community] “I've been waiting on this for a LONG time. Integration with Cursor when Cerebras released their earlier models was patchy at best, even thro…”
   • [community] “The DFlash2 draft model we're using was trained on a lot of code, so if you use it in a coding agent you'll probably notice it run a lot fas…”

   proof ↗⚑ flag

   Baseten✓fullclaimed7/10

   Baseten ships the official open-source Truss SDK/CLI for packaging and deploying models (github.com/basetenlabs/truss) and also exposes OpenAI-compatible and Anthropic Messages-compatible endpoints so developers can use those official client SDKs (and LangChain) directly against Baseten (baseten-docs-21, baseten-docs-33, baseten-gh-1/2/5/6). This gives AI-native users multiple first-party and compatible SDK paths for building agentic workflows. Missing for 10: a dedicated first-party 'Baseten Python client' distinct from Truss, and independent/hands-on corroboration of SDK reliability beyond docs and repo README.

   • [github] “Write once, run anywhere: Package model code, weights, and dependencies with a model server that behaves the same in development and product…”
   • [github] “You write a `config.yaml` that specifies the model, the hardware, and the engine, then `uvx truss push` builds a TensorRT-optimized containe…”
   • [github] “Truss lets you serve models with the Baseten Inference Stack as well as deploy models from any open-source framework: vLLM, SGLang, TensorRT…”
   • [github] “Deploying a model to Baseten via Truss turns a Hugging Face model into a production-ready API endpoint. You write a `config.yaml` that speci…”
   • [claimed-docs] “They support the OpenAI Chat Completions API and the Anthropic Messages API in beta, so you can use familiar client SDKs.”
   • [claimed-docs] “Because Baseten exposes an OpenAI-compatible endpoint, you can use LangChain's `ChatOpenAI` with `with_structured_output` by pointing `base_…”
   • [claimed-docs] “Call hosted models through an OpenAI-compatible API, deploy your own models on dedicated infrastructure”

   proof ↗⚑ flag

9. ai-native userSubscribe to events via webhooks

   weight 2 · round to Baseten
   Cerebras Inference—none0/10

   No evidence anywhere in the pack mentions webhooks, event subscriptions, or callback-based notifications; Cerebras Inference appears to be a synchronous/streaming inference API with no webhook mechanism documented.

   ⚑ flag

   Baseten~partialclaimed4/10

   Docs mention that async inference results can be delivered via webhook callback, showing Baseten supports webhook-based notification for job completion, but there is no dedicated webhook subscription system (event types, signing, retry, subscription management) described anywhere in the evidence. Missing for 10: a general event-subscription/webhook API or docs page (event catalog, signature verification, delivery guarantees), coverage beyond async-inference completion notifications, and any independent confirmation of webhook reliability.

   • [claimed-docs] “Async inference returns a request ID quickly and completes later through webhook or polling, which suits batch work, long documents”
   • [claimed-docs] “Async inference returns a request ID quickly and completes later through webhook or polling, which suits batch work, long documents, or any …”
   • [claimed-docs] “Async inference returns a request ID quickly and completes later through webhook or polling”

   proof ↗⚑ flag

Agentic features

1. ai-native userSet up automations that run autonomously in the background

   weight 2 · round to Cerebras Inference
   Cerebras Inference~partialclaimed3/10

   Cerebras is an inference API/hardware provider, not an agent-orchestration platform, but its Batch API lets requests be processed asynchronously in the background without immediate results, which is a thin building block for autonomous background automations. There is no evidence of scheduling, triggers, workflow orchestration, or persistent autonomous agents — missing for 10: scheduled/triggered automation framework, agent orchestration/state management, independent evidence of autonomous background runs beyond simple async batch calls.

   • [claimed-docs] “The Batch API lets you process groups of requests asynchronously, making it perfect for workloads where you don't need immediate results”

   proof ↗⚑ flag

   Baseten—none0/10

   Baseten's evidence covers async inference (deferred single requests via webhook/polling), model deployment, and training, but there is no evidence of scheduling, triggers, or autonomous multi-step automations running in the background — async inference is single-request deferral, not an automation/workflow engine.

   ⚑ flag

2. ai-native userDelegate tasks to a built-in AI assistant inside the product

   weight 3 · round drawn
   Cerebras Inference—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

   Baseten—none0/10

   Baseten's evidence shows MCP servers and a Baseten 'skill' that let external coding agents (Claude Code, Codex, etc.) manage a Baseten workspace — this is the reverse of a built-in in-product assistant; nothing in the evidence describes a first-party AI assistant living inside the Baseten UI/dashboard that a user can delegate platform tasks to.

   • [claimed-docs] “Install the Baseten skill and MCP servers so your coding agent can manage your Baseten workspace and search these docs.”
   • [claimed-docs] “Connect Claude Code, Codex CLI, or Pi with Baseten Switch.”
   • [claimed-docs] “Use [Baseten Switch](/reference/cli/baseten-switch) to route requests from Claude Code or Codex CLI to Model APIs.”
   • [probe] “official MCP server documented at https://docs.baseten.co/agent-setup”
   • [probe] “PROBE mcp-endpoint (2026-09-04): POST initialize to https://docs.baseten.co/mcp answered HTTP 200 with a JSON-RPC/MCP response (event: messa…”

   proof ↗⚑ flag

3. ai-native userOperate the product with natural-language commands

   weight 2 · round to Baseten
   Cerebras Inference—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

   Baseten~partialprobed6/10

   Baseten ships an official skill and MCP server so a coding agent can 'manage your Baseten workspace and search these docs' via natural language, and a probe confirms a live, reachable MCP endpoint responding to JSON-RPC calls — this is genuine natural-language operability of the product through an agent intermediary. However the docs give only a high-level description of what workspace actions are exposed, with no detailed command catalog or independent hands-on verification of breadth/reliability. Missing for 10: a documented list of specific natural-language-operable actions (deploy, scale, monitor, etc.), and independent/hands-on evidence confirming the MCP tools work reliably end-to-end.

   • [claimed-docs] “Install the Baseten skill and MCP servers so your coding agent can manage your Baseten workspace and search these docs.”
   • [probe] “official MCP server documented at https://docs.baseten.co/agent-setup”
   • [probe] “PROBE mcp-endpoint (2026-09-04): POST initialize to https://docs.baseten.co/mcp answered HTTP 200 with a JSON-RPC/MCP response (event: messa…”

   proof ↗⚑ flag

Api quality

1. ai-native userExplore an interactive API reference with runnable examples

   weight 2 · round to Cerebras Inference
   Cerebras Inference~partialprobed5/10

   Cerebras docs offer a quickstart with code snippets and a no-key Cloud Console playground for testing prompts, plus rich per-capability doc pages (streaming, tool use, structured outputs) with example code. However, there's no evidence of a true interactive API reference (e.g., Swagger/OpenAPI 'try it' explorer) — a probe explicitly found no openapi.json/swagger spec at expected paths, and no citation shows runnable code execution directly embedded in the reference docs. missing for 10: an OpenAPI/Swagger-style interactive reference, confirmation that code examples in docs are directly runnable/editable in-browser, independent user confirmation of using such a feature.

   • [claimed-docs] “Make your first Cerebras API call in just minutes.”
   • [claimed-docs] “Use the playground in the Cloud Console — no key or install needed.”
   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://inference-docs.cerebras.ai/openapi.json, https://inference-docs.cerebras.ai/swagger.json, ht…”
   • [probe] “PROBE llms.txt: HTTP 200 at https://inference-docs.cerebras.ai/llms.txt # Cerebras Inference - [Quickstart](https://inference-docs.cerebras…”

   proof ↗⚑ flag

   Baseten—none0/10

   Evidence shows extensive prose documentation (structured outputs, function calling, streaming, pricing) but no interactive API reference or runnable-example playground; a direct probe for an OpenAPI/Swagger spec (which typically powers interactive references) returned 404 on all candidate paths.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.baseten.co/openapi.json, https://docs.baseten.co/swagger.json, https://docs.baseten.co/…”
   • [claimed-docs] “Structured outputs let you generate text that conforms to specific JSON schemas, providing reliable data extraction and controlled text gene…”
   • [claimed-docs] “Function calling* (also called *tool calling*) lets a model choose a tool and produce its arguments from a user request.”
   • [claimed-docs] “Return model output token by token as it is generated.”

   proof ↗⚑ flag

2. ai-native userDownload a machine-readable API spec (OpenAPI or equivalent)

   weight 2 · round drawn
   Cerebras Inference—none0/10

   Cerebras is OpenAI-API-compatible and documents endpoints extensively, but a direct probe for machine-readable spec files (openapi.json, swagger.json, etc.) returned 404 on all candidate paths, and no evidence pack item links to a downloadable OpenAPI/Swagger spec.

   • [probe] “PROBE openapi: all candidate paths 404 (https://inference-docs.cerebras.ai/openapi.json, https://inference-docs.cerebras.ai/swagger.json, ht…”

   proof ↗⚑ flag

   Baseten—none0/10

   A direct probe for a machine-readable API spec (openapi.json, swagger.json, and related paths) returned 404 on all candidates, and no docs page claims to publish an OpenAPI/Swagger spec — only that the API is OpenAI/Anthropic-compatible in shape, which is not the same as Baseten publishing its own downloadable spec.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.baseten.co/openapi.json, https://docs.baseten.co/swagger.json, https://docs.baseten.co/…”
   • [claimed-docs] “They support the OpenAI Chat Completions API and the Anthropic Messages API in beta, so you can use familiar client SDKs.”
   • [claimed-docs] “Call hosted models through an OpenAI-compatible API, deploy your own models on dedicated infrastructure”

   proof ↗⚑ flag

3. ai-native userTest against a sandbox environment without touching production data

   weight 1 · round drawn
   Cerebras Inference~partialclaimed4/10

   Cerebras offers a no-key Cloud Console playground and $5 free credits to 'prototype prompts, agents, and real-time apps before you spend a dollar,' which lets a user experiment without hitting a paid/production billing tier, but there is no dedicated 'sandbox' API mode, test keys, or explicit separation from production data/environment documented. missing for 10: an explicit sandbox/test-mode endpoint or key type, documentation guaranteeing isolation from production data, and independent confirmation that free-tier usage never touches the same infra as production workloads.

   • [claimed-docs] “Use the playground in the Cloud Console — no key or install needed.”
   • [claimed-docs] “Get started with $5 in free credits after making an account”
   • [claimed-docs] “Get started with $5 in free credit after creating an account. Prototype prompts, agents, and real-time apps before you spend a dollar.”

   proof ↗⚑ flag

   Baseten~partialclaimed4/10

   Docs mention that deployments 'support stable environments for development, staging, and production' (baseten-docs-20) and that Truss models 'behave the same in development and production' (baseten-gh-1), implying separate non-prod environments exist. However there is no detailed documentation of how sandbox/staging is isolated from production data, no guidance on seeding test data, and no independent confirmation of this workflow. Missing for 10: explicit sandbox/staging setup guide, data-isolation guarantees, and hands-on/community corroboration of safe non-prod testing.

   • [claimed-docs] “They also support stable environments for development, staging, and production.”
   • [github] “Write once, run anywhere: Package model code, weights, and dependencies with a model server that behaves the same in development and product…”

   proof ↗⚑ flag

4. ai-native userRely on versioned APIs with a documented deprecation policy

   weight 2 · round to Baseten
   Cerebras Inference—none0/10

   No evidence of API versioning scheme or a documented deprecation policy; docs cover OpenAI-compatible endpoints, streaming, tool use, etc., but nothing on version lifecycle or deprecation timelines. The openapi.json probe even returned 404s, and no changelog or deprecation notice pages are cited.

   • [probe] “PROBE openapi: all candidate paths 404 (https://inference-docs.cerebras.ai/openapi.json, https://inference-docs.cerebras.ai/swagger.json, ht…”
   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.”

   proof ↗⚑ flag

   Baseten~partialprobed3/10

   Baseten documents a deprecation policy for Model APIs, instructing users to migrate to dedicated deployments when weights are deprecated (baseten-docs-12), showing some commitment to managing API lifecycle. However, there is no evidence of explicit API versioning (e.g., version numbers, changelogs) and probes for an OpenAPI/swagger spec returned 404s, suggesting no formal versioned API contract is published. Missing for 10: explicit API version scheme, published changelog, machine-readable API spec, and independent confirmation of deprecation timelines.

   • [claimed-docs] “Migrate to a dedicated deployment with the deprecated model weights. Contact us for assistance.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.baseten.co/openapi.json, https://docs.baseten.co/swagger.json, https://docs.baseten.co/…”

   proof ↗⚑ flag

1. ai-native userPerform bulk operations across many items at once

   weight 2 · round to Cerebras Inference
   Cerebras Inference✓fullclaimed7/10

   Cerebras offers a documented Batch API for processing groups of requests asynchronously, directly supporting bulk operations across many items, and rate-limit/caching docs describe handling high-volume token throughput. missing for 10: no hands-on/community evidence validating batch API at scale, and no details on batch size limits or job management UX.

   • [claimed-docs] “The Batch API lets you process groups of requests asynchronously, making it perfect for workloads where you don't need immediate results”
   • [claimed-docs] “Cached tokens don't count toward your uncached TPM limit, so a higher cache hit rate lets you process far more total tokens within the same …”
   • [claimed-docs] “a higher cache hit rate lets you process far more total tokens within the same uncached limit”
   • [claimed-docs] “Improving your cache hit rate lets the same uncached limit serve significantly more total tokens”

   proof ↗⚑ flag

   Baseten~partialclaimed5/10

   Baseten's async inference is explicitly described as suited to 'batch work, long documents' where a request ID is returned and completion happens later via webhook or polling, which supports processing many items without holding open connections. However, there is no documented batch/bulk API endpoint for submitting many items in a single call, nor bulk management operations (e.g., bulk model/deployment updates) across the platform. Missing for 10: explicit multi-item batch submission API, bulk resource management (models/deployments/keys) in one operation, and independent evidence of throughput at scale for bulk workloads.

   • [claimed-docs] “Async inference returns a request ID quickly and completes later through webhook or polling, which suits batch work, long documents”
   • [claimed-docs] “Async inference returns a request ID quickly and completes later through webhook or polling, which suits batch work, long documents, or any …”
   • [claimed-docs] “Async inference returns a request ID quickly and completes later through webhook or polling”

   proof ↗⚑ flag

Batch

1. ml-engineerSubmit asynchronous batch inference jobs at a documented discount versus real-time pricing

   weight 2 · round drawn
   Cerebras Inference~partialclaimed4/10

   Cerebras documents a Batch API for asynchronous, non-immediate processing of grouped requests (cerebras-docs-8), satisfying the async batch-submission part of the story. However, no evidence pack item documents a specific discounted price for batch jobs versus real-time pricing — the pricing pages only mention free credits, $10 self-serve tier, and rate-limit multipliers (cerebras-docs-13, cerebras-docs-14, cerebras-docs-16), not a batch discount. missing for 10: documented batch discount percentage or rate card, independent confirmation of batch pricing savings.

   • [claimed-docs] “The Batch API lets you process groups of requests asynchronously, making it perfect for workloads where you don't need immediate results”
   • [claimed-docs] “Get started with $5 in free credits after making an account”
   • [claimed-docs] “Self-serve payment starting at just $10 * 10x higher rate limits than free tier * Higher priority processing”
   • [claimed-docs] “Get access to Cerebras Inference through our partner APIs”

   proof ↗⚑ flag

   Baseten~partialclaimed4/10

   Baseten documents asynchronous inference (request ID returned quickly, completion via webhook/polling) explicitly for batch work and long documents, so the submission mechanism is real. However, the only documented pricing discount is for KV-cached input tokens, not for async/batch requests specifically — there is no evidence of a documented discount rate for async vs real-time inference pricing. missing for 10: a documented async/batch discount rate or pricing tier distinct from real-time pricing, first-party pricing page showing the discount percentage.

   • [claimed-docs] “Async inference returns a request ID quickly and completes later through webhook or polling, which suits batch work, long documents”
   • [claimed-docs] “Async inference returns a request ID quickly and completes later through webhook or polling, which suits batch work, long documents, or any …”
   • [claimed-docs] “Async inference returns a request ID quickly and completes later through webhook or polling”
   • [claimed-docs] “Cached input tokens are prompt tokens served from the KV cache at a discounted rate. Caching is automatic and requires no request flags.”
   • [claimed-docs] “Model APIs bill by token and enforce request and token rate limits. You can also set a workspace budget and query usage by API key or model.”

   proof ↗⚑ flag

Dedicated

1. ml-engineerDeploy a model on dedicated GPU capacity with autoscaling so my traffic is isolated from the shared serverless pool

   weight 2 · round drawn
   Cerebras Inference~partialclaimed5/10

   Cerebras documents dedicated endpoints as private, reserved capacity isolated from the shared serverless pool, including support for custom fine-tuned models — directly matching the isolation requirement. However, there is no evidence of autoscaling on dedicated capacity; docs describe reserved/provisioned instances without any scaling mechanism mentioned. Missing for 10: explicit autoscaling capability, capacity/quota controls, and independent confirmation of dedicated-endpoint behavior in production.

   • [claimed-docs] “A dedicated endpoint is a private, provisioned instance of the Cerebras Inference service reserved exclusively for your organization.”
   • [claimed-docs] “Your endpoint runs on reserved capacity that is not shared with other customers, so your performance is never impacted by other workloads.”
   • [claimed-docs] “Deploy your custom fine-tuned models alongside standard model variants.”

   proof ↗⚑ flag

   Baseten~partialclaimed5/10

   Baseten clearly supports deploying models on dedicated GPUs/dedicated infrastructure (baseten-docs-2, baseten-docs-17, baseten-docs-30) with production-stable environments (baseten-docs-20) and resilient multi-cluster deployment (baseten-docs-15), which is distinct from the shared Model APIs pool (baseten-docs-1). However, no evidence explicitly describes autoscaling behavior for these dedicated deployments (scale-to-zero, concurrency-based scaling, etc.), so the isolation-plus-autoscaling combination is only partially substantiated. Missing for 10: explicit autoscaling documentation for dedicated deployments, and independent/hands-on confirmation of traffic isolation from the shared pool.

   • [claimed-docs] “Deploy an open-source, fine-tuned, or custom model on dedicated GPUs.”
   • [claimed-docs] “Deploy an open-source, fine-tuned, or custom model on dedicated infrastructure.”
   • [claimed-docs] “Call hosted models through an OpenAI-compatible API, deploy your own models on dedicated infrastructure”
   • [claimed-docs] “They also support stable environments for development, staging, and production.”
   • [claimed-docs] “Deployments run active-active across clusters and clouds. If a region or provider loses capacity, MCM reroutes and reprovisions workloads.”
   • [claimed-docs] “Use Model APIs to call supported language models without deploying them.”

   proof ↗⚑ flag

Fine tune

1. ml-engineerFine-tune a supported base model on my own data and serve the result on the same platform

   weight 2 · round to Baseten
   Cerebras Inference~partialclaimed4/10

   Cerebras dedicated endpoints explicitly support deploying custom fine-tuned models alongside standard variants on reserved capacity (cerebras-docs-9, cerebras-docs-10, cerebras-docs-24), covering the 'serve' half of the story. However, there is no evidence that Cerebras itself provides a fine-tuning service/API — the docs imply fine-tuning happens elsewhere and the resulting model is uploaded/deployed to a dedicated endpoint, not that the platform trains it. missing for 10: an actual fine-tuning API/pipeline on Cerebras, documentation of supported base models for tuning, and any hands-on confirmation of the full fine-tune-then-serve workflow.

   • [claimed-docs] “A dedicated endpoint is a private, provisioned instance of the Cerebras Inference service reserved exclusively for your organization.”
   • [claimed-docs] “Deploy your custom fine-tuned models alongside standard model variants.”
   • [claimed-docs] “Your endpoint runs on reserved capacity that is not shared with other customers, so your performance is never impacted by other workloads.”

   proof ↗⚑ flag

   Baseten✓fullclaimed8/10

   Baseten documents Training Jobs/Loops for fine-tuning supported base models (Axolotl/TRL/custom loops), automatic checkpoint syncing, and one-CLI-command deployment of any synced checkpoint to production inference on the same platform, matching the story end-to-end. missing for 10: independent/hands-on third-party confirmation of the fine-tune-to-serve workflow (community evidence only covers unrelated Chains/embedding topics) and explicit list of which base models are 'supported' for fine-tuning.

   • [claimed-docs] “Fine-tune with Loops or run your own training code with Training Jobs.”
   • [claimed-docs] “Baseten trains models on managed GPUs and deploys the resulting checkpoints to production inference on the same platform.”
   • [claimed-docs] “Baseten provisions GPUs through MCM, runs your training container, and syncs checkpoints to storage as the job progresses.”
   • [claimed-docs] “Your Axolotl config, TRL script, or custom loop runs unchanged in a container. Baseten provisions the GPUs, syncs checkpoints as your job sa…”
   • [claimed-docs] “Deploy any synced checkpoint with one CLI command”
   • [claimed-docs] “Fine-tuning or running RL on a supported base model: Loops provisions a dedicated trainer and paired sampler, and each training step is an A…”
   • [claimed-docs] “Loops provisions a dedicated trainer and paired sampler, and each training step is an API call from a Python loop you write.”

   proof ↗⚑ flag

2. ml-engineerUpload and serve my own custom model weights or LoRA adapters

   weight 2 · round to Baseten
   Cerebras Inference~partialclaimed4/10

   Cerebras' dedicated endpoint docs state customers can 'deploy your custom fine-tuned models alongside standard model variants' on reserved capacity, which implies some path to serve custom fine-tuned weights — but this is only mentioned for the enterprise 'dedicated endpoint' tier, not the standard self-serve API, and there is no mention of LoRA adapter support, upload workflow, or self-serve model registration. Missing for 10: LoRA adapter upload/serving, self-serve (non-dedicated) custom weight upload process, independent/hands-on confirmation that custom fine-tuned models can actually be deployed.

   • [claimed-docs] “A dedicated endpoint is a private, provisioned instance of the Cerebras Inference service reserved exclusively for your organization.”
   • [claimed-docs] “Deploy your custom fine-tuned models alongside standard model variants.”
   • [claimed-docs] “Your endpoint runs on reserved capacity that is not shared with other customers, so your performance is never impacted by other workloads.”

   proof ↗⚑ flag

   Baseten✓fullclaimed8/10

   Baseten documents deploying custom/fine-tuned/open-source models via Truss (config.yaml + weights, supports transformers/diffusers/vLLM/etc.), and its Training product syncs LoRA/checkpoint artifacts that can be deployed as production endpoints with one CLI command, directly covering custom weights and LoRA adapter serving. Missing for 10: explicit LoRA-adapter-specific hot-swapping/multi-adapter serving docs and independent hands-on confirmation beyond vendor docs/GitHub README.

   • [claimed-docs] “Deploy an open-source, fine-tuned, or custom model on dedicated GPUs.”
   • [claimed-docs] “Deploy an open-source, fine-tuned, or custom model on dedicated infrastructure.”
   • [github] “Write once, run anywhere: Package model code, weights, and dependencies with a model server that behaves the same in development and product…”
   • [github] “You write a `config.yaml` that specifies the model, the hardware, and the engine, then `uvx truss push` builds a TensorRT-optimized containe…”
   • [github] “Support for all Python frameworks: From `transformers` and `diffusers` to PyTorch and TensorFlow to vLLM, SGLang, and TensorRT-LLM, Truss su…”
   • [claimed-docs] “Your Axolotl config, TRL script, or custom loop runs unchanged in a container. Baseten provisions the GPUs, syncs checkpoints as your job sa…”
   • [claimed-docs] “Deploy any synced checkpoint with one CLI command”
   • [claimed-docs] “Baseten trains models on managed GPUs and deploys the resulting checkpoints to production inference on the same platform.”

   proof ↗⚑ flag

Catalog

1. ml-engineerGet newly released open-weight models on the platform quickly after their public release

   weight 2 · round to Cerebras Inference
   Cerebras Inference~partialcommunity3/10

   Community comments suggest Cerebras adds new open-weight models fairly often (e.g. hosting Qwen 3.8 27B, DFlash2 draft model) and docs show a model catalog exists, but there's no concrete evidence of turnaround time from a model's public release to availability on Cerebras, and one comment notes a newly released model (Qwen 3.8) wasn't yet available via a partner (OpenRouter), implying some lag. missing for 10: documented release-to-availability timelines, first-party announcements tying model launches to Cerebras availability, and independent confirmation of consistent fast onboarding of new open-weight models.

   • [community] “I used their Coding Plan for a few months. It is genuinely difficult to keep up with the models. The output is so fast. Qwen 3.8 27B is like…”
   • [community] “Noticed they are present in OpenRouter, but Qwen 3.8 is not there yet... the context size they allow for Qwen is just 128k. Still interestin…”
   • [community] “The DFlash2 draft model we're using was trained on a lot of code, so if you use it in a coding agent you'll probably notice it run a lot fas…”
   • [claimed-docs] “Find the right open-source model for your workload on Cerebras, including alternatives for Claude, GPT, and Gemini.”
   • [claimed-docs] “Browse all models available on Cerebras public endpoints.”

   proof ↗⚑ flag

   Baseten—none0/10

   The evidence describes Baseten's general capabilities (Model APIs, Truss deployment, training) but contains no claims or evidence about how quickly newly released open-weight models are added to the hosted Model APIs catalog after their public release. Missing for 10: any documentation or track record on model-catalog update cadence, day-1 support claims, or examples of specific new model releases being onboarded quickly.

   ⚑ flag

2. developerRely on a documented deprecation policy with advance notice before a hosted model is removed

   weight 1 · round to Baseten
   Cerebras Inference—none0/10

   No evidence pack item describes a deprecation policy, model retirement timeline, or advance-notice process for hosted models; documentation covers model selection but not lifecycle/deprecation.

   ⚑ flag

   Baseten~partialclaimed4/10

   Baseten has a dedicated deprecation docs page instructing users to migrate to a dedicated deployment with deprecated model weights, showing some documented deprecation handling exists, but the evidence pack shows no explicit advance-notice period, timeline, or notification process. Missing for 10: concrete advance-notice window/SLA, notification channel details, and independent confirmation that notice is actually given before removal.

   • [claimed-docs] “Migrate to a dedicated deployment with the deprecated model weights. Contact us for assistance.”

   proof ↗⚑ flag

3. ai-native userHave an agent enumerate the live model catalog programmatically via a documented GET /v1/models-style endpoint

   weight 2 · round to Baseten
   Cerebras Inference~partialprobed7/10

   A live probe confirms `GET https://api.cerebras.ai/v1/models` is a real, JSON-speaking, OpenAI-style endpoint (403 unauthenticated, not 404), and docs repeatedly assert OpenAI API compatibility and a public models catalog page (cerebras-docs-3, -4, -21). However, no first-party doc page explicitly documents the /v1/models endpoint schema/response, nor is there an OpenAPI spec (all openapi.json paths 404). missing for 10: explicit documented endpoint reference/response schema for /v1/models, no OpenAPI spec confirmation, no independent hands-on report of enumerating the catalog via this endpoint.

   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.cerebras.ai/v1/models without a key returned HTTP 403 ({"detail":"Not authenticated"}) —…”
   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.”
   • [claimed-docs] “Browse all models available on Cerebras public endpoints.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://inference-docs.cerebras.ai/openapi.json, https://inference-docs.cerebras.ai/swagger.json, ht…”

   proof ↗⚑ flag

   Baseten✓fullprobed8/10

   A live probe confirms the OpenAI-style GET /v1/models endpoint exists at https://inference.baseten.co/v1/models and returns proper JSON auth-error responses when unauthenticated, consistent with Baseten's documented OpenAI-compatible Chat Completions API (baseten-docs-21, baseten-docs-30). This lets an agent enumerate the model catalog programmatically once authenticated. Missing for 10: explicit first-party documentation of the /v1/models endpoint's response schema/fields and an authenticated hands-on example showing the returned catalog contents.

   • [probe] “PROBE models-endpoint (2026-09-04): GET https://inference.baseten.co/v1/models without a key returned HTTP 401 (No Authorization header prov…”
   • [claimed-docs] “They support the OpenAI Chat Completions API and the Anthropic Messages API in beta, so you can use familiar client SDKs.”
   • [claimed-docs] “Call hosted models through an OpenAI-compatible API, deploy your own models on dedicated infrastructure”

   proof ↗⚑ flag

4. developerChoose among a broad catalog of current open-weight model families (Llama, Qwen, DeepSeek, GPT-OSS and peers) on shared serverless endpoints

   weight 3 · round to Cerebras Inference
   Cerebras Inference~partialcommunity7/10

   Cerebras docs confirm a public model catalog with guidance to pick the right model and a models/overview page for browsing all available public endpoints, and community reports confirm live usage of Llama 3.1 70B and Qwen models on shared endpoints. However, no evidence explicitly names DeepSeek or GPT-OSS in the catalog, and one community note flags a context-size limitation (128k) for at least one hosted model, suggesting the catalog's breadth/parity with 'peers' isn't fully documented. missing for 10: explicit confirmation of DeepSeek and GPT-OSS availability, and a full enumerated model list showing parity across all cited families.

   • [claimed-docs] “Use this guide to find the right model for your use case on Cerebras.”
   • [claimed-docs] “Find the right open-source model for your workload on Cerebras, including alternatives for Claude, GPT, and Gemini.”
   • [claimed-docs] “Browse all models available on Cerebras public endpoints.”
   • [community] “I used their Coding Plan for a few months. It is genuinely difficult to keep up with the models. The output is so fast. Qwen 3.8 27B is like…”
   • [community] “Noticed they are present in OpenRouter, but Qwen 3.8 is not there yet... the context size they allow for Qwen is just 128k. Still interestin…”
   • [community] “This is astonishingly fast. I'm struggling to get over 100 tok/s on my own Llama 3.1 70b implementation on an 8x H100 cluster.”

   proof ↗⚑ flag

   Baseten~partialprobed4/10

   Baseten's docs confirm a serverless 'Model APIs' offering for calling supported language models without deploying them, with OpenAI/Anthropic-compatible endpoints, implying a hosted catalog exists — but no evidence enumerates specific open-weight families like Llama, Qwen, DeepSeek, or GPT-OSS, and the live models endpoint requires an API key to inspect. missing for 10: explicit list of supported model families, breadth/currency of catalog, independent confirmation of specific models available on shared serverless endpoints.

   • [claimed-docs] “Use Model APIs to call supported language models without deploying them.”
   • [claimed-docs] “They support the OpenAI Chat Completions API and the Anthropic Messages API in beta, so you can use familiar client SDKs.”
   • [claimed-docs] “Call hosted models through an OpenAI-compatible API, deploy your own models on dedicated infrastructure”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://inference.baseten.co/v1/models without a key returned HTTP 401 (No Authorization header prov…”

   proof ↗⚑ flag

Modalities

1. developerGenerate embeddings (and rerank results) for retrieval pipelines without a second vendor

   weight 1 · round to Baseten
   Cerebras Inference—none0/10

   No evidence of embeddings or reranking models/endpoints anywhere in the docs, SDKs, or model catalog — Cerebras Inference documentation focuses solely on chat/completions, tool use, structured outputs, and streaming for LLMs. No mention of an embeddings API or reranking capability.

   ⚑ flag

   Baseten~partialcommunity5/10

   Baseten's general-purpose model deployment (Truss, custom model hosting, transformers/diffusers/vLLM support) can host embedding or reranking models on dedicated infrastructure, and a community report confirms real-world 'Baseten.co embedding workloads' being run on the platform. However, there is no first-party documentation of a dedicated embeddings or rerank API/product page, no mention of specific embedding model catalog entries, or built-in reranking features analogous to competitors' turnkey embedding endpoints. missing for 10: dedicated embeddings/rerank API docs, named embedding/rerank models in the catalog, first-party guidance on building RAG retrieval pipelines end-to-end.

   • [community] “Developer notes that with Baseten.co embedding workloads, the client (not server) becomes the bottleneck due to Python's GIL, prompting them…”
   • [claimed-docs] “Deploy an open-source, fine-tuned, or custom model on dedicated GPUs.”
   • [claimed-docs] “Deploy an open-source, fine-tuned, or custom model on dedicated infrastructure.”
   • [github] “Support for all Python frameworks: From `transformers` and `diffusers` to PyTorch and TensorFlow to vLLM, SGLang, and TensorRT-LLM, Truss su…”
   • [github] “Truss lets you serve models with the Baseten Inference Stack as well as deploy models from any open-source framework: vLLM, SGLang, TensorRT…”

   proof ↗⚑ flag

2. developerCall vision, audio, or image-generation models beyond text chat on the same platform

   weight 1 · round to Baseten
   Cerebras Inference~partialclaimed3/10

   Docs show only a narrow vision capability (base64 image_url input support for chat completions) but no evidence of dedicated vision, audio, or image-generation models being served on the platform, nor documentation of separate multimodal model endpoints. missing for 10: audio input/output model support, image-generation model support, explicit vision-model catalog entries, and any hands-on/community confirmation of using multimodal (non-text) capabilities.

   • [claimed-docs] “The standard OpenAI `image_url` content shape is supported. Supply the image as a base64 data URI”
   • [claimed-docs] “Browse all models available on Cerebras public endpoints.”
   • [claimed-docs] “Find the right open-source model for your workload on Cerebras, including alternatives for Claude, GPT, and Gemini.”

   proof ↗⚑ flag

   Baseten~partialclaimed5/10

   Baseten's Truss framework explicitly supports 'diffusers' (used for image-generation models) and any open-source framework, and the platform lets you deploy 'open-source, fine-tuned, or custom' models on dedicated GPUs, implying vision/audio/image models can be hosted alongside text. However, the hosted 'Model APIs' product is explicitly scoped to 'supported language models' (baseten-docs-1), and no evidence names specific vision, audio, or image-generation models or endpoints being offered as first-class hosted APIs — multimodal support relies on self-deploying via Truss rather than a turnkey catalog like text chat models get. missing for 10: explicit vision/audio/image-generation model catalog or API examples, first-party documentation naming specific multimodal models supported out-of-the-box.

   • [claimed-docs] “Use Model APIs to call supported language models without deploying them.”
   • [claimed-docs] “Deploy an open-source, fine-tuned, or custom model on dedicated infrastructure.”
   • [github] “Support for all Python frameworks: From `transformers` and `diffusers` to PyTorch and TensorFlow to vLLM, SGLang, and TensorRT-LLM, Truss su…”
   • [github] “Truss lets you serve models with the Baseten Inference Stack as well as deploy models from any open-source framework: vLLM, SGLang, TensorRT…”
   • [claimed-docs] “Use a Python model class or a custom Docker server when you need custom preprocessing, postprocessing, dependencies, or server behavior.”

   proof ↗⚑ flag

Compat

1. ai-native userPlug the provider into coding agents and agent frameworks through documented, first-party integration guides

   weight 2 · round to Baseten
   Cerebras Inference~partialcommunity6/10

   Cerebras publishes first-party OpenAI-compatibility docs and SDKs (cerebras-docs-3, cerebras-docs-4, cerebras-gh-1/2) that let any OpenAI-compatible coding agent or framework plug in by swapping API key/base URL, and community reports confirm 'official support' for tools like Cursor (cerebras-comm-12). However there is no dedicated first-party guide for specific agent frameworks (e.g., LangChain, Cursor, Claude Code) beyond generic OpenAI-compat instructions, and one report shows friction integrating with claude-code-router (422 wrong_api_format, cerebras-comm-11). Missing for 10: explicit named integration guides/tutorials for popular coding agents or agent frameworks, and confirmation that such integrations work smoothly end-to-end.

   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.”
   • [github] “This SDK has a mechanism that sends a few requests to `/v1/tcp_warming` upon construction to reduce the TTFT.”
   • [github] “This library provides convenient access to the Cerebras REST API from server-side TypeScript or JavaScript.”
   • [community] “I've been waiting on this for a LONG time. Integration with Cursor when Cerebras released their earlier models was patchy at best, even thro…”
   • [community] “It hits the request per minute limit instantly and then you wait a minute. (API Error: 422 ... wrong_api_format when integrating with claude…”

   proof ↗⚑ flag

   Baseten✓fullprobed9/10

   Baseten has dedicated first-party docs for connecting coding agents (Claude Code, Codex CLI, Pi) via Baseten Switch, plus a documented MCP server/skill for agent workspace management, and OpenAI/Anthropic-compatible endpoints enabling framework integrations like LangChain. This is corroborated by a live probe confirming the MCP endpoint responds correctly. Missing for 10: independent hands-on validation of the coding-agent connection flow beyond docs/probes.

   • [claimed-docs] “To point a coding agent at Model APIs, see [Coding agents](/inference/model-apis/coding-agents).”
   • [claimed-docs] “Install the Baseten skill and MCP servers so your coding agent can manage your Baseten workspace and search these docs.”
   • [claimed-docs] “Switch can also install Pi's direct Baseten provider, compare Baseten spend with estimated costs from Anthropic or OpenAI, and route request…”
   • [claimed-docs] “Connect Claude Code, Codex CLI, or Pi with Baseten Switch.”
   • [claimed-docs] “Use [Baseten Switch](/reference/cli/baseten-switch) to route requests from Claude Code or Codex CLI to Model APIs.”
   • [claimed-docs] “Because Baseten exposes an OpenAI-compatible endpoint, you can use LangChain's `ChatOpenAI` with `with_structured_output` by pointing `base_…”
   • [probe] “official MCP server documented at https://docs.baseten.co/agent-setup”
   • [probe] “PROBE mcp-endpoint (2026-09-04): POST initialize to https://docs.baseten.co/mcp answered HTTP 200 with a JSON-RPC/MCP response (event: messa…”

   proof ↗⚑ flag

2. ai-native userHave an agent switch to or away from this provider mid-workflow because it speaks the standard chat-completions API without provider-specific code changes

   weight 3 · round to Baseten
   Cerebras Inference~partialcommunity7/10

   Cerebras explicitly documents OpenAI-compatible chat completions requiring only base URL/API key/model swap ('two code changes'), and supports streaming, tool calling, image_url content, and structured outputs matching OpenAI's API shape (cerebras-docs-3, cerebras-docs-4, cerebras-docs-15, cerebras-docs-23). However, real-world integration reports show friction: a user hit a 'wrong_api_format' 422 error integrating with claude-code-router (cerebras-comm-11), and Cursor integration was described as 'patchy' before official support was added (cerebras-comm-12), suggesting the compatibility layer isn't always frictionless in practice. Missing for 10: independent verification of drop-in compatibility across multiple agent frameworks without errors, and no OpenAPI spec is publicly served (cerebras-probe-3) to confirm exact schema parity.

   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.”
   • [claimed-docs] “The standard OpenAI `image_url` content shape is supported. Supply the image as a base64 data URI”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.​”
   • [community] “It hits the request per minute limit instantly and then you wait a minute. (API Error: 422 ... wrong_api_format when integrating with claude…”
   • [community] “I've been waiting on this for a LONG time. Integration with Cursor when Cerebras released their earlier models was patchy at best, even thro…”

   proof ↗⚑ flag

   Baseten✓fullclaimed8/10

   Baseten documents OpenAI Chat Completions API compatibility for Model APIs, explicitly enabling drop-in use with familiar client SDKs (e.g., LangChain's ChatOpenAI via base_url swap), and even documents a Switch tool that can route requests between Baseten and other providers like Anthropic/OpenAI mid-workflow. This directly supports agent-driven provider switching without provider-specific code changes. Missing for 10: independent/hands-on confirmation of seamless mid-workflow switching beyond vendor docs, and no explicit statement of full drop-in parity with OpenAI's complete API surface.

   • [claimed-docs] “They support the OpenAI Chat Completions API and the Anthropic Messages API in beta, so you can use familiar client SDKs.”
   • [claimed-docs] “Call hosted models through an OpenAI-compatible API, deploy your own models on dedicated infrastructure”
   • [claimed-docs] “Because Baseten exposes an OpenAI-compatible endpoint, you can use LangChain's `ChatOpenAI` with `with_structured_output` by pointing `base_…”
   • [claimed-docs] “Switch can also install Pi's direct Baseten provider, compare Baseten spend with estimated costs from Anthropic or OpenAI, and route request…”
   • [claimed-docs] “Use [Baseten Switch](/reference/cli/baseten-switch) to route requests from Claude Code or Codex CLI to Model APIs.”

   proof ↗⚑ flag

3. developerPoint an existing OpenAI SDK client at the provider by changing only the base URL and API key

   weight 3 · round drawn
   Cerebras Inference✓fullprobed9/10

   Official docs explicitly state existing OpenAI SDK apps can switch to Cerebras by changing only the API key, base URL, and model ID, and marketing reiterates 'just two code changes'; a live probe confirms the API serves an OpenAI-style /v1/models endpoint. Community reports (e.g. Cursor/OpenRouter integrations) corroborate real-world drop-in usage. Missing for 10: independent hands-on confirmation of a literal SDK base_url swap with zero other code changes.

   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.​”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.cerebras.ai/v1/models without a key returned HTTP 403 ({"detail":"Not authenticated"}) —…”
   • [community] “I've been waiting on this for a LONG time. Integration with Cursor when Cerebras released their earlier models was patchy at best, even thro…”

   proof ↗⚑ flag

   Baseten✓fullprobed9/10

   Baseten explicitly documents an OpenAI Chat Completions-compatible endpoint for its Model APIs, and shows real-world usage where only base_url and API key need to change (e.g. LangChain's ChatOpenAI pointed at Baseten). A live probe confirms an OpenAI-style /v1/models endpoint responding at inference.baseten.co. Missing for 10: no independent third-party hands-on report explicitly confirming a drop-in swap with the official OpenAI SDK client itself (only LangChain example and docs claims).

   • [claimed-docs] “They support the OpenAI Chat Completions API and the Anthropic Messages API in beta, so you can use familiar client SDKs.”
   • [claimed-docs] “Call hosted models through an OpenAI-compatible API, deploy your own models on dedicated infrastructure”
   • [claimed-docs] “Because Baseten exposes an OpenAI-compatible endpoint, you can use LangChain's `ChatOpenAI` with `with_structured_output` by pointing `base_…”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://inference.baseten.co/v1/models without a key returned HTTP 401 (No Authorization header prov…”

   proof ↗⚑ flag

1. ai-native userDo everything through the API that I can do in the UI

   weight 2 · round drawn
   Cerebras Inference~partialcommunity6/10

   Cerebras's API surface is extensive and well-documented (chat completions, streaming, tool calling, structured outputs, batch API, dedicated endpoints, model catalog) and the console playground is explicitly described as just a convenience UI on top of the same API, suggesting strong API/UI parity. However there's no explicit statement or independent verification that every console feature (e.g., dedicated-endpoint provisioning, billing/account management, fine-tuned model deployment) is fully API-driven rather than requiring console/sales interaction, and community reports mention waitlists and manual onboarding steps outside the API. Missing for 10: explicit parity documentation, evidence that account/billing/deployment actions are API-accessible, and independent confirmation of full UI-to-API feature parity.

   • [claimed-docs] “Use the playground in the Cloud Console — no key or install needed.”
   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.”
   • [claimed-docs] “A dedicated endpoint is a private, provisioned instance of the Cerebras Inference service reserved exclusively for your organization.”
   • [claimed-docs] “Deploy your custom fine-tuned models alongside standard model variants.”
   • [claimed-docs] “Your endpoint runs on reserved capacity that is not shared with other customers, so your performance is never impacted by other workloads.”
   • [community] “They have a waitlist for trying their API. You have to be a bit skeptical when a company makes claims but does not offer their services to b…”
   • [community] “Very interested in playing with their hardware and cloud. Also I wonder if it's possible to try cloud without contacting their sales.”

   proof ↗⚑ flag

   Baseten~partialprobed6/10

   Baseten's core workflows — deploying models (Truss/config.yaml/CLI), running inference (OpenAI/Anthropic-compatible endpoints, streaming, async, structured outputs, function calling), and training (Loops API calls, Training Jobs CLI deploy) — are all API/CLI-driven, and usage/budget can be queried by API key, suggesting strong API-UI parity for the platform's main functions. However, there is no explicit documentation asserting full UI/API parity, no discoverable OpenAPI/reference spec (probe found only 404s), and no evidence that workspace/account-management UI features (team, billing settings) are exposed via API. Missing for 10: an explicit parity statement or full API reference, confirmation of workspace/admin functions being API-accessible, and independent verification of parity beyond inference/training/deploy workflows.

   • [github] “You write a `config.yaml` that specifies the model, the hardware, and the engine, then `uvx truss push` builds a TensorRT-optimized containe…”
   • [claimed-docs] “Your Axolotl config, TRL script, or custom loop runs unchanged in a container. Baseten provisions the GPUs, syncs checkpoints as your job sa…”
   • [claimed-docs] “Deploy any synced checkpoint with one CLI command”
   • [claimed-docs] “Loops provisions a dedicated trainer and paired sampler, and each training step is an API call from a Python loop you write.”
   • [claimed-docs] “Async inference returns a request ID quickly and completes later through webhook or polling, which suits batch work, long documents”
   • [claimed-docs] “Structured outputs let you generate text that conforms to specific JSON schemas, providing reliable data extraction and controlled text gene…”
   • [claimed-docs] “Function calling* (also called *tool calling*) lets a model choose a tool and produce its arguments from a user request.”
   • [claimed-docs] “You can also set a workspace budget and query usage by API key or model.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.baseten.co/openapi.json, https://docs.baseten.co/swagger.json, https://docs.baseten.co/…”

   proof ↗⚑ flag

2. ai-native userExport all of my data in open formats and leave

   weight 3 · round to Baseten
   Cerebras Inference—none0/10

   No evidence describes any data-export mechanism (usage logs, fine-tuned model weights, account data) in open formats; the closest analog—OpenAI API compatibility—only covers code portability for switching inference providers, not actual data export/exit tooling. Missing for 10: any documented data export feature, format, or exit/offboarding process.

   ⚑ flag

   Baseten~partialclaimed4/10

   Baseten's tooling leans open: models are packaged via the open-source Truss format (config.yaml, no proprietary container lock-in), and training checkpoints are synced to external storage as the job runs, so a user's model weights and training artifacts aren't trapped in a proprietary format. However there is no explicit documentation of exporting workspace data, logs, usage/billing history, or deployment configs in bulk, and no dedicated 'account export' or data-portability feature. missing for 10: explicit full-account/data export tooling, documentation of exporting logs/usage/config metadata, and any user testimonial of migrating away with all data intact.

   • [github] “Write once, run anywhere: Package model code, weights, and dependencies with a model server that behaves the same in development and product…”
   • [github] “You write a `config.yaml` that specifies the model, the hardware, and the engine, then `uvx truss push` builds a TensorRT-optimized containe…”
   • [claimed-docs] “Your Axolotl config, TRL script, or custom loop runs unchanged in a container. Baseten provisions the GPUs, syncs checkpoints as your job sa…”
   • [claimed-docs] “Deploy any synced checkpoint with one CLI command”
   • [claimed-docs] “Baseten provisions GPUs through MCM, runs your training container, and syncs checkpoints to storage as the job progresses.”

   proof ↗⚑ flag

3. ai-native userRead the product's source under an open license

   weight 2 · round to Baseten
   Cerebras Inference—none0/10

   Cerebras Inference is a closed, hosted API service; the evidence pack shows only client SDKs (Node/Python) on GitHub, not the source of the inference service or model weights under an open license. There is no evidence the core product's source code is available for review.

   ⚑ flag

   Baseten~partialclaimed3/10

   Baseten's core platform (inference service, dashboard, orchestration/MCM) is proprietary SaaS with no evidence of an open license; only the Truss packaging library is open-source on GitHub, which is a peripheral client tool rather than the product itself. missing for 10: no open license for the actual Baseten platform/service source, no repository for the core inference or orchestration engine, only a tangential SDK is open.

   • [github] “Write once, run anywhere: Package model code, weights, and dependencies with a model server that behaves the same in development and product…”
   • [github] “You write a `config.yaml` that specifies the model, the hardware, and the engine, then `uvx truss push` builds a TensorRT-optimized containe…”
   • [github] “Fast developer loop: Iterate with live reload, skip Docker and Kubernetes configuration, and use a batteries-included serving environment.”
   • [github] “Support for all Python frameworks: From `transformers` and `diffusers` to PyTorch and TensorFlow to vLLM, SGLang, and TensorRT-LLM, Truss su…”
   • [github] “Truss lets you serve models with the Baseten Inference Stack as well as deploy models from any open-source framework: vLLM, SGLang, TensorRT…”
   • [github] “Deploying a model to Baseten via Truss turns a Hugging Face model into a production-ready API endpoint. You write a `config.yaml` that speci…”

   proof ↗⚑ flag

Limits

1. developerRead documented rate limits and how they scale across usage tiers before I hit them in production

   weight 2 · round drawn
   Cerebras Inference~partialcommunity6/10

   Cerebras has a dedicated rate-limits doc explaining TPM/RPM mechanics and how cache hit rate affects effective throughput (cerebras-docs-12/19/22), and the pricing page states tiers scale limits (e.g., 10x higher on the $10 self-serve tier vs free) (cerebras-docs-14/20/26). Community reports confirm rate limits are real and enforced in practice (cerebras-comm-11), corroborating the docs. However, the evidence never shows concrete numeric RPM/TPM values per tier or a full scaling table across all tiers (free, self-serve, dedicated), so a developer can't fully predict exact limits before hitting them. Missing for 10: explicit numeric rate-limit tables per tier, dedicated-endpoint tier limits, and independent confirmation that documented numbers match real-world enforcement.

   • [claimed-docs] “Cached tokens don't count toward your uncached TPM limit, so a higher cache hit rate lets you process far more total tokens within the same …”
   • [claimed-docs] “a higher cache hit rate lets you process far more total tokens within the same uncached limit”
   • [claimed-docs] “Improving your cache hit rate lets the same uncached limit serve significantly more total tokens”
   • [claimed-docs] “Self-serve payment starting at just $10 * 10x higher rate limits than free tier * Higher priority processing”
   • [claimed-docs] “Self-serve payment starting at just $10 ... 10x higher rate limits than free tier ... Higher priority processing”
   • [claimed-docs] “10x higher rate limits than free tier”
   • [community] “It hits the request per minute limit instantly and then you wait a minute. (API Error: 422 ... wrong_api_format when integrating with claude…”

   proof ↗⚑ flag

   Baseten~partialclaimed6/10

   Baseten has a dedicated pricing-and-limits doc covering request/token rate limits, rate-limit response headers, tier upgrades (Basic→Pro/Enterprise via email verification), workspace budgets, and usage monitoring by API key/model. However, it lacks a detailed table of exact numeric limits per tier and doesn't cover rate limits for dedicated-deployment (non-Model-APIs) inference. missing for 10: explicit numeric limit tables per tier, independent/community corroboration of documented limits, coverage of dedicated deployment rate limits (not just Model APIs).

   • [claimed-docs] “Model APIs bill by token and enforce request and token rate limits. You can also set a workspace budget and query usage by API key or model.”
   • [claimed-docs] “To monitor token and request consumption by API key or model, see Usage.”
   • [claimed-docs] “To raise a Basic account's limits, request email verification. You can also use that form to move to Pro or Enterprise.”
   • [claimed-docs] “x-ratelimit-remaining-requests: Reports how many requests remain before you reach the request rate limit.”
   • [claimed-docs] “You can also set a workspace budget and query usage by API key or model.”

   proof ↗⚑ flag

Pricing

1. founderSet spending caps or budget alerts so a runaway workload cannot generate an unbounded bill

   weight 1 · round to Baseten
   Cerebras Inference—none0/10

   No evidence of spending caps, budget alerts, or usage-limit controls in Cerebras docs; only rate-limit tiers and free credit amounts are mentioned, not billing caps or alerts a founder could set to bound spend.

   • [claimed-docs] “Cached tokens don't count toward your uncached TPM limit, so a higher cache hit rate lets you process far more total tokens within the same …”
   • [claimed-docs] “Get started with $5 in free credits after making an account”
   • [claimed-docs] “Self-serve payment starting at just $10 * 10x higher rate limits than free tier * Higher priority processing”

   proof ↗⚑ flag

   Baseten~partialclaimed5/10

   Baseten docs mention setting a workspace budget and monitoring usage by API key/model, plus rate limits (request/token) that cap throughput, which supports pricing-limits awareness (baseten-docs-10, -23, -37, -38). However, there's no evidence of automated spending caps that hard-stop billing or proactive budget alerts/notifications — the workspace budget appears to be a settable figure without documented enforcement or alerting mechanics. Missing for 10: explicit alerting/notification system, enforcement behavior when budget is exceeded, and independent confirmation that budgets actually cap spend rather than just track it.

   • [claimed-docs] “Model APIs bill by token and enforce request and token rate limits. You can also set a workspace budget and query usage by API key or model.”
   • [claimed-docs] “To monitor token and request consumption by API key or model, see Usage.”
   • [claimed-docs] “x-ratelimit-remaining-requests: Reports how many requests remain before you reach the request rate limit.”
   • [claimed-docs] “You can also set a workspace budget and query usage by API key or model.”

   proof ↗⚑ flag

2. founderSee public per-token prices for every hosted model without talking to sales

   weight 3 · round to Cerebras Inference
   Cerebras Inference~partialcommunity4/10

   Cerebras publishes a public pricing page with self-serve signup, free credits, and tiered self-serve pricing ($10 minimum, 10x rate limits), showing pricing information is not gated behind a sales call for the basic tier (cerebras-docs-13,14,20,25,26). However, none of the evidence shows an actual published per-token $/M-token rate for each hosted model, and one pricing-related doc references access via 'partner APIs' plus a community comment explicitly wonders whether trying the cloud is possible without contacting sales, suggesting the full price list isn't clearly self-evident. Missing for 10: an explicit per-model per-token price table, confirmation that all hosted models (not just self-serve tiers) have listed rates, and independent corroboration that no sales contact is needed to see model-level pricing.

   • [claimed-docs] “Get started with $5 in free credits after making an account”
   • [claimed-docs] “Self-serve payment starting at just $10 * 10x higher rate limits than free tier * Higher priority processing”
   • [claimed-docs] “Self-serve payment starting at just $10 ... 10x higher rate limits than free tier ... Higher priority processing”
   • [claimed-docs] “Get started with $5 in free credit after creating an account. Prototype prompts, agents, and real-time apps before you spend a dollar.”
   • [claimed-docs] “Get access to Cerebras Inference through our partner APIs”
   • [community] “Very interested in playing with their hardware and cloud. Also I wonder if it's possible to try cloud without contacting their sales.”

   proof ↗⚑ flag

   Baseten~partialprobed3/10

   Docs confirm Model APIs are billed per token and that usage/rate limits can be queried without contacting sales, but no evidence shows an actual public price list with per-model per-token rates; the models endpoint even requires an API key to enumerate the catalog. missing for 10: a public pricing/rate-card page listing token prices per model, independent confirmation that pricing is visible without account creation or sales contact.

   • [claimed-docs] “Model APIs bill by token and enforce request and token rate limits. You can also set a workspace budget and query usage by API key or model.”
   • [claimed-docs] “To raise a Basic account's limits, request email verification. You can also use that form to move to Pro or Enterprise.”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://inference.baseten.co/v1/models without a key returned HTTP 401 (No Authorization header prov…”

   proof ↗⚑ flag

1. ai-native userChoose where my data is stored (region/residency)

   weight 2 · round drawn
   Cerebras Inference—none0/10

   No evidence pack item mentions data residency, regional storage options, or any control over where data is processed/stored; only dedicated endpoint (capacity isolation, not geography) and general API docs are present.

   ⚑ flag

   Baseten—none0/10

   No evidence in the pack mentions region selection, data residency options, or multi-region storage controls for user data; deployments are described as active-active across clusters/clouds for reliability (baseten-docs-15), not as a user-selectable residency control. Missing for 10: any mention of region/residency selection, data storage location controls, or compliance-driven data locality options.

   • [claimed-docs] “Deployments run active-active across clusters and clouds. If a region or provider loses capacity, MCM reroutes and reprovisions workloads.”

   proof ↗⚑ flag

2. ai-native userPrevent my data from being used to train AI models

   weight 3 · round drawn
   Cerebras Inference—none0/10

   No evidence pack item addresses a data-privacy/training-opt-out policy, data retention terms, or zero-retention agreement for Cerebras Inference API usage; nothing confirms user data is excluded from model training.

   ⚑ flag

   Baseten—none0/10

   No evidence pack content addresses data usage for training, opt-out controls, or any privacy/data-retention policy commitments; the axis is applicable (Baseten processes customer data/prompts and could plausibly offer such guarantees) but nothing in the docs, GitHub, or community evidence confirms it.

   ⚑ flag

3. ai-native userControl data retention and deletion

   weight 2 · round drawn
   Cerebras Inference—none0/10

   No evidence pack item discusses data retention policies, deletion controls, or privacy/data-handling settings for Cerebras Inference; documentation focuses on API usage, performance, and pricing only.

   ⚑ flag

   Baseten—none0/10

   The evidence pack covers deployment, training, inference APIs, pricing, and coding-agent integration, but no documentation addresses data retention policies, deletion of inputs/outputs, or user control over stored data/logs. This is a fair axis for an AI inference/hosting platform (buyers routinely ask about data handling), so it applies, but there is no evidence it is delivered.

   ⚑ flag

4. ai-native userOpt out of telemetry and usage tracking

   weight 2 · round drawn
   Cerebras Inference—none0/10

   No evidence in the pack addresses telemetry, usage tracking, or opt-out privacy controls for Cerebras Inference; the docs focus on API usage, pricing, and performance, not data collection settings.

   ⚑ flag

   Baseten—none0/10

   No evidence in the pack addresses telemetry/usage-tracking opt-out settings for Baseten's platform, CLI, or SDK; the evidence only covers inference features, training, and pricing/usage monitoring for billing purposes, not privacy telemetry controls.

   ⚑ flag

Reliability

1. founderGet a stated availability SLA on paid or enterprise tiers

   weight 1 · round drawn
   Cerebras Inference—none0/10

   Evidence shows a public status page and dedicated/reserved-capacity endpoints for enterprise customers, but nowhere in docs or pricing pages is an explicit uptime SLA percentage, credit policy, or contractual availability guarantee stated for paid or enterprise tiers. Community threads even highlight unpredictable rate-limiting and onboarding issues rather than confirming a formal SLA.

   • [claimed-docs] “A dedicated endpoint is a private, provisioned instance of the Cerebras Inference service reserved exclusively for your organization.”
   • [claimed-docs] “Your endpoint runs on reserved capacity that is not shared with other customers, so your performance is never impacted by other workloads.”
   • [probe] “PROBE status-page (2026-09-04): https://status.cerebras.ai returns HTTP 200 and renders a public service-status page (page body includes "op…”
   • [community] “It hits the request per minute limit instantly and then you wait a minute. (API Error: 422 ... wrong_api_format when integrating with claude…”

   proof ↗⚑ flag

   Baseten—none0/10

   Baseten documents Pro/Enterprise tiers, active-active multi-cloud deployments, and a public status page, but no evidence pack item states an actual SLA (uptime percentage, response-time guarantee, or contractual commitment) for any paid or enterprise tier.

   • [claimed-docs] “To raise a Basic account's limits, request email verification. You can also use that form to move to Pro or Enterprise.”
   • [claimed-docs] “Deployments run active-active across clusters and clouds. If a region or provider loses capacity, MCM reroutes and reprovisions workloads.”
   • [probe] “PROBE status-page (2026-09-04): https://status.baseten.co returns HTTP 200 and renders a public service-status page (page body includes "ope…”

   proof ↗⚑ flag

2. founderCheck a public status page with incident history before betting production traffic on the platform

   weight 2 · round drawn
   Cerebras Inference~partialprobed6/10

   A live public status page (status.cerebras.ai) was confirmed via probe, returning HTTP 200 and showing an 'operational' status, which supports founders checking service health before committing production traffic. However, there's no evidence in the pack of a visible incident history log or historical uptime records on that page, and community threads note some real-world reliability hiccups (rate-limit bursts, onboarding scaling issues) without connecting them to the status page. Missing for 10: documented incident history/timeline on the status page, uptime SLA data, and independent confirmation that past incidents are publicly logged.

   • [probe] “PROBE status-page (2026-09-04): https://status.cerebras.ai returns HTTP 200 and renders a public service-status page (page body includes "op…”
   • [community] “apologies we just got a sudden burst of new users and traffic, it's scaling up now.”
   • [community] “It hits the request per minute limit instantly and then you wait a minute. (API Error: 422 ... wrong_api_format when integrating with claude…”

   proof ↗⚑ flag

   Baseten~partialprobed6/10

   A live probe confirms Baseten operates a public status page at status.baseten.co that returns HTTP 200 and shows current operational status, which is the core reliability-status signal a founder would check before committing production traffic. However, the evidence pack does not confirm the page includes a visible incident history/archive log, only that it renders current status. missing for 10: explicit confirmation that the status page displays past incident history/timeline, not just live status.

   • [probe] “PROBE status-page (2026-09-04): https://status.baseten.co returns HTTP 200 and renders a public service-status page (page body includes "ope…”

   proof ↗⚑ flag

Benchmarks

1. ml-engineerSee published tokens-per-second or latency numbers, benchmarks, or load-testing guides backing the provider's speed claims

   weight 2 · round to Cerebras Inference
   Cerebras Inference~partialcommunity5/10

   There is no first-party benchmark page or load-testing guide in the evidence pack, but community reports repeatedly cite concrete tok/s figures (e.g., 'break 300 tok/s', comparisons showing Cerebras far outpacing 100 tok/s H100 clusters) and the SDK docs mention a TTFT-reduction mechanism, giving some quantitative backing for speed claims. Missing for 10: an official published benchmark/whitepaper with tokens-per-second numbers, a load-testing guide, or independent third-party benchmark reports (e.g., Artificial Analysis) directly cited in the pack.

   • [community] “The DFlash2 draft model we're using was trained on a lot of code, so if you use it in a coding agent you'll probably notice it run a lot fas…”
   • [community] “This is astonishingly fast. I'm struggling to get over 100 tok/s on my own Llama 3.1 70b implementation on an 8x H100 cluster.”
   • [community] “It generates code faster than I can inspect it. In other words, it's needlessly fast.”
   • [community] “It's insanely fast. Here's an AI voice assistant I built that uses it: cerebras.vercel.app”
   • [github] “This SDK has a mechanism that sends a few requests to `/v1/tcp_warming` upon construction to reduce the TTFT.”

   proof ↗⚑ flag

   Baseten—none0/10

   The evidence pack covers Baseten's product features (Model APIs, Truss, training, coding-agent integrations) but contains no published benchmark numbers, tokens-per-second figures, latency claims, or load-testing guides; the only performance-adjacent comment is a third-party observation about client-side GIL bottlenecks, which is unrelated to Baseten's own speed claims.

   ⚑ flag

Serving

1. developerServe latency-sensitive workloads with fast time-to-first-token and high-throughput generation

   weight 3 · round to Cerebras Inference
   Cerebras Inference✓fullcommunity9/10

   Cerebras' whole value proposition centers on speed: dedicated wafer-scale inference, TTFT-optimized SDK warming (cerebras-gh-1), streaming API (cerebras-docs-5), dedicated non-shared capacity for consistent latency (cerebras-docs-24), and cache-hit optimizations for throughput (cerebras-docs-12/19/22). Independent hands-on community reports strongly corroborate extreme throughput/low-latency (300+ tok/s coding model, beating 8x H100 clusters, 'needlessly fast' code gen) across multiple HN threads (cerebras-comm-1,6,7,10,13,14,15). Missing for 10: no first-party published TTFT/tok-s benchmark numbers in this pack, and some community reports note rate-limit throttling and onboarding friction that slightly tempers the sustained-throughput story (cerebras-comm-11,4).

   • [github] “This SDK has a mechanism that sends a few requests to `/v1/tcp_warming` upon construction to reduce the TTFT.”
   • [claimed-docs] “The Cerebras API supports streaming responses, which send messages back in chunks and display them incrementally as the model generates them…”
   • [claimed-docs] “Your endpoint runs on reserved capacity that is not shared with other customers, so your performance is never impacted by other workloads.”
   • [claimed-docs] “Cached tokens don't count toward your uncached TPM limit, so a higher cache hit rate lets you process far more total tokens within the same …”
   • [claimed-docs] “a higher cache hit rate lets you process far more total tokens within the same uncached limit”
   • [claimed-docs] “Improving your cache hit rate lets the same uncached limit serve significantly more total tokens”
   • [community] “I used their Coding Plan for a few months. It is genuinely difficult to keep up with the models. The output is so fast. Qwen 3.8 27B is like…”
   • [community] “The DFlash2 draft model we're using was trained on a lot of code, so if you use it in a coding agent you'll probably notice it run a lot fas…”
   • [community] “This is astonishingly fast. I'm struggling to get over 100 tok/s on my own Llama 3.1 70b implementation on an 8x H100 cluster.”
   • [community] “It generates code faster than I can inspect it. In other words, it's needlessly fast.”
   • [community] “It's insanely fast. Here's an AI voice assistant I built that uses it: cerebras.vercel.app”
   • [community] “Ok that speed's fucking ridiculous are you kidding me?!?!?! I just tried the Chat trial wtf.”
   • [community] “Damn, that's some impressive speeds. At that rate it doesn't matter if the first try resulted in an unwanted answer, you'll be able to run o…”
   • [community] “It hits the request per minute limit instantly and then you wait a minute. (API Error: 422 ... wrong_api_format when integrating with claude…”

   proof ↗⚑ flag

   Baseten✓fullcommunity8/10

   Baseten's docs and GitHub materials show explicit architecture for latency-sensitive, high-throughput serving: token-by-token streaming (baseten-docs-8, baseten-docs-26), dedicated GPU deployment with TensorRT-LLM/vLLM/SGLang optimized builds via Truss (baseten-gh-2, baseten-gh-4, baseten-gh-6), automatic KV-cache discounting for repeated prompt tokens (baseten-docs-11), and active-active multi-cluster routing to avoid capacity-related slowdowns (baseten-docs-15). Async inference is also offered for non-latency-critical batch work, showing the platform differentiates paths for latency vs throughput use cases (baseten-docs-9, baseten-docs-27). missing for 10: published quantitative TTFT/throughput benchmarks or independent third-party latency comparisons; one community note (baseten-comm-3) suggests client-side bottlenecks can arise in high-throughput client code, an important but non-contradicting caveat.

   • [claimed-docs] “Return model output token by token as it is generated.”
   • [claimed-docs] “Streaming sends tokens as they are generated over server-sent events, which suits long generations and UIs where partial output beats a blan…”
   • [claimed-docs] “Cached input tokens are prompt tokens served from the KV cache at a discounted rate. Caching is automatic and requires no request flags.”
   • [claimed-docs] “Deployments run active-active across clusters and clouds. If a region or provider loses capacity, MCM reroutes and reprovisions workloads.”
   • [github] “You write a `config.yaml` that specifies the model, the hardware, and the engine, then `uvx truss push` builds a TensorRT-optimized containe…”
   • [github] “Support for all Python frameworks: From `transformers` and `diffusers` to PyTorch and TensorFlow to vLLM, SGLang, and TensorRT-LLM, Truss su…”
   • [github] “Deploying a model to Baseten via Truss turns a Hugging Face model into a production-ready API endpoint. You write a `config.yaml` that speci…”
   • [claimed-docs] “Async inference returns a request ID quickly and completes later through webhook or polling, which suits batch work, long documents”
   • [claimed-docs] “Async inference returns a request ID quickly and completes later through webhook or polling, which suits batch work, long documents, or any …”
   • [community] “Developer notes that with Baseten.co embedding workloads, the client (not server) becomes the bottleneck due to Python's GIL, prompting them…”

   proof ↗⚑ flag

2. ml-engineerBenefit from prompt/prefix caching that reduces latency or cost on repeated context

   weight 1 · round to Baseten
   Cerebras Inference~partialclaimed6/10

   Docs confirm Cerebras supports prompt/prefix caching for rate-limit purposes—cached tokens don't count toward uncached TPM limits, letting engineers push more total throughput—implying reduced cost/latency impact on repeated context. However, there's no explicit documentation on cache TTL, hit-rate mechanics, pricing discount for cached tokens, or independent benchmarks showing actual latency/cost reduction from caching in practice. missing for 10: documented cache pricing/discount, cache TTL/eviction details, independent latency benchmarks demonstrating caching benefit.

   • [claimed-docs] “Cached tokens don't count toward your uncached TPM limit, so a higher cache hit rate lets you process far more total tokens within the same …”
   • [claimed-docs] “a higher cache hit rate lets you process far more total tokens within the same uncached limit”
   • [claimed-docs] “Improving your cache hit rate lets the same uncached limit serve significantly more total tokens”

   proof ↗⚑ flag

   Baseten✓fullclaimed7/10

   Docs explicitly describe automatic prompt caching: cached input tokens are served from KV cache at a discounted rate with no request flags needed, directly addressing latency/cost reduction on repeated context. missing for 10: no quantified latency/cost benchmarks, no independent/community corroboration of caching behavior in practice.

   • [claimed-docs] “Cached input tokens are prompt tokens served from the KV cache at a discounted rate. Caching is automatic and requires no request flags.”
   • [claimed-docs] “Model APIs bill by token and enforce request and token rate limits. You can also set a workspace budget and query usage by API key or model.”

   proof ↗⚑ flag

3. developerStream completions token by token over SSE for responsive user experiences

   weight 3 · round drawn
   Cerebras Inference✓fullcommunity8/10

   Official docs explicitly describe streaming responses that send messages back in chunks and display them incrementally as the model generates them, and the SDK/API are OpenAI-compatible so standard SSE streaming semantics apply; community reports independently corroborate extremely fast token generation experienced in real-time apps. missing for 10: explicit SSE protocol details/code sample and independent hands-on confirmation specifically of streaming (vs just raw speed).

   • [claimed-docs] “The Cerebras API supports streaming responses, which send messages back in chunks and display them incrementally as the model generates them…”
   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.”
   • [community] “It's insanely fast. Here's an AI voice assistant I built that uses it: cerebras.vercel.app”
   • [community] “This is astonishingly fast. I'm struggling to get over 100 tok/s on my own Llama 3.1 70b implementation on an 8x H100 cluster.”

   proof ↗⚑ flag

   Baseten✓fullclaimed8/10

   Baseten's docs explicitly describe SSE token-by-token streaming ('Return model output token by token as it is generated' / 'Streaming sends tokens as they are generated over server-sent events') as a first-class inference mode alongside async, with OpenAI/Anthropic-compatible client SDK support for easy integration. Missing for 10: no independent/hands-on benchmark or community confirmation of streaming latency/responsiveness in practice.

   • [claimed-docs] “Return model output token by token as it is generated.”
   • [claimed-docs] “Streaming sends tokens as they are generated over server-sent events, which suits long generations and UIs where partial output beats a blan…”
   • [claimed-docs] “They support the OpenAI Chat Completions API and the Anthropic Messages API in beta, so you can use familiar client SDKs.”

   proof ↗⚑ flag

Structured

1. developerEnforce structured outputs against a JSON schema (or grammar) so model responses parse reliably

   weight 3 · round to Baseten
   Cerebras Inference✓fullclaimed7/10

   Cerebras docs explicitly describe a Structured Outputs feature that constrains model responses to a JSON schema for reliable parsing, alongside OpenAI-API compatibility that typically carries this through standard SDKs. Missing for 10: independent/hands-on confirmation of schema-enforcement reliability, details on grammar-based constraints beyond JSON schema, and coverage of edge cases (nested schemas, strict mode) in evidence.

   • [claimed-docs] “Structured Outputs constrains model responses to a JSON schema so applications can process generated data reliably.”
   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.”

   proof ↗⚑ flag

   Baseten✓fullclaimed8/10

   Baseten has dedicated first-party docs on structured outputs enforcing JSON schemas for reliable data extraction, plus separate function/tool calling support and OpenAI-compatible client integration (e.g., LangChain with_structured_output) confirming reliable parsing workflows. Missing for 10: independent/hands-on validation of schema-enforcement reliability and no mention of grammar-based (e.g., regex/CFG) constraints beyond JSON schema.

   • [claimed-docs] “Structured outputs let you generate text that conforms to specific JSON schemas, providing reliable data extraction and controlled text gene…”
   • [claimed-docs] “Function calling* (also called *tool calling*) lets a model choose a tool and produce its arguments from a user request.”
   • [claimed-docs] “Function calling (also called tool calling) lets a model choose a tool and produce its arguments from a user request.”
   • [claimed-docs] “Because Baseten exposes an OpenAI-compatible endpoint, you can use LangChain's `ChatOpenAI` with `with_structured_output` by pointing `base_…”
   • [claimed-docs] “They support the OpenAI Chat Completions API and the Anthropic Messages API in beta, so you can use familiar client SDKs.”

   proof ↗⚑ flag

Tools

1. ai-native userRely on faithful function/tool calling — including parallel and multi-step tool use — so agent loops run on open models without breaking

   weight 3 · round to Cerebras Inference
   Cerebras Inference~partialcommunity5/10

   Cerebras documents a tool-calling/function-calling capability (cerebras-docs-7) as part of its OpenAI-compatible API, and community reports mention using it for coding agents (cerebras-comm-6, cerebras-comm-12) suggesting real agentic integrations exist. However, the docs pack contains no detail on parallel or multi-step tool-call handling, and one user reports API format errors when integrating with an agent router (cerebras-comm-11), hinting at possible friction in tool-use compatibility. missing for 10: explicit documentation of parallel tool calls, multi-step tool-call chaining, and independent benchmarks confirming reliability of tool-calling in long agent loops.

   • [claimed-docs] “Tool calling, also known as tool use or function calling, lets a model request functions that your application defines.”
   • [community] “The DFlash2 draft model we're using was trained on a lot of code, so if you use it in a coding agent you'll probably notice it run a lot fas…”
   • [community] “I've been waiting on this for a LONG time. Integration with Cursor when Cerebras released their earlier models was patchy at best, even thro…”
   • [community] “It hits the request per minute limit instantly and then you wait a minute. (API Error: 422 ... wrong_api_format when integrating with claude…”

   proof ↗⚑ flag

   Baseten~partialclaimed4/10

   Baseten docs confirm function/tool-calling support ("lets a model choose a tool and produce its arguments from a user request") and structured outputs for reliable JSON generation, but the pack never mentions parallel tool calls, multi-step agent loops, or any reliability testing on open models. Missing for 10: explicit parallel tool-call support docs, multi-step agent loop examples, and independent/hands-on evidence of reliability across open models.

   • [claimed-docs] “Function calling* (also called *tool calling*) lets a model choose a tool and produce its arguments from a user request.”
   • [claimed-docs] “Function calling (also called tool calling) lets a model choose a tool and produce its arguments from a user request.”
   • [claimed-docs] “Structured outputs let you generate text that conforms to specific JSON schemas, providing reliable data extraction and controlled text gene…”
   • [claimed-docs] “Because Baseten exposes an OpenAI-compatible endpoint, you can use LangChain's `ChatOpenAI` with `with_structured_output` by pointing `base_…”

   proof ↗⚑ flag