Engineering answer

When should you use an AI gateway?

Use an AI gateway when several services need shared model access, when credentials and spend controls should be centralized, when teams switch or evaluate models often, or when routing and request evidence should follow one policy. A direct provider integration is often simpler for one stable workload that needs provider-native features and has no shared governance requirement.

Last reviewed 2026-08-15 · Editorial review: KeepRouter Editorial

Five signals that justify a gateway

Signal	What is already becoming difficult
Several applications call models	Credentials, SDK wrappers and error policy are duplicated
More than one approved model or provider	IDs, routes, evaluation and rollback scatter across codebases
Spend needs ownership	Provider invoices do not map cleanly to tenants, services or features
Reliability policy is shared	Every team implements different retries, timeouts and fallbacks
Security or audit evidence is required	Request identity, data handling and model decisions are inconsistent

One signal can be enough if its cost is real. Do not wait for every row. Equally, do not introduce a gateway because multi-model architecture sounds mature when none of these problems exists.

Cases where direct integration is clearer

A product may depend on one provider's newest API, specialized tool behavior, fine-tuning, batch service or contractual region. A gateway compatibility layer might lag or omit those features. Direct integration can also be easier for a small internal script with low risk, one owner and no need for shared billing or audit.

The choice is not permanent. Keep provider configuration behind a small application boundary, preserve request tests and add a gateway when the operational pressure appears. The OpenAI-compatible API answer explains what a later base URL migration can and cannot preserve.

Managed or self-hosted comes next

After deciding that a gateway is useful, decide who should run it. Managed model access reduces provider onboarding and infrastructure work. BYOK preserves provider accounts while centralizing policy. Self-hosting gives infrastructure control and adds deployment, security, storage and on-call responsibility. The managed versus self-hosted guide compares those ownership models.

A small adoption path

Choose one representative service and one approved model.
Document the exact endpoint, streaming, tool and error contract it uses.
Issue a narrow key and reproduce the direct request through the gateway.
Reconcile model, usage, cost and status with both sides.
Test failure, cancellation and rollback.
Move shared policy into the gateway only after the evidence is stable.

Do not migrate every application at once. A gateway becomes useful by making common policy visible and repeatable, not by creating a large central project.

Recheck after the first month

Count how many provider integrations were removed, which incidents the gateway clarified, what operational work it added, and whether request attribution improved. Keep it if the shared control is earning its place. Simplify or remove it if it only adds another dashboard and hop. Use how to choose a gateway for the product evaluation stage.

Frequently asked questions

Do I need multiple providers before using a gateway?

No. Shared credentials, spend attribution, security policy or request evidence can justify a gateway even with one provider.

Is a gateway useful for a prototype?

It can be if it shortens model evaluation, but a small direct integration may be simpler when no shared controls are needed.

Should every AI call go through one gateway?

Only calls covered by its tested contract. Provider-native or specialized operations can remain direct when that is the clearer boundary.

Can I add a gateway later?

Yes. Keep provider URLs, keys and model IDs in configuration and maintain representative tests to make a later migration safer.

How do I know the gateway is helping?

Measure removed integration work, attribution quality, incident evidence, policy consistency, added latency and operating effort.