How to evaluate an AI gateway with a proof-based scorecard

The best gateway evaluation is a reproducible harness, not a vendor feature count. Score the exact protocols, models, failures, evidence, data controls, and operating duties your system needs.

Published 2026-08-15 · Updated 2026-08-15 · KeepRouter Editorial · 10 minute read

AI gateway evaluation scorecard covering contract, evidence, failure, security, and operations — A useful evaluation closes each required workload with evidence, an owner, and a rollback condition.

Short answer: evaluate an AI gateway by sending a fixed corpus of your own production-shaped requests through it and requiring pass/fail artifacts for protocol fidelity, model eligibility, reliability, observability, cost attribution, data controls, and operations. A public feature matrix is useful for discovery, but it is not acceptance evidence.

Start by choosing one bounded workflow: for example, a server-side text endpoint with streaming and one idempotent tool. Remove secrets from captured requests, define structural and quality assertions, and keep the previous route available. Expand only after this slice passes.

Evidence scorecard

Dimension	Required artifact	Example failure condition
Protocol	Request/response fixtures for every consumed field	A field is dropped, renamed, or silently coerced
Model eligibility	Current catalog or API result plus scoped smoke evidence	Route exists but selected model rejects the endpoint
Streaming	Ordered event log including cancellation and partial failure	UI hangs or treats an error as successful completion
Tools	Full tool-call and tool-result transcript	Arguments change shape or a side effect is duplicated
Reliability	Retry/fallback policy and injected-failure results	Non-retryable errors repeat or fallback crosses an unapproved model
Observability	Correlated app, gateway, and billing records	A request cannot be traced to owner, model, outcome, and usage
Cost control	Key scope, output bound, attempt limit, and alert test	One job can create unbounded attempts or output
Data controls	Logging, retention, region, and redaction configuration	Sensitive content appears in logs contrary to policy
Operations	Deployment, upgrade, incident, and rollback runbooks	Only one person can restore service

Compare operating models, not only APIs

A managed gateway, a BYOK control plane, and a self-hosted proxy can expose similar request shapes while assigning responsibility differently. Cloudflare documents a gateway layer with analytics and traffic controls. Portkey documents gateway configuration and governance capabilities. LiteLLM documents a proxy server that your team can deploy and operate. OpenRouter documents a hosted unified API and provider-routing controls. KeepRouter exposes its public routes through OpenAPI and current model eligibility through the model catalog.

Ask who owns provider accounts, credentials, billing reconciliation, gateway uptime, upgrades, security patches, data retention, model onboarding, and incident response. A deployment that looks inexpensive on a rate card may be expensive to operate; a managed service may reduce operations but require different trust and data review. Record these as responsibilities, not marketing adjectives.

Run the harness in four stages

Static inspection. Read OpenAPI, model details, status, security, limits, and error documentation. Record unknowns as unknowns.
Local contract tests. Point a test client at each candidate using sanitized fixtures. Assert fields, events, tools, usage, and error classes.
Injected failure tests. Exercise invalid credentials, unsupported fields, rate limits, timeout, cancellation, retry exhaustion, and eligible fallback. Never trigger destructive tools.
Bounded canary. Route a small, reversible slice with explicit quality, latency, error, and cost gates. Compare against the same baseline workload.

Use AI gateway fundamentals to define scope, routing versus load balancing to review policy, and the OpenAI-compatible migration checklist to test client behavior. KeepRouter's features hub, status page, and security page cover separate layers; none substitutes for a generation-path result.

Boundary: verification is route-, model-, account-, and time-specific

A passing GET, health page, or OpenAPI path proves structure or availability at that moment. It does not prove that your key can call a selected model, that a tool round trip works, or that a fallback preserves quality. Conversely, one failed request may indicate account scope or payload error rather than global outage. Label every artifact with timestamp, route, model, key scope, build when available, and test case so reviewers know exactly what it proves.

Frequently asked questions

Can a feature matrix choose an AI gateway?

It can shortlist candidates. Acceptance should come from reproducible tests of your exact contract, workload, data policy, and operating responsibilities.

Does a health check prove the generation route works?

No. It proves only the health surface at that time. A scoped generation test is needed for model eligibility, authentication, protocol, and usage evidence.

Sources reviewed

Article last reviewed 2026-08-15