Cerebras Inference

Drive the product through a documented public API G

Agent access

Connect an agent via an official MCP server G

Agent access

Delegate tasks to a built-in AI assistant inside the product G

Agentic features

Plug MCP servers into this product so it can use their tools G

Agent access

Point an agent at llms.txt or agent-oriented docs G

Agent access

Build against official SDKs G

Agent access

Run the product headlessly / in CI for automation G

Agent access

Explore an interactive API reference with runnable examples G

Api quality

Set up automations that run autonomously in the background G

Agentic features

Download a machine-readable API spec (OpenAPI or equivalent) G

Api quality

Issue scoped/least-privilege API credentials for an agent G

Agent access

Rely on versioned APIs with a documented deprecation policy G

Api quality

Use an official CLI G

Agent access

Get AI-generated insights and suggestions from my data inside the product G

Agentic features

Operate the product with natural-language commands G

Agentic features

Subscribe to events via webhooks G

Agent access

Test against a sandbox environment without touching production data G

Api quality

Point an existing OpenAI SDK client at the provider by changing only the base URL and API key C

Compat

Serve latency-sensitive workloads with fast time-to-first-token and high-throughput generation C

Serving

Stream completions token by token over SSE for responsive user experiences C

Serving

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

Catalog

Enforce structured outputs against a JSON schema (or grammar) so model responses parse reliably C

Structured

Have an agent switch to or away from this provider mid-workflow because it speaks the standard chat-completions API without provider-specific code changes C

Compat

Rely on faithful function/tool calling — including parallel and multi-step tool use — so agent loops run on open models without breaking C

Tools

See public per-token prices for every hosted model without talking to sales G

Pricing

Define rules that trigger actions automatically on events G

Export all of my data in open formats and leave G

Prevent my data from being used to train AI models G

Self-host the core product G

Have an agent enumerate the live model catalog programmatically via a documented GET /v1/models-style endpoint C

Catalog

Perform bulk operations across many items at once G

Check a public status page with incident history before betting production traffic on the platform G

Reliability

Do everything through the API that I can do in the UI G

Plug the provider into coding agents and agent frameworks through documented, first-party integration guides C

Compat

Read documented rate limits and how they scale across usage tiers before I hit them in production G

Limits

Deploy a model on dedicated GPU capacity with autoscaling so my traffic is isolated from the shared serverless pool C

Dedicated

See published tokens-per-second or latency numbers, benchmarks, or load-testing guides backing the provider's speed claims C

Benchmarks

Fine-tune a supported base model on my own data and serve the result on the same platform C

Fine tune

Submit asynchronous batch inference jobs at a documented discount versus real-time pricing G

Batch

Upload and serve my own custom model weights or LoRA adapters C

Fine tune

Get newly released open-weight models on the platform quickly after their public release C

Catalog

Choose where my data is stored (region/residency) G

Control data retention and deletion G

Opt out of telemetry and usage tracking G

Read the product's source under an open license G

Schedule recurring jobs or workflows G

Benefit from prompt/prefix caching that reduces latency or cost on repeated context C

Serving

Call vision, audio, or image-generation models beyond text chat on the same platform C

Modalities

Get a stated availability SLA on paid or enterprise tiers C

Reliability

Set spending caps or budget alerts so a runaway workload cannot generate an unbounded bill G

Pricing

Generate embeddings (and rerank results) for retrieval pipelines without a second vendor C

Modalities

Rely on a documented deprecation policy with advance notice before a hosted model is removed C

Catalog

Version, review, and roll back my automations G