Vercel AI Gateway vs Kong AI Gateway

Agent access

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

   weight 2 · round to Vercel AI Gateway
   Vercel AI Gateway✓fullprobed9/10

   Vercel confirms a live llms.txt (probe-1) plus per-page .md documentation endpoints (probe-2) and an OpenAPI spec (probe-3), all machine-readable for agent consumption, and docs explicitly mention coding-agent setup guidance (docs-8, docs-24). Missing for 10: no independent/community confirmation that agents actually use these llms.txt/docs successfully in practice.

   • [probe] “PROBE llms.txt: HTTP 200 at https://vercel.com/llms.txt # Vercel > Vercel is a cloud platform for building, deploying, and scaling web appl…”
   • [probe] “PROBE docs-md: HTTP 200 at https://vercel.com/docs/ai-gateway.md --- title: AI Gateway product: vercel url: /docs/ai-gateway canonical_url: …”
   • [probe] “PROBE openapi: HTTP 200 at https://vercel.com/openapi.json — contains "openapi" key”
   • [claimed-docs] “vercel ai-gateway coding-agents setup”
   • [claimed-docs] “AI Gateway routes requests, manages fallbacks and budgets, monitors usage, and connects supported coding agents.”

   proof ↗⚑ flag

   Kong AI Gateway✓fullprobed8/10

   A probe confirms developer.konghq.com/llms.txt returns HTTP 200 with a valid llms.txt manifest listing Kong's docs (including AI Gateway), and individual doc pages are also served as .md files (e.g. ai-providers.md, a2a.md), making the docs directly agent-consumable. missing for 10: no explicit vendor statement instructing agents to use llms.txt, and the generic /.md probe for the ai-gateway index returned 404, showing coverage is per-page rather than fully uniform.

   • [probe] “PROBE llms.txt: HTTP 200 at https://developer.konghq.com/llms.txt # developer.konghq.com > Kong's developer documentation for Kong Gateway,…”
   • [claimed-docs] “The core of AI Gateway is the ability to serve AI Models from various AI Model Providers via a provider-agnostic API.”
   • [probe] “PROBE docs-md: HTTP 404 at https://developer.konghq.com/ai-gateway/.md”

   proof ↗⚑ flag

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

   weight 2 · round to Vercel AI Gateway
   Vercel AI Gateway✓fullprobed8/10

   AI Gateway is an API/HTTP gateway with OpenAI-compatible endpoints, unauthenticated model listing, OIDC token auth for deployed apps, and a keyless models endpoint verified live — all of which support pure headless/CI/automation use without any UI dependency. missing for 10: no explicit CI pipeline example (e.g., GitHub Actions) or independent third-party report of CI usage.

   • [claimed-docs] “You can use existing OpenAI client libraries, switch to AI Gateway with a URL change, and keep your current tools and workflows without code…”
   • [claimed-docs] “For applications deployed on Vercel, OIDC tokens are automatically available as `VERCEL_OIDC_TOKEN`. No secrets to manage, no keys to rotate…”
   • [claimed-docs] “This endpoint requires no authentication and returns detailed information including model IDs, context windows, and pricing.”
   • [claimed-docs] “For applications deployed on Vercel, OIDC tokens are automatically available as VERCEL_OIDC_TOKEN. No secrets to manage, no keys to rotate. …”
   • [claimed-docs] “You can use the AI Gateway's Chat Completions API with existing tools and libraries like the OpenAI client libraries and AI SDK. Point your …”
   • [probe] “PROBE openapi: HTTP 200 at https://vercel.com/openapi.json — contains "openapi" key”
   • [probe] “PROBE keyless-endpoint: GET https://ai-gateway.vercel.sh/v1/models returned HTTP 200 application/json with an OpenAI-compatible {"object":"l…”

   proof ↗⚑ flag

   Kong AI Gateway~partialprobed6/10

   Kong AI Gateway exposes a CLI (kongctl) and Konnect APIs (list-ai-gateways, config conversion) plus a quickstart script, implying it can be driven headlessly and scripted rather than only via UI. However there is no explicit CI or CD pipeline example, declarative config-as-code workflow, or GitOps documentation demonstrating automated headless operation in CI. Missing for 10: explicit CI or CD pipeline documentation, dedicated headless-mode guide, independent CI usage report.

   • [claimed-docs] “list-ai-gateways summary: List AI Gateways description: Returns a list of AI Gateways in the organization.”
   • [claimed-docs] “walks you through migrating an existing configuration using the kongctl AI Gateway conversion extension”
   • [claimed-docs] “You can use the quickstart script to get a demo instance of AI Gateway running almost instantly.”
   • [probe] “official CLI documented at https://developer.konghq.com/kongctl/”

   proof ↗⚑ flag

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

   weight 3 · round to Kong AI Gateway
   Vercel AI Gateway—none0/10

   The evidence describes Vercel AI Gateway as a model-routing/proxy layer (provider switching, fallbacks, spend monitoring, caching, coding-agent connections) but contains no mention of MCP server integration or tool-use via MCP anywhere in the docs pack.

   ⚑ flag

   Kong AI Gateway~partialclaimed6/10

   Kong AI Gateway documents an 'AI MCP Proxy' capability that explicitly can front upstream MCP servers (in addition to converting REST APIs into MCP tools), meaning it can plug in external MCP servers so the traffic/tools flow through the gateway. However, the evidence is a single brief concept mention with no detailed configuration walkthrough, no example of registering a specific upstream MCP server, and no independent/hands-on corroboration. Missing for 10: a full docs page on connecting/registering upstream MCP servers, concrete config examples, and third-party verification.

   • [claimed-docs] “AI MCP Proxy bridges Kong-managed Services to the Model Context Protocol, converting REST APIs into MCP tools or fronting upstream MCP serve…”
   • [claimed-docs] “Turn any API into an MCP server using the AI MCP Server entity. This approach does not require an LLM and provides full control over product…”
   • [claimed-docs] “Govern LLM, MCP, and agent-to-agent (A2A) traffic with the same Kong AI Gateway.”

   proof ↗⚑ flag

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

   weight 3 · round to Kong AI Gateway
   Vercel AI Gateway—none0/10

   The docs mention connecting 'supported coding agents' and generic gateway integration, but there is no evidence of an official MCP (Model Context Protocol) server or endpoint that agents can connect to. Missing for 10: explicit MCP server documentation, MCP endpoint URL, and demonstration of an agent connecting via MCP.

   • [claimed-docs] “vercel ai-gateway coding-agents setup”
   • [claimed-docs] “AI Gateway routes requests, manages fallbacks and budgets, monitors usage, and connects supported coding agents.”

   proof ↗⚑ flag

   Kong AI Gateway✓fullclaimed8/10

   Kong AI Gateway ships an official 'AI MCP Server' entity and 'AI MCP Proxy' that converts REST APIs into MCP tools or fronts upstream MCP servers, letting agents connect via a first-party MCP interface, plus governance of MCP traffic (auth, OTel metrics, rate limiting) alongside it. Missing for 10: independent/hands-on verification of an agent successfully connecting through the MCP server and no example configuration walkthrough shown in the pack.

   • [claimed-docs] “AI MCP Proxy bridges Kong-managed Services to the Model Context Protocol, converting REST APIs into MCP tools or fronting upstream MCP serve…”
   • [claimed-docs] “Turn any API into an MCP server using the AI MCP Server entity. This approach does not require an LLM and provides full control over product…”
   • [claimed-docs] “Govern LLM, MCP, and agent-to-agent (A2A) traffic with the same Kong AI Gateway.”
   • [claimed-docs] “AI Gateway can export OpenTelemetry (OTLP) metrics for generative AI, MCP, and A2A traffic through an OpenTelemetry AI Policy.”

   proof ↗⚑ flag

5. ai-native userUse an official CLI

   weight 2 · round to Kong AI Gateway
   Vercel AI Gateway~partialclaimed3/10

   The only evidence of a CLI is the fragment 'vercel ai-gateway coding-agents setup', implying an official `vercel` CLI subcommand for configuring the AI Gateway with coding agents, but there is no detailed command reference, install instructions, or broader CLI documentation for AI-native workflows beyond this single mention. Missing for 10: full CLI command reference/docs, examples of CLI usage for model routing/config, independent confirmation of CLI functionality.

   • [claimed-docs] “vercel ai-gateway coding-agents setup”

   proof ↗⚑ flag

   Kong AI Gateway✓fullprobed7/10

   Kong ships an official CLI, kongctl, documented at a dedicated docs URL and explicitly used for AI Gateway tasks (e.g., the 'kongctl AI Gateway conversion extension' for migrating configurations). This confirms a real, first-party CLI exists for AI Gateway workflows. Missing for 10: deeper documentation of kongctl's full command surface for AI-native tasks (agents, MCP, A2A) and independent/hands-on corroboration beyond the docs probe.

   • [claimed-docs] “walks you through migrating an existing configuration using the kongctl AI Gateway conversion extension”
   • [probe] “official CLI documented at https://developer.konghq.com/kongctl/”

   proof ↗⚑ flag

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

   weight 3 · round to Vercel AI Gateway
   Vercel AI Gateway✓fullprobed9/10

   AI Gateway exposes a documented, OpenAI-compatible public API (Chat Completions, models endpoint) with a verified OpenAPI spec, unauthenticated models endpoint, and probe confirming a live JSON API response, enabling programmatic/agentic control via standard SDKs or raw HTTP. missing for 10: independent third-party developer writeups explicitly praising the API's completeness/stability beyond Vercel's own docs and probes.

   • [claimed-docs] “You can use existing OpenAI client libraries, switch to AI Gateway with a URL change, and keep your current tools and workflows without code…”
   • [claimed-docs] “This endpoint requires no authentication and returns detailed information including model IDs, context windows, and pricing.”
   • [claimed-docs] “Query the models endpoint directly to get a JSON list of all available models with pricing and capabilities”
   • [claimed-docs] “You can use the AI Gateway's Chat Completions API with existing tools and libraries like the OpenAI client libraries and AI SDK. Point your …”
   • [probe] “PROBE openapi: HTTP 200 at https://vercel.com/openapi.json — contains "openapi" key”
   • [probe] “PROBE keyless-endpoint: GET https://ai-gateway.vercel.sh/v1/models returned HTTP 200 application/json with an OpenAI-compatible {"object":"l…”

   proof ↗⚑ flag

   Kong AI Gateway~partialprobed6/10

   Kong documents a Konnect API for managing AI Gateway resources (e.g., list-ai-gateways endpoint) and a dedicated kongctl CLI, showing programmatic control exists and is documented. However, probes for a discoverable OpenAPI/swagger spec at standard locations all returned 404, so the breadth/openness of the API surface is not independently confirmed. Missing for 10: a publicly discoverable OpenAPI/swagger spec, broader endpoint coverage beyond the single example, and independent/hands-on confirmation of API usage.

   • [claimed-docs] “list-ai-gateways summary: List AI Gateways description: Returns a list of AI Gateways in the organization.”
   • [probe] “official CLI documented at https://developer.konghq.com/kongctl/”
   • [probe] “PROBE openapi: all candidate paths 404 (https://developer.konghq.com/openapi.json, https://developer.konghq.com/swagger.json, https://develo…”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   Vercel AI Gateway~partialclaimed5/10

   Docs show budget caps that can be set per API key, per project, or per team member (docs-16), and per-key usage breakdown in Requests logs (docs-12), plus short-lived OIDC tokens for Vercel-deployed apps that avoid static secrets (docs-14/22). This supports issuing distinct, budget-limited keys for an agent, but there is no documentation of fine-grained permission scoping (e.g. restricting a key to specific models/providers/actions) beyond spend limits. missing for 10: explicit least-privilege scoping controls (model/provider/action restrictions per key), role-based permission model, first-party or independent confirmation of true least-privilege agent credentials

   • [claimed-docs] “To cap how much your team, a project, an API key, or a team member can spend, set [budgets]”
   • [claimed-docs] “The Requests section displays summaries by project, API key, and a detailed log of all requests.”
   • [claimed-docs] “For applications deployed on Vercel, OIDC tokens are automatically available as `VERCEL_OIDC_TOKEN`. No secrets to manage, no keys to rotate…”
   • [claimed-docs] “For applications deployed on Vercel, OIDC tokens are automatically available as VERCEL_OIDC_TOKEN. No secrets to manage, no keys to rotate. …”

   proof ↗⚑ flag

   Kong AI Gateway~partialclaimed5/10

   Kong AI Gateway provides building blocks relevant to credential control for agents — AI Auth Strategy entities can authenticate via enterprise IdPs (Okta/Azure AD/OIDC) 'without managing keys manually,' and AI Vault lets model providers, auth strategies, agents, and MCP servers reference secrets from external backends instead of storing literal keys, plus Azure Managed Identity support for scoped cloud authentication. However, none of this evidence explicitly describes issuing scoped/least-privilege credentials per agent (e.g., fine-grained permission scopes, per-agent token minting) — it's centralized secret referencing and IdP auth rather than a dedicated least-privilege credential-issuance mechanism. missing for 10: explicit documentation of scoped/least-privilege credential issuance per agent, granular permission/scope controls tied to individual agent identities, and independent/hands-on verification of this behavior.

   • [claimed-docs] “An AI Vault entity lets you register an external secret backend (AWS Secrets Manager, HashiCorp Vault, environment variables, or others) so …”
   • [claimed-docs] “Authenticate enterprise users through an existing identity provider (Okta, Azure AD, Google, or any OIDC-compliant IdP) without managing key…”
   • [claimed-docs] “Yes, if AI Gateway is running on Azure, you can configure an AI Model Provider to detect the designated Managed Identity or User-Assigned Id…”

   proof ↗⚑ flag

8. ai-native userBuild against official SDKs

   weight 2 · round to Vercel AI Gateway
   Vercel AI Gateway✓fullprobed8/10

   AI Gateway is designed to be used via existing official SDKs (OpenAI client libraries, Anthropic SDK, Vercel AI SDK) with just a base URL/model-string change, documented in openai-compat and getting-started guides, and confirmed by a live OpenAI-compatible endpoint probe. It also supports agent-building primitives like ToolLoopAgent for agentic workflows. missing for 10: no independent/hands-on developer confirmation beyond vendor docs and one API probe.

   • [claimed-docs] “You can use existing OpenAI client libraries, switch to AI Gateway with a URL change, and keep your current tools and workflows without code…”
   • [claimed-docs] “Point your existing OpenAI or Anthropic SDK at AI Gateway. Same calls, no rewrites.”
   • [claimed-docs] “You can use the AI Gateway's Chat Completions API with existing tools and libraries like the OpenAI client libraries and AI SDK. Point your …”
   • [claimed-docs] “Move existing OpenAI, Anthropic, and AI SDK integrations to AI Gateway with a base URL swap.”
   • [claimed-docs] “const agent = new ToolLoopAgent({ model: 'anthropic/claude-sonnet-5', tools: { getWeather: tool({”
   • [probe] “PROBE keyless-endpoint: GET https://ai-gateway.vercel.sh/v1/models returned HTTP 200 application/json with an OpenAI-compatible {"object":"l…”

   proof ↗⚑ flag

   Kong AI Gateway—none0/10

   The evidence pack contains no mention of official SDKs for building against Kong AI Gateway (only a CLI 'kongctl' and REST API references), and probes explicitly show no OpenAPI/SDK artifacts (404s for openapi.json, swagger.json, etc.). While an API gateway could plausibly offer official client SDKs, none are documented here.

   • [probe] “PROBE openapi: all candidate paths 404 (https://developer.konghq.com/openapi.json, https://developer.konghq.com/swagger.json, https://develo…”
   • [probe] “official CLI documented at https://developer.konghq.com/kongctl/”
   • [claimed-docs] “list-ai-gateways summary: List AI Gateways description: Returns a list of AI Gateways in the organization.”

   proof ↗⚑ flag

Api quality

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

   weight 2 · round to Vercel AI Gateway
   Vercel AI Gateway~partialprobed5/10

   There's a documented OpenAPI spec and a live, keyless models endpoint that returns JSON model data, showing some interactive/runnable API surface, but there's no evidence of an interactive API reference UI (like Swagger/Redoc) with embedded runnable examples or a try-it-now console. missing for 10: an actual interactive API explorer/playground page, runnable code snippets embedded in docs, independent confirmation of a docs UI with live request execution.

   • [probe] “PROBE openapi: HTTP 200 at https://vercel.com/openapi.json — contains "openapi" key”
   • [probe] “PROBE keyless-endpoint: GET https://ai-gateway.vercel.sh/v1/models returned HTTP 200 application/json with an OpenAI-compatible {"object":"l…”
   • [claimed-docs] “This endpoint requires no authentication and returns detailed information including model IDs, context windows, and pricing.”
   • [claimed-docs] “Query the models endpoint directly to get a JSON list of all available models with pricing and capabilities”

   proof ↗⚑ flag

   Kong AI Gateway—none0/10

   Evidence shows Kong publishes API reference content (e.g., list-ai-gateways endpoint docs) but there is no indication of an interactive reference with runnable/try-it-out examples; probes for OpenAPI/swagger specs on the docs site all returned 404, suggesting no such interactive tooling is exposed.

   • [claimed-docs] “list-ai-gateways summary: List AI Gateways description: Returns a list of AI Gateways in the organization.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://developer.konghq.com/openapi.json, https://developer.konghq.com/swagger.json, https://develo…”

   proof ↗⚑ flag

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

   weight 2 · round to Vercel AI Gateway
   Vercel AI Gateway✓fullprobed7/10

   A probe confirms a live OpenAPI JSON spec at vercel.com/openapi.json (contains the 'openapi' key) and the docs also expose a keyless models JSON endpoint and OpenAI-compatible API, all of which are machine-readable and programmatically consumable. Missing for 10: explicit confirmation the openapi.json spec covers AI Gateway endpoints specifically (vs. general Vercel API) and independent third-party validation of the spec's completeness.

   • [probe] “PROBE openapi: HTTP 200 at https://vercel.com/openapi.json — contains "openapi" key”
   • [probe] “PROBE keyless-endpoint: GET https://ai-gateway.vercel.sh/v1/models returned HTTP 200 application/json with an OpenAI-compatible {"object":"l…”
   • [claimed-docs] “This endpoint requires no authentication and returns detailed information including model IDs, context windows, and pricing.”
   • [claimed-docs] “Query the models endpoint directly to get a JSON list of all available models with pricing and capabilities”

   proof ↗⚑ flag

   Kong AI Gateway—none0/10

   Evidence includes API reference pages (e.g. Konnect AI Gateway API docs) but no downloadable OpenAPI/Swagger spec file is evidenced; a direct probe for standard OpenAPI/swagger.json locations returned 404 at all candidate paths, indicating no machine-readable spec is exposed there.

   • [probe] “PROBE openapi: all candidate paths 404 (https://developer.konghq.com/openapi.json, https://developer.konghq.com/swagger.json, https://develo…”
   • [claimed-docs] “list-ai-gateways summary: List AI Gateways description: Returns a list of AI Gateways in the organization.”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   Vercel AI Gateway—none0/10

   Evidence shows an OpenAI-compatible endpoint with a 'v1' path and an openapi.json spec, but nowhere does the pack document an explicit API versioning scheme or a deprecation policy for endpoints/models. Missing for 10: explicit versioning strategy documentation, deprecation/sunset timelines, migration guidance for breaking changes.

   • [probe] “PROBE openapi: HTTP 200 at https://vercel.com/openapi.json — contains "openapi" key”
   • [probe] “PROBE keyless-endpoint: GET https://ai-gateway.vercel.sh/v1/models returned HTTP 200 application/json with an OpenAI-compatible {"object":"l…”
   • [claimed-docs] “Query the models endpoint directly to get a JSON list of all available models with pricing and capabilities”

   proof ↗⚑ flag

   Kong AI Gateway—none0/10

   The evidence shows an API reference exists (e.g., 'v1' Konnect AI Gateway API) but there is no documentation of a versioning scheme or deprecation policy for the AI Gateway APIs, and OpenAPI spec probes returned 404s.

   • [claimed-docs] “list-ai-gateways summary: List AI Gateways description: Returns a list of AI Gateways in the organization.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://developer.konghq.com/openapi.json, https://developer.konghq.com/swagger.json, https://develo…”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   Vercel AI Gateway—none0/10

   The evidence pack covers routing, failover, BYOK, observability, and embeddings, but there is no mention of any batch/bulk request API or capability to process many items in a single operation. Since a gateway product could plausibly offer batch endpoints (e.g., batch inference or bulk embeddings), the axis applies, but no evidence supports it being delivered.

   ⚑ flag

   Kong AI Gateway—none0/10

   The evidence covers proxying, observability, security, and MCP/A2A support but never describes any bulk or batch operation capability (e.g., batch creating/updating routes, plugins, or AI entities across many items at once). The kongctl CLI and Konnect API are mentioned but only for single-entity or config migration tasks, not bulk item processing.

   • [claimed-docs] “list-ai-gateways summary: List AI Gateways description: Returns a list of AI Gateways in the organization.”
   • [claimed-docs] “walks you through migrating an existing configuration using the kongctl AI Gateway conversion extension”
   • [probe] “official CLI documented at https://developer.konghq.com/kongctl/”

   proof ↗⚑ flag

2. ai-native userDefine rules that trigger actions automatically on events

   weight 3 · round to Kong AI Gateway
   Vercel AI Gateway~partialclaimed4/10

   The Gateway supports narrow built-in automation triggers — failover/retry on provider failure, per-provider timeouts triggering fast failover, and budget caps that block spend once a threshold is hit — which are rule-like actions triggered by events (docs-3, docs-10, docs-13, docs-16). However, there's no general-purpose, user-defined rules/event system (e.g., custom conditions, webhooks, arbitrary trigger-action pairs) for AI-native users to configure; the 'rules' are limited to routing, caching, and spend-cap logic. Missing for 10: a configurable rules engine for arbitrary events/actions, webhook or external-trigger support, and evidence of user-defined conditional automation beyond routing/budget defaults.

   • [claimed-docs] “High reliability. Automatically retries requests to other providers if one fails”
   • [claimed-docs] “You can set per-provider timeouts to trigger fast failover when a provider is slow to respond.”
   • [claimed-docs] “If a query using your credentials fails, AI Gateway will retry the query with its system credentials to improve service availability.”
   • [claimed-docs] “To cap how much your team, a project, an API key, or a team member can spend, set [budgets]”
   • [claimed-docs] “You can use order, only, and sort in providerOptions.gateway to control which providers handle your requests, in what order, and how they ar…”

   proof ↗⚑ flag

   Kong AI Gateway~partialclaimed5/10

   Kong AI Gateway supports policy-based automatic actions triggered by request/response events—e.g., Prompt Guards and content-safety policies acting on chat/completion requests, AI Rate Limiting Advanced enforcing limits automatically, AWS Guardrails validating requests/responses before forwarding, and semantic caching/routing acting on similarity triggers—which functions as rule-based automation on traffic events. However, this is scoped to gateway traffic policies rather than a general-purpose event/trigger automation framework for arbitrary conditions and actions. Missing for 10: evidence of a generic conditional rule engine (if-this-then-that style), user-defined custom triggers beyond built-in policies, and any workflow/orchestration automation beyond request-level policy enforcement.

   • [claimed-docs] “AI Gateway supports content safety features across providers and also includes our Prompt Guards that act on any llm/v1/chat or llm/v1/compl…”
   • [claimed-docs] “AI Rate Limiting Advanced - Plugin | Kong Docs”
   • [claimed-docs] “Apply safety and DLP policies to prevent toxic content and remove personally identifiable information.”
   • [claimed-docs] “AI Gateway can perform intelligent request routing, caching, and content filtering using semantic similarity queries.”
   • [claimed-docs] “Use AWS Guardrails to validate requests and/or responses before forwarding them between clients and upstream LLMs.”

   proof ↗⚑ flag

Caching

1. developerCache responses at the gateway to cut cost and latency on repeated requests

   weight 2 · round to Kong AI Gateway
   Vercel AI Gateway~partialcommunity6/10

   Docs explicitly describe a `caching: 'auto'` option in providerOptions.gateway that lets the gateway apply provider-appropriate caching automatically, directly supporting cost/latency reduction on repeated requests, and community pricing discussion confirms cache-read pricing exists for at least one model. However, details on cache hit-rate guarantees, TTL, scope, or independent benchmarks of latency/cost savings are absent. Missing for 10: independent hands-on benchmarks showing latency/cost reduction, documentation on cache TTL/invalidation/scope, confirmation caching works uniformly across all providers rather than being provider-dependent.

   • [claimed-docs] “You can use `caching: 'auto'` in `providerOptions.gateway` to let AI Gateway automatically apply the appropriate caching strategy based on t…”
   • [claimed-docs] “You can use caching: 'auto' in providerOptions.gateway to let AI Gateway automatically apply the appropriate caching strategy based on the p…”
   • [community] “Vercel cache read pricing for DeepSeek-v4: $0.01/M for flash, $0.14/M for pro — a 64% discount vs DeepSeek/OpenRouter on flash caching, prom…”

   proof ↗⚑ flag

   Kong AI Gateway✓fullclaimed8/10

   Kong ships a dedicated AI Semantic Cache plugin explicitly designed to cache LLM responses at the gateway, and separate docs confirm gateway-level semantic caching to reduce redundant calls and improve latency/cost. This is first-party documentation with a named product feature, though no independent benchmark or hands-on cost/latency measurement is provided. Missing for 10: independent/hands-on validation of actual cost/latency savings, and exact-match (non-semantic) cache configuration details.

   • [claimed-docs] “AI Semantic Cache - Plugin | Kong Docs”
   • [claimed-docs] “AI Gateway can perform intelligent request routing, caching, and content filtering using semantic similarity queries.”

   proof ↗⚑ flag

Latency

1. platform engineerRun traffic through gateway infrastructure that adds minimal latency overhead to provider calls

   weight 2 · round to Kong AI Gateway
   Vercel AI Gateway~partialclaimed4/10

   Docs show performance-related controls (per-provider timeouts for fast failover, sort-by-performance metric, automatic caching) that imply attention to latency, but there is no direct benchmark or explicit claim quantifying the gateway's own added latency overhead. Missing for 10: published latency benchmarks or first-party/independent measurements showing the gateway adds minimal overhead versus direct provider calls, and no community corroboration of low added latency.

   • [claimed-docs] “You can set per-provider timeouts to trigger fast failover when a provider is slow to respond.”
   • [claimed-docs] “You can also use `sort` to rank providers by a performance or cost metric. The gateway sorts providers by the chosen metric and tries them i…”
   • [claimed-docs] “You can use `caching: 'auto'` in `providerOptions.gateway` to let AI Gateway automatically apply the appropriate caching strategy based on t…”
   • [claimed-docs] “You can use caching: 'auto' in providerOptions.gateway to let AI Gateway automatically apply the appropriate caching strategy based on the p…”

   proof ↗⚑ flag

   Kong AI Gateway~partialclaimed5/10

   Kong documents latency-related tooling (usage analytics and OTel metrics that track LLM request latency and upstream processing time, semantic caching, load balancing across models) which implies attention to performance, but there is no explicit claim or benchmark quantifying the gateway's own added latency overhead as 'minimal'. missing for 10: quantified latency-overhead benchmarks, independent performance tests, explicit vendor claim of low proxy overhead.

   • [claimed-docs] “You can use these metrics to: Track LLM request latency and upstream provider processing time * Monitor token consumption ... * Calculate AI…”
   • [claimed-docs] “AI Semantic Cache - Plugin | Kong Docs”
   • [claimed-docs] “AI Gateway provides load balancing capabilities to distribute requests across multiple LLM models.”
   • [claimed-docs] “See usage analytics for requests, tokens, errors, and latency”

   proof ↗⚑ flag

Agent guardrails

1. ai-native userGive an autonomous agent its own key with budget and rate guardrails so it cannot run away on spend

   weight 2 · round drawn
   Vercel AI Gateway~partialcommunity6/10

   Docs confirm you can create per-API-key budgets and view spend/usage broken out by API key (docs-16, docs-12, docs-5), which supports giving an agent its own key with a spend cap. However, there is no explicit documentation of rate-limit guardrails (only spend budgets), and community evidence shows real friction around balance-based enforcement even with BYOK keys, so the 'cannot run away on spend' guarantee is only partially evidenced. Missing for 10: explicit per-key rate-limiting controls, clear docs on key-level rate guardrails (not just budget caps), and independent confirmation that budget enforcement reliably halts an autonomous agent before overspend.

   • [claimed-docs] “To cap how much your team, a project, an API key, or a team member can spend, set [budgets]”
   • [claimed-docs] “The Requests section displays summaries by project, API key, and a detailed log of all requests.”
   • [claimed-docs] “Spend monitoring. Monitor your spending across different providers”
   • [community] “If I bring my own Anthropic/OpenAI key and have usage available, why does Vercel's account balance determine whether my request executes? (V…”

   proof ↗⚑ flag

   Kong AI Gateway~partialclaimed6/10

   Kong AI Gateway supports per-consumer/agent authentication (AI Auth Strategy, AI Vault for keys) plus AI Rate Limiting Advanced and model cost calculation/tracking that matches provider billing, which together let an operator scope an agent's key and cap its request rate and see accrued spend. However, evidence shows cost *calculation and observability* (docs-9, docs-10, docs-18) rather than an explicit hard budget cap/kill-switch that automatically cuts off an agent once a spend threshold is reached, so the 'cannot run away on spend' guarantee is only partially evidenced. Missing for 10: explicit documented budget-threshold enforcement/auto-cutoff tied to a specific agent key, and a worked example of issuing a scoped key to an autonomous agent with combined rate+spend limits.

   • [claimed-docs] “AI Rate Limiting Advanced - Plugin | Kong Docs”
   • [claimed-docs] “AI Gateway calculates that figure for every request to a large language model (LLM) provider, matching what the provider actually bills.”
   • [claimed-docs] “An AI Vault entity lets you register an external secret backend (AWS Secrets Manager, HashiCorp Vault, environment variables, or others) so …”
   • [claimed-docs] “Authenticate enterprise users through an existing identity provider (Okta, Azure AD, Google, or any OIDC-compliant IdP) without managing key…”
   • [claimed-docs] “You can use these metrics to: Track LLM request latency and upstream provider processing time * Monitor token consumption ... * Calculate AI…”

   proof ↗⚑ flag

Budgets

1. platform engineerSet hard budgets and spend limits per key, team, or user

   weight 3 · round to Vercel AI Gateway
   Vercel AI Gateway✓fullclaimed8/10

   Docs explicitly state budgets can be set to cap spend for a team, project, API key, or team member, directly matching the story, and spend monitoring/usage dashboards support enforcement. missing for 10: independent/hands-on confirmation that hard budgets actually block spend at the limit (community evidence only discusses BYOK balance blocking, not per-key/team/user budget enforcement specifics) and more detail on granularity/configuration UI.

   • [claimed-docs] “To cap how much your team, a project, an API key, or a team member can spend, set [budgets]”
   • [claimed-docs] “Spend monitoring. Monitor your spending across different providers”
   • [claimed-docs] “The Requests section displays summaries by project, API key, and a detailed log of all requests.”
   • [claimed-docs] “The Usage section displays four metrics to help you monitor your AI Gateway activity.”

   proof ↗⚑ flag

   Kong AI Gateway~partialclaimed5/10

   Kong AI Gateway provides cost calculation, tracking across teams, and rate limiting plugins (AI Rate Limiting Advanced) which can enforce request/token limits per consumer, but evidence shows rate limiting and cost tracking as separate capabilities rather than a unified hard 'budget/spend limit' enforcement tied directly to cost thresholds per key/team/user. missing for 10: explicit documentation of setting a dollar-based spend cap or budget that auto-blocks requests once exceeded, per-key/team/user budget configuration UI or API, and independent confirmation of budget enforcement in production.

   • [claimed-docs] “AI Rate Limiting Advanced - Plugin | Kong Docs”
   • [claimed-docs] “AI Gateway calculates that figure for every request to a large language model (LLM) provider, matching what the provider actually bills.”
   • [claimed-docs] “AI Gateway can proxy requests from AI command-line tools to LLM providers. This gives you centralized control over AI traffic: log all reque…”
   • [claimed-docs] “You can use these metrics to: Track LLM request latency and upstream provider processing time * Monitor token consumption ... * Calculate AI…”

   proof ↗⚑ flag

Spend tracking

1. platform engineerTrack spend per model, key, team, or user across all providers in one place

   weight 3 · round to Vercel AI Gateway
   Vercel AI Gateway✓fullclaimed8/10

   Docs show request/usage summaries broken down by project and API key, budgets settable per team/project/API key/team member, and detailed logs filterable by model/provider/status with CSV/JSON export, directly matching the story's cost-tracking dimensions. Missing for 10: explicit per-user (not just per-team-member/API key) breakdowns and independent third-party confirmation of the dashboard's granularity in practice.

   • [claimed-docs] “Spend monitoring. Monitor your spending across different providers”
   • [claimed-docs] “The Requests section displays summaries by project, API key, and a detailed log of all requests.”
   • [claimed-docs] “To cap how much your team, a project, an API key, or a team member can spend, set [budgets]”
   • [claimed-docs] “open the Logs page, where you can search by request ID, filter by model, provider, or status code, follow requests live, and open one reques…”
   • [claimed-docs] “The Usage section displays four metrics to help you monitor your AI Gateway activity.”

   proof ↗⚑ flag

   Kong AI Gateway~partialclaimed7/10

   Kong AI Gateway computes exact per-request LLM costs matching provider billing, logs cost metrics with token/model details, exports cost-related OTel metrics, and documentation explicitly states cost tracking 'across teams'; since all providers route through the same gateway, aggregation across providers is plausible. However, evidence never shows a consolidated dashboard or breakdown specifically by model, API key, or individual user — only 'teams' is explicitly named as a tracked dimension. Missing for 10: explicit per-user and per-API-key cost attribution/dashboard, and a documented single-pane cross-provider spend view (vs. raw logs/metrics that would need external aggregation).

   • [claimed-docs] “AI Gateway calculates that figure for every request to a large language model (LLM) provider, matching what the provider actually bills.”
   • [claimed-docs] “AI Gateway logs capture detailed information about the request and response payloads, token usage, model details, latency, and cost metrics.”
   • [claimed-docs] “You can use these metrics to: Track LLM request latency and upstream provider processing time * Monitor token consumption ... * Calculate AI…”
   • [claimed-docs] “AI Gateway can proxy requests from AI command-line tools to LLM providers. This gives you centralized control over AI traffic: log all reque…”
   • [claimed-docs] “AI Gateway can export OpenTelemetry (OTLP) metrics for generative AI, MCP, and A2A traffic through an OpenTelemetry AI Policy.”

   proof ↗⚑ flag

Byok

1. developerBring my own provider API keys and have the gateway use them for my traffic

   weight 2 · round to Kong AI Gateway
   Vercel AI Gateway!disputedcontradicted5/10

   Vercel's docs clearly describe BYOK support — pass provider credentials per-request via providerOptions.gateway.byok, with no markup on tokens, and even fallback to system credentials on failure — suggesting bring-your-own-key traffic should run on the developer's own credentials (docs-6, docs-13, docs-19, docs-27). However, a concrete community report describes BYOK requests being blocked by Vercel's account balance reaching $0 even when the user's own provider key has available usage, directly contradicting the expectation that BYOK traffic is unaffected by platform account balance (comm-1). missing for 10: independent confirmation resolving the balance-dependency complaint, clearer documentation on how billing/balance interacts with BYOK requests.

   • [claimed-docs] “No markup on tokens. Tokens cost the same as they would from the provider directly, with zero markup, including with [Bring Your Own Key (BY…”
   • [claimed-docs] “If a query using your credentials fails, AI Gateway will retry the query with its system credentials to improve service availability.”
   • [claimed-docs] “No markup on tokens. Tokens cost the same as they would from the provider directly, with zero markup, including with Bring Your Own Key (BYO…”
   • [claimed-docs] “you can pass provider credentials on a per-request basis using the byok option in providerOptions.gateway. This is useful when you need to u…”
   • [community] “If I bring my own Anthropic/OpenAI key and have usage available, why does Vercel's account balance determine whether my request executes? (V…”

   proof ↗⚑ flag

   Kong AI Gateway~partialclaimed6/10

   Docs show AI Model Providers are configured with credentials, and an AI Vault entity lets AI Model Providers reference externally-stored secrets (AWS Secrets Manager, HashiCorp Vault, env vars) rather than storing keys as literal values, and Azure-specific managed identity auth is also supported — implying developers can supply and manage their own provider keys/secrets. However, no explicit doc snippet shows the exact 'paste your own API key per provider' config example or per-request key override behavior. Missing for 10: an explicit example of configuring a provider with a literal/bring-your-own API key, and confirmation of per-consumer or per-request key overrides.

   • [claimed-docs] “The core of AI Gateway is the ability to serve AI Models from various AI Model Providers via a provider-agnostic API.”
   • [claimed-docs] “Yes, if AI Gateway is running on Azure, you can configure an AI Model Provider to detect the designated Managed Identity or User-Assigned Id…”
   • [claimed-docs] “An AI Vault entity lets you register an external secret backend (AWS Secrets Manager, HashiCorp Vault, environment variables, or others) so …”
   • [claimed-docs] “Authenticate enterprise users through an existing identity provider (Okta, Azure AD, Google, or any OIDC-compliant IdP) without managing key…”

   proof ↗⚑ flag

Programmatic admin

1. ai-native userProvision gateways, keys, and budgets programmatically through an admin API

   weight 3 · round to Kong AI Gateway
   Vercel AI Gateway—none0/10

   Docs describe setting budgets, API keys, and monitoring spend through the Vercel dashboard/CLI, and a general Vercel OpenAPI spec exists, but there is no evidence of a documented admin API specifically for programmatically provisioning AI Gateway resources, keys, or budgets. Missing for 10: explicit admin/API endpoints for creating gateways, generating keys, or setting budgets programmatically, and any docs or examples showing this workflow.

   • [claimed-docs] “To cap how much your team, a project, an API key, or a team member can spend, set [budgets]”
   • [claimed-docs] “The Requests section displays summaries by project, API key, and a detailed log of all requests.”
   • [probe] “PROBE openapi: HTTP 200 at https://vercel.com/openapi.json — contains "openapi" key”

   proof ↗⚑ flag

   Kong AI Gateway~partialclaimed5/10

   Kong exposes a Konnect API for listing/managing AI Gateways (list-ai-gateways) and entity-based configuration (AI Auth Strategy, AI Vault) that implies programmatic provisioning of gateways and credentials, plus a kongctl CLI for gateway configuration/migration. However, there is no explicit documentation of an API or CLI for creating/managing spend budgets or quotas, and no end-to-end example of provisioning gateway+keys+budget via API. Missing for 10: explicit budget/quota provisioning API, a full API reference for creating AI Auth Strategies/keys programmatically, and independent confirmation of the admin API's completeness for this workflow.

   • [claimed-docs] “list-ai-gateways summary: List AI Gateways description: Returns a list of AI Gateways in the organization.”
   • [claimed-docs] “An AI Vault entity lets you register an external secret backend (AWS Secrets Manager, HashiCorp Vault, environment variables, or others) so …”
   • [claimed-docs] “Authenticate enterprise users through an existing identity provider (Okta, Azure AD, Google, or any OIDC-compliant IdP) without managing key…”
   • [claimed-docs] “walks you through migrating an existing configuration using the kongctl AI Gateway conversion extension”
   • [claimed-docs] “AI Gateway calculates that figure for every request to a large language model (LLM) provider, matching what the provider actually bills.”

   proof ↗⚑ flag

Virtual keys

1. platform engineerMint gateway-managed keys for teams and apps without exposing raw provider keys

   weight 3 · round to Vercel AI Gateway
   Vercel AI Gateway✓fullclaimed7/10

   Docs show a single Gateway-managed API key (or OIDC token) replaces direct provider keys, with per-API-key usage tracking and budget caps that imply teams/apps can have distinct managed keys, and BYOK/system-credential fallback keeps raw provider secrets out of app code. Missing for 10: explicit dashboard walkthrough for creating/rotating multiple named keys per team or app, and documentation of per-key role/permission scoping.

   • [claimed-docs] “One key, hundreds of models. Access models from multiple providers with a single API key”
   • [claimed-docs] “The Requests section displays summaries by project, API key, and a detailed log of all requests.”
   • [claimed-docs] “To cap how much your team, a project, an API key, or a team member can spend, set [budgets]”
   • [claimed-docs] “If a query using your credentials fails, AI Gateway will retry the query with its system credentials to improve service availability.”
   • [claimed-docs] “For applications deployed on Vercel, OIDC tokens are automatically available as `VERCEL_OIDC_TOKEN`. No secrets to manage, no keys to rotate…”
   • [claimed-docs] “you can pass provider credentials on a per-request basis using the byok option in providerOptions.gateway. This is useful when you need to u…”

   proof ↗⚑ flag

   Kong AI Gateway~partialclaimed6/10

   Kong AI Gateway's AI Vault lets provider keys be stored in external secret backends and referenced by AI Model Providers/Agents instead of literal values, and AI Auth Strategy lets client apps authenticate via an IdP or Kong-managed auth without handling provider keys directly — both point toward the platform-engineer being able to shield raw provider keys from consumers. However, the evidence never explicitly describes a workflow for minting distinct gateway-managed keys per team/app, rotating them, or a consumer/key-auth flow tied to AI traffic. Missing for 10: explicit documentation of issuing/rotating per-team or per-app gateway-managed API keys, and any admin UI/API walkthrough for that key-minting process.

   • [claimed-docs] “An AI Vault entity lets you register an external secret backend (AWS Secrets Manager, HashiCorp Vault, environment variables, or others) so …”
   • [claimed-docs] “Authenticate enterprise users through an existing identity provider (Okta, Azure AD, Google, or any OIDC-compliant IdP) without managing key…”
   • [claimed-docs] “Reference an AI Auth Strategy on your AI Agent entities to authenticate A2A clients, and attach the Request Size Limiting Policy to limit re…”

   proof ↗⚑ flag

Integrations

1. developerExport gateway logs and traces to my own observability stack

   weight 1 · round to Kong AI Gateway
   Vercel AI Gateway~partialclaimed5/10

   The gateway provides a Logs UI with filtering and CSV/JSON export of request logs, which supports manual export of logs, but there's no evidence of native integration with external observability stacks (e.g., OpenTelemetry export, webhook/streaming to Datadog/Grafana, or trace export in standard formats like OTLP). missing for 10: OpenTelemetry/OTLP trace export, native integrations with third-party observability platforms, automated/streaming log export (vs manual CSV/JSON download), independent confirmation of export usability.

   • [claimed-docs] “open the Logs page, where you can search by request ID, filter by model, provider, or status code, follow requests live, and open one reques…”
   • [claimed-docs] “The Requests section displays summaries by project, API key, and a detailed log of all requests.”
   • [claimed-docs] “The Usage section displays four metrics to help you monitor your AI Gateway activity.”

   proof ↗⚑ flag

   Kong AI Gateway✓fullclaimed8/10

   Kong AI Gateway documents exporting OpenTelemetry (OTLP) metrics for generative AI, MCP, and A2A traffic via an OpenTelemetry AI Policy, plus detailed audit logs capturing request/response payloads, token usage, latency, and cost — enabling integration with a developer's own observability stack. Missing for 10: explicit trace export (spans/tracing) documentation and independent/hands-on confirmation of exporting to third-party backends like Datadog or Grafana.

   • [claimed-docs] “AI Gateway can export OpenTelemetry (OTLP) metrics for generative AI, MCP, and A2A traffic through an OpenTelemetry AI Policy.”
   • [claimed-docs] “You can use these metrics to: Track LLM request latency and upstream provider processing time * Monitor token consumption ... * Calculate AI…”
   • [claimed-docs] “AI Gateway logs capture detailed information about the request and response payloads, token usage, model details, latency, and cost metrics.”

   proof ↗⚑ flag

Logs

1. platform engineerInspect logged requests and responses with latency, token counts, and cost attached

   weight 3 · round drawn
   Vercel AI Gateway✓fullclaimed8/10

   Docs describe a Logs page with per-request detail, search by request ID, filtering by model/provider/status, and export as CSV/JSON, plus a Requests section with summaries by project/API key and a Usage section with metrics — covering latency, tokens, cost, and routing detail. Missing for 10: independent/hands-on confirmation that latency and cost are explicitly visible per logged request (only vendor docs cited), and no third-party corroboration of the observability UI.

   • [claimed-docs] “The Requests section displays summaries by project, API key, and a detailed log of all requests.”
   • [claimed-docs] “open the Logs page, where you can search by request ID, filter by model, provider, or status code, follow requests live, and open one reques…”
   • [claimed-docs] “The Usage section displays four metrics to help you monitor your AI Gateway activity.”
   • [claimed-docs] “Spend monitoring. Monitor your spending across different providers”

   proof ↗⚑ flag

   Kong AI Gateway✓fullclaimed8/10

   Kong AI Gateway docs explicitly describe audit logs capturing request/response payloads, token usage, model details, latency, and cost metrics, plus usage analytics dashboards and OTel metrics for latency, tokens, and cost. missing for 10: independent/hands-on corroboration of the logging UI in practice and detail on log retention/query interface.

   • [claimed-docs] “See usage analytics for requests, tokens, errors, and latency”
   • [claimed-docs] “You can use these metrics to: Track LLM request latency and upstream provider processing time * Monitor token consumption ... * Calculate AI…”
   • [claimed-docs] “AI Gateway logs capture detailed information about the request and response payloads, token usage, model details, latency, and cost metrics.”
   • [claimed-docs] “AI Gateway calculates that figure for every request to a large language model (LLM) provider, matching what the provider actually bills.”
   • [claimed-docs] “AI Gateway can export OpenTelemetry (OTLP) metrics for generative AI, MCP, and A2A traffic through an OpenTelemetry AI Policy.”

   proof ↗⚑ flag

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

   weight 2 · round to Vercel AI Gateway
   Vercel AI Gateway~partialprobed6/10

   The gateway is largely API-first: model access, provider routing, caching, BYOK, and even a keyless public models endpoint are confirmed to work programmatically (docs-1,2,9,15,25,27; probe-4). However, budgets, spend caps, and the detailed Requests/Logs dashboard are described only as UI features (docs-5,12,16,23,30) without documented API endpoints to set budgets or pull logs programmatically. Missing for 10: budgets/spend-cap management API, log/usage export API parity, team/key management API parity.

   • [claimed-docs] “One key, hundreds of models. Access models from multiple providers with a single API key”
   • [claimed-docs] “Unified API. Switch between providers and models with minimal code changes”
   • [claimed-docs] “You can use `order`, `only`, and `sort` in `providerOptions.gateway` to control which providers handle your requests, in what order, and how…”
   • [claimed-docs] “This endpoint requires no authentication and returns detailed information including model IDs, context windows, and pricing.”
   • [claimed-docs] “Query the models endpoint directly to get a JSON list of all available models with pricing and capabilities”
   • [claimed-docs] “you can pass provider credentials on a per-request basis using the byok option in providerOptions.gateway. This is useful when you need to u…”
   • [claimed-docs] “Spend monitoring. Monitor your spending across different providers”
   • [claimed-docs] “To cap how much your team, a project, an API key, or a team member can spend, set [budgets]”
   • [claimed-docs] “open the Logs page, where you can search by request ID, filter by model, provider, or status code, follow requests live, and open one reques…”
   • [probe] “PROBE keyless-endpoint: GET https://ai-gateway.vercel.sh/v1/models returned HTTP 200 application/json with an OpenAI-compatible {"object":"l…”

   proof ↗⚑ flag

   Kong AI Gateway~partialprobed5/10

   Kong exposes AI Gateway configuration via the Konnect API (e.g. list-ai-gateways endpoint) and the kongctl CLI, and docs explicitly note that 'the same capabilities' configurable via plugins can be set on Services/Routes rather than only the UI, implying strong API-driven configuration. However there is no explicit statement of full UI/API parity, and probes for a discoverable OpenAPI spec returned 404s, leaving completeness unverified. Missing for 10: an explicit parity statement or comprehensive OpenAPI spec confirming every UI action (e.g. dashboards, analytics views) is also API-accessible, and independent confirmation of no UI-only features.

   • [claimed-docs] “list-ai-gateways summary: List AI Gateways description: Returns a list of AI Gateways in the organization.”
   • [claimed-docs] “This page shows you how to configure the same capabilities with AI plugins on Services and Routes.”
   • [probe] “official CLI documented at https://developer.konghq.com/kongctl/”
   • [probe] “PROBE openapi: all candidate paths 404 (https://developer.konghq.com/openapi.json, https://developer.konghq.com/swagger.json, https://develo…”

   proof ↗⚑ flag

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

   weight 3 · round to Vercel AI Gateway
   Vercel AI Gateway~partialcommunity5/10

   Vercel documents that the Logs page lets users export request/usage data as CSV or JSON, and its OpenAI-compatible API means you can point clients elsewhere with minimal rewrite, reducing lock-in. However, there's no evidence of a comprehensive account/data export (billing history, configs, keys) beyond request logs, and BYOK behavior tied to Vercel balance (comm-1) suggests some platform dependency remains. Missing for 10: full account/config data export, evidence of exporting all historical usage/billing data, and confirmation that BYOK/API keys are fully portable without gateway dependency.

   • [claimed-docs] “open the Logs page, where you can search by request ID, filter by model, provider, or status code, follow requests live, and open one reques…”
   • [claimed-docs] “You can use existing OpenAI client libraries, switch to AI Gateway with a URL change, and keep your current tools and workflows without code…”
   • [claimed-docs] “Point your existing OpenAI or Anthropic SDK at AI Gateway. Same calls, no rewrites.”
   • [claimed-docs] “You can use the AI Gateway's Chat Completions API with existing tools and libraries like the OpenAI client libraries and AI SDK. Point your …”
   • [community] “If I bring my own Anthropic/OpenAI key and have usage available, why does Vercel's account balance determine whether my request executes? (V…”

   proof ↗⚑ flag

   Kong AI Gateway—none0/10

   Kong AI Gateway/Konnect is a SaaS-hosted control plane with configuration, logs, and analytics data, so data portability/export-and-leave is a fair axis to ask, but the evidence pack contains no mention of a bulk data export feature, open-format export of configs/logs/analytics, or a documented migration-out path in open standards. Evidence only covers observability (metrics, logs) and a CLI conversion extension for migrating configs between versions, not exporting all user data to leave the platform.

   • [claimed-docs] “walks you through migrating an existing configuration using the kongctl AI Gateway conversion extension”
   • [claimed-docs] “You can use these metrics to: Track LLM request latency and upstream provider processing time * Monitor token consumption ... * Calculate AI…”
   • [claimed-docs] “AI Gateway logs capture detailed information about the request and response payloads, token usage, model details, latency, and cost metrics.”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   Vercel AI Gateway—none0/10

   Vercel AI Gateway is a closed, proprietary hosted service; no evidence in the pack points to any open-source repository or open license for its source code — all evidence is documentation and API endpoints, not source availability.

   ⚑ flag

   Kong AI Gateway—none0/10

   Kong AI Gateway is a commercial/enterprise product built on Kong Gateway; the evidence pack contains only product documentation, API references, and probes for docs/CLI—no mention of source code availability or an open-source license for AI Gateway itself. Kong Gateway OSS is a separate product; nothing here confirms AI Gateway's source is published under an open license.

   ⚑ flag

4. ai-native userSelf-host the core product

   weight 3 · round to Kong AI Gateway
   Vercel AI Gateway—none0/10

   Vercel AI Gateway is a hosted cloud service with no evidence of a self-hostable core product or open-source release; nothing in the docs suggests an on-prem or self-managed deployment option.

   ⚑ flag

   Kong AI Gateway~partialclaimed5/10

   Docs reference configuring AI Gateway capabilities 'on-prem' via plugins on Services and Routes, and a quickstart script to spin up a demo instance, implying self-hosted deployment is possible. However, there's no detailed self-hosting guide, licensing/open-source clarification, or infrastructure requirements documented in the evidence pack. Missing for 10: explicit self-hosting/installation documentation, open-source license details, infrastructure/deployment guides beyond a demo quickstart.

   • [claimed-docs] “This page shows you how to configure the same capabilities with AI plugins on Services and Routes.”
   • [claimed-docs] “You can use the quickstart script to get a demo instance of AI Gateway running almost instantly.”

   proof ↗⚑ flag

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

   weight 2 · round to Kong AI Gateway
   Vercel AI Gateway—none0/10

   No evidence of region/data-residency controls or storage location choices in the AI Gateway docs; the pack covers routing, failover, pricing, and observability but nothing about where data is stored or processed. Missing for 10: any mention of regional data residency options, storage location controls, or compliance certifications addressing data locality.

   ⚑ flag

   Kong AI Gateway~partialclaimed3/10

   Kong AI Gateway documentation mentions an on-prem/self-hosted configuration option (configure-on-prem.md), which implies some ability to control where the gateway (and thus data in transit) runs, but there is no explicit documentation of region selection, data residency guarantees, or storage location controls for logs/analytics/cost data. missing for 10: explicit region/residency configuration options, documented data storage location controls, compliance statements about data residency.

   • [claimed-docs] “This page shows you how to configure the same capabilities with AI plugins on Services and Routes.”
   • [claimed-docs] “AI Gateway logs capture detailed information about the request and response payloads, token usage, model details, latency, and cost metrics.”

   proof ↗⚑ flag

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

   weight 3 · round drawn
   Vercel AI Gateway—none0/10

   No evidence in the pack addresses data-training opt-out policies, zero-retention agreements, or any explicit statement that prompts/outputs are excluded from model training; the docs focus on routing, pricing, and observability instead. Missing for 10: any privacy policy or training-opt-out statement, zero-data-retention terms, provider-level data-use guarantees.

   ⚑ flag

   Kong AI Gateway—none0/10

   Kong AI Gateway is an enterprise routing/governance layer for LLM traffic, so control over how provider data is used is a fair question, but the evidence pack contains no mention of training-data opt-out, zero-retention guarantees, or contractual terms preventing model providers from using proxied data for training — only general DLP/PII redaction and content-safety features are documented, which don't address this specific claim.

   ⚑ flag

3. ai-native userControl data retention and deletion

   weight 2 · round drawn
   Vercel AI Gateway—none0/10

   No evidence in the pack addresses data retention policies, log/data deletion controls, or retention configuration for requests/prompts processed through AI Gateway; observability docs mention logging/exporting requests but nothing about retention duration or deletion mechanisms.

   ⚑ flag

   Kong AI Gateway—none0/10

   Evidence shows Kong AI Gateway logs request/response payloads, token usage, and costs (docs-10), and offers DLP/PII removal (docs-20) and semantic caching (docs-11), but none of the evidence describes configurable data retention periods or deletion/purge controls for logs, caches, or audit trails. Missing for 10: explicit retention policy settings, log/cache deletion or expiry controls, data subject deletion workflow.

   • [claimed-docs] “AI Gateway logs capture detailed information about the request and response payloads, token usage, model details, latency, and cost metrics.”
   • [claimed-docs] “Apply safety and DLP policies to prevent toxic content and remove personally identifiable information.”
   • [claimed-docs] “AI Semantic Cache - Plugin | Kong Docs”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   Vercel AI Gateway—none0/10

   No evidence of any telemetry opt-out or privacy/data-collection controls; the gateway's observability features are about logging usage for the account owner, not about disabling tracking. Missing for 10: any documented telemetry opt-out setting, privacy policy on data retention/training use, or way to disable request logging.

   ⚑ flag

   Kong AI Gateway—none0/10

   No evidence pack items mention any telemetry opt-out, privacy settings, or usage-tracking controls for Kong AI Gateway itself; all evidence concerns the product's own traffic logging/analytics features rather than user opt-out of vendor telemetry.

   ⚑ flag

Fallbacks

1. platform engineerConfigure automatic fallback to another model or provider when one fails

   weight 3 · round to Vercel AI Gateway
   Vercel AI Gateway✓fullclaimed9/10

   Docs explicitly describe automatic retries to other providers on failure, plus fine-grained control via `order`, `only`, and `sort` in providerOptions.gateway, per-provider timeouts for fast failover, and even BYOK-to-system-credential fallback — directly matching the fallback/routing-resilience story. This is well-documented first-party capability with concrete configuration options, though missing for 10: independent hands-on verification that fallback behaves correctly in production scenarios.

   • [claimed-docs] “High reliability. Automatically retries requests to other providers if one fails”
   • [claimed-docs] “You can use `order`, `only`, and `sort` in `providerOptions.gateway` to control which providers handle your requests, in what order, and how…”
   • [claimed-docs] “You can set per-provider timeouts to trigger fast failover when a provider is slow to respond.”
   • [claimed-docs] “If a query using your credentials fails, AI Gateway will retry the query with its system credentials to improve service availability.”
   • [claimed-docs] “You can use order, only, and sort in providerOptions.gateway to control which providers handle your requests, in what order, and how they ar…”
   • [claimed-docs] “AI Gateway routes requests, manages fallbacks and budgets, monitors usage, and connects supported coding agents.”

   proof ↗⚑ flag

   Kong AI Gateway~partialclaimed5/10

   Kong AI Gateway documents load balancing across multiple LLM models/providers, which implies routing resilience, but the evidence never explicitly describes an automatic fallback mechanism triggered by provider failure (e.g., retry-on-error, circuit breaking, or failover behavior). Missing for 10: explicit fallback-on-failure documentation, failure detection/health-check mechanism, and any hands-on confirmation that fallback actually occurs when a provider errors out.

   • [claimed-docs] “AI Gateway provides load balancing capabilities to distribute requests across multiple LLM models.”
   • [claimed-docs] “The core of AI Gateway is the ability to serve AI Models from various AI Model Providers via a provider-agnostic API.”

   proof ↗⚑ flag

Load balancing

1. platform engineerLoad-balance traffic across providers, deployments, or keys by weight, latency, or cost

   weight 2 · round drawn
   Vercel AI Gateway~partialclaimed6/10

   Docs confirm ordering/sorting by performance or cost metric (`order`, `only`, `sort`), per-provider timeouts for fast failover, and automatic retries/fallbacks to other providers — covering latency- and cost-based routing and failover. However, explicit weight-based load balancing across providers/deployments/keys is not documented, and there's no evidence of key-level load balancing beyond BYOK fallback to system credentials. missing for 10: explicit weighted load-balancing configuration, per-key routing/balancing control, independent verification of routing behavior under load.

   • [claimed-docs] “You can use `order`, `only`, and `sort` in `providerOptions.gateway` to control which providers handle your requests, in what order, and how…”
   • [claimed-docs] “You can set per-provider timeouts to trigger fast failover when a provider is slow to respond.”
   • [claimed-docs] “You can also use `sort` to rank providers by a performance or cost metric. The gateway sorts providers by the chosen metric and tries them i…”
   • [claimed-docs] “High reliability. Automatically retries requests to other providers if one fails”
   • [claimed-docs] “If a query using your credentials fails, AI Gateway will retry the query with its system credentials to improve service availability.”

   proof ↗⚑ flag

   Kong AI Gateway~partialclaimed6/10

   Kong AI Gateway explicitly documents load-balancing across multiple LLM models/providers (docs-17), and it also tracks and calculates per-request LLM costs (docs-18) and latency metrics (docs-9), which are the raw signals needed for cost- or latency-aware routing. However, the evidence pack never confirms the specific load-balancing algorithms (e.g., weighted, latency-based, cost-based) are selectable options, so it's unclear whether these criteria can actually be used as balancing strategies. Missing for 10: explicit documentation of weight-based, latency-based, and cost-based load-balancing algorithms/configuration options, and independent confirmation these modes work as described.

   • [claimed-docs] “AI Gateway provides load balancing capabilities to distribute requests across multiple LLM models.”
   • [claimed-docs] “AI Gateway calculates that figure for every request to a large language model (LLM) provider, matching what the provider actually bills.”
   • [claimed-docs] “You can use these metrics to: Track LLM request latency and upstream provider processing time * Monitor token consumption ... * Calculate AI…”

   proof ↗⚑ flag

Policy routing

1. ai-native userMy agent can switch models mid-task by policy — cost, capability, or availability — through gateway routing rules

   weight 3 · round to Vercel AI Gateway
   Vercel AI Gateway~partialcommunity7/10

   Docs show routing rules via providerOptions.gateway (order, only, sort, timeouts) that let requests fail over across providers by availability/latency, plus cost/spend visibility and budgets, which supports policy-driven switching. However, there's no explicit evidence of dynamic mid-task capability-based switching or agent-driven runtime policy changes beyond static config, and community evidence flags BYOK requests being blocked once account balance hits $0, undermining pure availability-based routing in some cases. missing for 10: explicit support for capability-based (not just cost/availability) routing rules, evidence of an agent dynamically changing models mid-task at runtime, and independent confirmation that failover/ordering works reliably in production.

   • [claimed-docs] “You can use `order`, `only`, and `sort` in `providerOptions.gateway` to control which providers handle your requests, in what order, and how…”
   • [claimed-docs] “You can set per-provider timeouts to trigger fast failover when a provider is slow to respond.”
   • [claimed-docs] “You can also use `sort` to rank providers by a performance or cost metric. The gateway sorts providers by the chosen metric and tries them i…”
   • [claimed-docs] “High reliability. Automatically retries requests to other providers if one fails”
   • [claimed-docs] “To cap how much your team, a project, an API key, or a team member can spend, set [budgets]”
   • [community] “If I bring my own Anthropic/OpenAI key and have usage available, why does Vercel's account balance determine whether my request executes? (V…”

   proof ↗⚑ flag

   Kong AI Gateway~partialclaimed5/10

   Kong AI Gateway documents load balancing across multiple LLM models (covering availability-based routing) and per-request cost calculation, which together imply some policy-driven model selection, but there is no explicit documentation of switching models based on capability or triggering mid-task, dynamic policy switches tied to cost or availability thresholds. Missing for 10: explicit cost/capability-based routing policy documentation, mid-task dynamic model switching evidence, and independent/hands-on corroboration of this routing-resilience behavior.

   • [claimed-docs] “AI Gateway provides load balancing capabilities to distribute requests across multiple LLM models.”
   • [claimed-docs] “AI Gateway calculates that figure for every request to a large language model (LLM) provider, matching what the provider actually bills.”
   • [claimed-docs] “AI Gateway can perform intelligent request routing, caching, and content filtering using semantic similarity queries.”

   proof ↗⚑ flag

Rate limits

1. platform engineerSmooth provider rate limits by spreading traffic across keys and queuing or throttling requests

   weight 2 · round to Kong AI Gateway
   Vercel AI Gateway~partialclaimed4/10

   The gateway supports failover/retry across providers, per-provider timeouts for fast failover, and ordering/sorting of providers (docs-3, docs-9, docs-10, docs-13, docs-20, docs-29), which can mitigate a single provider's rate limits by shifting traffic elsewhere. However, there's no documented mechanism for spreading load across multiple API keys for the same provider, nor any explicit request queuing/throttling feature to smooth bursts against rate limits. Missing for 10: multi-key load balancing, explicit request queuing/throttling controls, and any rate-limit-specific documentation or independent verification.

   • [claimed-docs] “High reliability. Automatically retries requests to other providers if one fails”
   • [claimed-docs] “You can use `order`, `only`, and `sort` in `providerOptions.gateway` to control which providers handle your requests, in what order, and how…”
   • [claimed-docs] “You can set per-provider timeouts to trigger fast failover when a provider is slow to respond.”
   • [claimed-docs] “If a query using your credentials fails, AI Gateway will retry the query with its system credentials to improve service availability.”
   • [claimed-docs] “You can also use `sort` to rank providers by a performance or cost metric. The gateway sorts providers by the chosen metric and tries them i…”

   proof ↗⚑ flag

   Kong AI Gateway~partialclaimed6/10

   Kong AI Gateway documents load balancing across multiple LLM models/providers and a dedicated 'AI Rate Limiting Advanced' plugin for enforcing rate limits, which together support spreading traffic and throttling. However, there is no explicit documentation of spreading traffic across multiple API keys for the same provider or of request queuing behavior when limits are hit. Missing for 10: explicit key-rotation/multi-key load balancing, request queuing mechanics, and any hands-on/independent verification of these features working together.

   • [claimed-docs] “AI Gateway provides load balancing capabilities to distribute requests across multiple LLM models.”
   • [claimed-docs] “AI Rate Limiting Advanced - Plugin | Kong Docs”
   • [claimed-docs] “AI Gateway calculates that figure for every request to a large language model (LLM) provider, matching what the provider actually bills.”

   proof ↗⚑ flag

Retries

1. platform engineerSet automatic retry policies for transient provider errors

   weight 2 · round to Vercel AI Gateway
   Vercel AI Gateway✓fullclaimed8/10

   Docs explicitly describe automatic failover/retry to other providers on failure (docs-3), configurable provider ordering/fallback with `order`, `only`, `sort` (docs-9, docs-29), per-provider timeouts to trigger fast failover (docs-10), and even automatic retry with system credentials when BYOK fails (docs-13). This directly covers automatic retry policies for transient provider errors. Missing for 10: no independent/hands-on confirmation of retry behavior in production, and no explicit documentation of configurable retry counts/backoff intervals distinct from provider ordering.

   • [claimed-docs] “High reliability. Automatically retries requests to other providers if one fails”
   • [claimed-docs] “You can use `order`, `only`, and `sort` in `providerOptions.gateway` to control which providers handle your requests, in what order, and how…”
   • [claimed-docs] “You can set per-provider timeouts to trigger fast failover when a provider is slow to respond.”
   • [claimed-docs] “If a query using your credentials fails, AI Gateway will retry the query with its system credentials to improve service availability.”
   • [claimed-docs] “You can also use `sort` to rank providers by a performance or cost metric. The gateway sorts providers by the chosen metric and tries them i…”

   proof ↗⚑ flag

   Kong AI Gateway~partialclaimed4/10

   Kong AI Gateway documents load balancing across multiple LLM models/providers (docs-17), which implies some failover capability, but the evidence never explicitly describes configurable automatic retry policies for transient provider errors (e.g., retry counts, backoff, error-code triggers). Missing for 10: explicit retry-policy documentation, retry configuration options, and evidence of behavior on transient errors specifically.

   • [claimed-docs] “AI Gateway provides load balancing capabilities to distribute requests across multiple LLM models.”
   • [claimed-docs] “The core of AI Gateway is the ability to serve AI Models from various AI Model Providers via a provider-agnostic API.”

   proof ↗⚑ flag

Streaming

1. developerStream token-by-token responses through the gateway from any provider

   weight 3 · round to Kong AI Gateway
   Vercel AI Gateway~partialclaimed5/10

   The gateway advertises full OpenAI/Anthropic SDK compatibility and AI SDK integration (docs-7, docs-18, docs-28, docs-31), and since streaming is a standard capability of those client libraries, it is implicitly supported when pointing existing clients at the gateway's base URL. However, no evidence explicitly documents streaming/token-by-token responses, SSE support, or shows this working across all providers. Missing for 10: explicit docs on streaming endpoints/parameters, hands-on confirmation of token-by-token streaming across multiple providers, and any independent verification.

   • [claimed-docs] “You can use existing OpenAI client libraries, switch to AI Gateway with a URL change, and keep your current tools and workflows without code…”
   • [claimed-docs] “Point your existing OpenAI or Anthropic SDK at AI Gateway. Same calls, no rewrites.”
   • [claimed-docs] “You can use the AI Gateway's Chat Completions API with existing tools and libraries like the OpenAI client libraries and AI SDK. Point your …”
   • [claimed-docs] “Move existing OpenAI, Anthropic, and AI SDK integrations to AI Gateway with a base URL swap.”

   proof ↗⚑ flag

   Kong AI Gateway✓fullclaimed8/10

   Kong AI Gateway has a dedicated streaming.md doc confirming token-by-token streaming from models is supported and rendered in real time, layered on top of its provider-agnostic API supporting multiple LLM providers. Missing for 10: independent/hands-on confirmation of streaming behavior across every provider and any details on streaming with proxy-added policies (e.g., guardrails) applied mid-stream.

   • [claimed-docs] “most models can stream each word (or sets of words and tokens) back to the client. This allows the chat response to be rendered in real time…”
   • [claimed-docs] “The core of AI Gateway is the ability to serve AI Models from various AI Model Providers via a provider-agnostic API.”

   proof ↗⚑ flag

Tool calling

1. developerRequest structured JSON-schema outputs across providers

   weight 1 · round drawn
   Vercel AI Gateway—none0/10

   The evidence describes unified APIs, OpenAI-compatibility, provider routing, embeddings, and observability, but nowhere mentions structured JSON-schema/response_format output support or how it is normalized across different providers. Missing for 10: any documentation of JSON-schema/structured-output support, cross-provider schema normalization, or examples using response_format/json_schema.

   ⚑ flag

   Kong AI Gateway—none0/10

   The evidence pack covers provider-agnostic proxying, streaming, load balancing, cost tracking, and safety policies, but no document mentions structured/JSON-schema output enforcement or normalization across providers. This is a fair capability to expect from an AI gateway, but no evidence supports it.

   ⚑ flag

2. developerMake tool and function calls across different providers with a consistent schema

   weight 3 · round to Vercel AI Gateway
   Vercel AI Gateway✓fullprobed8/10

   Vercel AI Gateway supports a unified API/schema (OpenAI-compatible and AI SDK) that lets developers call tools/functions across many providers with consistent code, including an explicit ToolLoopAgent example with tool calling, provider routing/fallback, and no-rewrite compatibility with existing OpenAI/Anthropic clients. missing for 10: independent hands-on verification of cross-provider tool-call schema consistency (e.g., confirming tool-calling behaves identically across Anthropic/OpenAI/other models) and any documented edge cases or limitations in function-calling parity across providers.

   • [claimed-docs] “Unified API. Switch between providers and models with minimal code changes”
   • [claimed-docs] “You can use existing OpenAI client libraries, switch to AI Gateway with a URL change, and keep your current tools and workflows without code…”
   • [claimed-docs] “Point your existing OpenAI or Anthropic SDK at AI Gateway. Same calls, no rewrites.”
   • [claimed-docs] “const agent = new ToolLoopAgent({ model: 'anthropic/claude-sonnet-5', tools: { getWeather: tool({”
   • [claimed-docs] “You can use the AI Gateway's Chat Completions API with existing tools and libraries like the OpenAI client libraries and AI SDK. Point your …”
   • [probe] “PROBE keyless-endpoint: GET https://ai-gateway.vercel.sh/v1/models returned HTTP 200 application/json with an OpenAI-compatible {"object":"l…”

   proof ↗⚑ flag

   Kong AI Gateway~partialclaimed6/10

   Kong AI Gateway documents a provider-agnostic API for LLM traffic and an AI MCP Proxy/Server that converts REST APIs into MCP tools, suggesting a consistent tool-calling interface across providers and models. However, there is no explicit documentation showing normalization of function/tool-call schemas (e.g., translating OpenAI-style function calls to Anthropic tool-use format) for the same request across different providers. Missing for 10: explicit cross-provider function-call schema translation examples, hands-on/independent verification of consistent tool-call behavior across providers.

   • [claimed-docs] “The core of AI Gateway is the ability to serve AI Models from various AI Model Providers via a provider-agnostic API.”
   • [claimed-docs] “AI MCP Proxy bridges Kong-managed Services to the Model Context Protocol, converting REST APIs into MCP tools or fronting upstream MCP serve…”
   • [claimed-docs] “Turn any API into an MCP server using the AI MCP Server entity. This approach does not require an LLM and provides full control over product…”
   • [claimed-docs] “Govern LLM, MCP, and agent-to-agent (A2A) traffic with the same Kong AI Gateway.”

   proof ↗⚑ flag

Catalog

1. developerBrowse or query a catalog of available models with pricing and context-window metadata

   weight 2 · round to Vercel AI Gateway
   Vercel AI Gateway✓fullprobed9/10

   Docs explicitly describe a models endpoint returning model IDs, context windows, and pricing with no authentication required, and a probe independently confirms the live unauthenticated /v1/models endpoint returns a full JSON model catalog. This directly satisfies browsing/querying a model catalog with pricing and context-window metadata. Missing for 10: no evidence of a dedicated visual browsing UI/dashboard for the catalog beyond the API and docs page.

   • [claimed-docs] “This endpoint requires no authentication and returns detailed information including model IDs, context windows, and pricing.”
   • [claimed-docs] “Query the models endpoint directly to get a JSON list of all available models with pricing and capabilities”
   • [probe] “PROBE keyless-endpoint: GET https://ai-gateway.vercel.sh/v1/models returned HTTP 200 application/json with an OpenAI-compatible {"object":"l…”

   proof ↗⚑ flag

   Kong AI Gateway—none0/10

   Evidence shows Kong AI Gateway supports multi-provider routing and calculates per-request costs, but there is no documented catalog/endpoint for browsing or querying available models with pricing and context-window metadata — the closest evidence (model-cost-management, list-ai-gateways API) covers cost calculation per request and listing gateway instances, not a model metadata catalog.

   • [claimed-docs] “AI Gateway calculates that figure for every request to a large language model (LLM) provider, matching what the provider actually bills.”
   • [claimed-docs] “list-ai-gateways summary: List AI Gateways description: Returns a list of AI Gateways in the organization.”
   • [claimed-docs] “The core of AI Gateway is the ability to serve AI Models from various AI Model Providers via a provider-agnostic API.”

   proof ↗⚑ flag

Compatibility

1. developerPoint existing OpenAI-compatible code at the gateway by changing only the base URL and key

   weight 3 · round to Vercel AI Gateway
   Vercel AI Gateway✓fullprobed9/10

   Vercel explicitly documents OpenAI-compatible Chat Completions API where developers just swap the base URL and key with existing OpenAI SDKs, with no code rewrites, and a probe confirms the gateway serves an OpenAI-compatible /v1/models JSON response. Community evidence around BYOK balance behavior is unrelated to base-URL compatibility, so it doesn't dispute this. Missing for 10: independent third-party hands-on confirmation of a full migration beyond Vercel's own docs/probes.

   • [claimed-docs] “You can use existing OpenAI client libraries, switch to AI Gateway with a URL change, and keep your current tools and workflows without code…”
   • [claimed-docs] “Point your existing OpenAI or Anthropic SDK at AI Gateway. Same calls, no rewrites.”
   • [claimed-docs] “You can use the AI Gateway's Chat Completions API with existing tools and libraries like the OpenAI client libraries and AI SDK. Point your …”
   • [claimed-docs] “Move existing OpenAI, Anthropic, and AI SDK integrations to AI Gateway with a base URL swap.”
   • [probe] “PROBE keyless-endpoint: GET https://ai-gateway.vercel.sh/v1/models returned HTTP 200 application/json with an OpenAI-compatible {"object":"l…”

   proof ↗⚑ flag

   Kong AI Gateway~partialclaimed6/10

   Kong AI Gateway is documented as a provider-agnostic API layer that proxies LLM requests (chat/completions endpoints) with streaming support, which implies OpenAI-style compatibility, but no evidence explicitly confirms that existing OpenAI SDK code works unmodified by only swapping base URL and API key. missing for 10: explicit doc/example showing an OpenAI SDK client redirected via base_url with no code changes, and confirmation that request/response schemas exactly mirror OpenAI's format.

   • [claimed-docs] “The core of AI Gateway is the ability to serve AI Models from various AI Model Providers via a provider-agnostic API.”
   • [claimed-docs] “most models can stream each word (or sets of words and tokens) back to the client. This allows the chat response to be rendered in real time…”
   • [claimed-docs] “This page shows you how to configure the same capabilities with AI plugins on Services and Routes.”

   proof ↗⚑ flag

One endpoint

1. developerCall many model providers through one consistent API

   weight 3 · round to Vercel AI Gateway
   Vercel AI Gateway✓fullprobed9/10

   Extensive first-party docs describe a single unified API/key across hundreds of providers, OpenAI-compatible client support, and provider routing/fallback controls, and a live probe confirms the OpenAI-compatible /v1/models endpoint works unauthenticated. There is a minor community-reported caveat about BYOK requests being blocked when Vercel balance hits $0, which slightly limits the 'bring your own key' flow but doesn't contradict the core unified-API story. Missing for 10: independent hands-on developer confirmation of multi-provider switching in production beyond docs and one probe.

   • [claimed-docs] “One key, hundreds of models. Access models from multiple providers with a single API key”
   • [claimed-docs] “Unified API. Switch between providers and models with minimal code changes”
   • [claimed-docs] “You can use existing OpenAI client libraries, switch to AI Gateway with a URL change, and keep your current tools and workflows without code…”
   • [claimed-docs] “Point your existing OpenAI or Anthropic SDK at AI Gateway. Same calls, no rewrites.”
   • [claimed-docs] “You can use the AI Gateway's Chat Completions API with existing tools and libraries like the OpenAI client libraries and AI SDK. Point your …”
   • [probe] “PROBE keyless-endpoint: GET https://ai-gateway.vercel.sh/v1/models returned HTTP 200 application/json with an OpenAI-compatible {"object":"l…”
   • [community] “If I bring my own Anthropic/OpenAI key and have usage available, why does Vercel's account balance determine whether my request executes? (V…”

   proof ↗⚑ flag

   Kong AI Gateway✓fullclaimed8/10

   Kong AI Gateway explicitly provides a provider-agnostic API to route to many LLM providers, with load balancing and unified logging/observability across providers, matching the core unified-API story. Missing for 10: independent/hands-on developer corroboration of consistent request/response schema across providers, and no explicit list of exact supported providers in evidence.

   • [claimed-docs] “The core of AI Gateway is the ability to serve AI Models from various AI Model Providers via a provider-agnostic API.”
   • [claimed-docs] “AI Gateway provides load balancing capabilities to distribute requests across multiple LLM models.”
   • [claimed-docs] “You can use these metrics to: Track LLM request latency and upstream provider processing time * Monitor token consumption ... * Calculate AI…”
   • [claimed-docs] “AI Gateway calculates that figure for every request to a large language model (LLM) provider, matching what the provider actually bills.”

   proof ↗⚑ flag