The best AI gateway is not the product with the longest feature table. It is the request layer that fits your production constraints without hiding operational trade-offs. A useful comparison should test how the gateway behaves with your SDKs, providers, failure modes, security model and cost-accountability requirements. These questions create a repeatable evaluation before a team commits critical model traffic to a new control point.
Key takeaways
- Evaluate the request path with real traffic rather than buying from a screenshot or feature matrix.
- Compatibility, credential isolation and failure behavior are baseline requirements before optimization features matter.
- Ask how the product proves routing, cost and reliability decisions after the fact, not only how it configures them.
1–3: compatibility, providers and migration
First ask which OpenAI-compatible endpoints and streaming behaviors your applications actually depend on, which providers or model backends must be supported, and how much application code changes during migration. A gateway that requires a large rewrite creates switching cost before it provides operational value.
Run a representative request through the candidate gateway and compare response shape, streaming, errors and latency with your direct-provider path. Do not assume the words 'OpenAI-compatible' mean identical behavior for every SDK feature you use.
4–6: routing, retries and failure evidence
Ask how model and provider routing is expressed, which conditions can trigger retry or fallback, and whether the resulting execution path is visible afterward. A reliability policy that cannot be audited is difficult to tune.
The platform should make it possible to answer which provider handled a request, whether another attempt occurred, what latency was added and how much the recovery path cost. Otherwise routing logic becomes another black box.
7–9: observability, cost and ownership
Check whether request telemetry includes provider, model, status, latency and token usage and whether cost can be attributed to the workload that generated it. Then ask whether those dimensions remain consistent across providers.
A total spend chart is not enough for operations. Teams need to move from a cost or latency change to the application, agent, model and individual requests behind it. That drill-down is what turns gateway telemetry into operational evidence.
10–12: credentials, tenancy and governance
Ask where provider credentials are stored, how they are encrypted and rotated, and whether one organization or workspace can ever resolve another tenant's provider connection. Then inspect API-key scoping, roles and audit evidence for privileged actions.
A gateway sits on a sensitive request path. Security and tenancy are not enterprise extras; they are part of the architecture that determines whether multiple teams can safely share the platform.
How to run the final bake-off
Choose one real but non-critical workload and run it through each serious candidate. Measure migration effort, request success, P50/P95 latency, streaming behavior, operational visibility and cost attribution. Trigger a controlled provider failure if your environment allows it and inspect the evidence afterward.
Document trade-offs instead of forcing a universal winner. One team may value self-hosting and deep customization; another may value a managed control plane and faster operational setup. The right choice is the one whose architecture matches the responsibilities your team wants to own.
FAQ
Common questions
What should I compare when choosing an AI gateway?
Compare application compatibility, provider support, migration effort, routing and failure policy, observability, request-level cost attribution, provider credential handling, tenant isolation, governance and the evidence available after each routing decision.
Is the AI gateway with the most integrations always better?
No. Integration count matters only if it covers the providers and frameworks you use. Production behavior, security, observability and migration cost can be more important than a larger catalog.
CLYVEL
Put the operating model into practice.
Clyvel connects production AI traffic, cost, reliability and governance in one operations layer.
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Clyvel Research uses primary technical and vendor references wherever a claim benefits from external context.
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