BUYER RESOURCE

AI Gateway Evaluation Checklist

Thirty-six production questions for evaluating an AI gateway across migration, routing, observability, cost, reliability and governance. Use the checklist with real traffic instead of comparing vendors by feature count alone.

Explore Clyvel Gateway Read the buyer guide

Compatibility & migration

6 checks

Can existing OpenAI-compatible clients migrate with a controlled base URL and credential change?

Are streaming behavior, status codes and errors compatible with the clients we already use?

Can migration happen workload by workload instead of as a flag-day rewrite?

Are provider-specific capabilities represented explicitly instead of silently normalized away?

Can we test one route against direct-provider behavior before expanding traffic?

Is the application-facing contract documented clearly enough for engineering ownership?

Identity, credentials & routing

6 checks

Do applications authenticate with scoped gateway credentials instead of upstream provider secrets?

Are provider credentials stored and resolved server-side per organization or workspace?

Can every request be attributed to an application, agent, environment or workload?

Can routes be selected by model or policy without embedding routing logic in every application?

Is the chosen provider and model visible after the request completes?

Can credentials be rotated or revoked without redeploying every application?

Observability

6 checks

Does every request capture provider, model, status and end-to-end latency?

Are input and output tokens visible at request level?

For streaming workloads, can we distinguish time to first token from total duration?

Can we move from an aggregate latency or error change to representative requests quickly?

Can agent or trace context group multiple model calls into one user-visible workflow?

Is telemetry useful without requiring sensitive prompt content to be stored indiscriminately?

AI FinOps

6 checks

Can provider spend be attributed to workloads, applications and agents?

Can we inspect cost per request or another useful unit instead of only monthly totals?

Are model and token dimensions available alongside estimated cost?

Can budgets belong to the teams or workloads that own the traffic?

When a budget moves, can operators identify the requests and model mix responsible?

Does the platform clearly separate its own fee from model-provider inference spend?

Reliability & failover

6 checks

Can retryable failures be defined explicitly instead of retrying every error?

Are retries and fallbacks recorded as separate attempts under the original trace or request context?

Can fallback policy account for model capability, latency and cost differences?

Can operators see provider health and the evidence behind a failover decision?

Are retry and fallback counts bounded to avoid hidden cost multiplication?

Can we measure whether the alternate route actually improves successful outcomes?

Security, governance & operations

6 checks

Are roles scoped so sensitive routing, provider, budget and governance changes are restricted?

Is there an audit history for operational changes that can affect production behavior?

Can an organization review which members and workloads can access production AI traffic?

Are private dashboard and account pages excluded from search indexing?

Can governance evidence be correlated with request and incident evidence?

Does the platform provide enough documentation for a different team or buyer to operate it safely?

Use it as an evaluation script.

Run one real workload through a candidate gateway, then answer these questions from the resulting evidence. If a requirement cannot be demonstrated with a production-like request, keep it marked as unverified rather than assuming the marketing page proves it.

Integration guides →Clyvel pricing →Enterprise controls →

Use it as an evaluation script.