In most enterprises, Claude adoption starts organically: one team wires up a support assistant, another builds a document pipeline, a third experiments in a notebook. Each integration works — and each answers governance questions differently, or not at all. The internal AI gateway pattern is the standard remedy: a service you run inside your network that all LLM traffic passes through on its way to the platform. Applications call your gateway; the gateway calls Claude on Amazon Bedrock, Google Vertex AI, Microsoft Foundry, or Claude Platform on AWS.
What a gateway buys you
One place for credentials. Application teams authenticate to your gateway with your standard internal auth; only the gateway holds cloud credentials for the model platform. That shrinks the credential surface dramatically and makes rotation a one-service exercise instead of an every-team scramble — the concerns covered in keeping credentials out of your code.
One place for logging. Every request and response can be logged consistently: who called, from which application, with how many tokens, at what latency, with what outcome. This is the raw material for audit trails, incident investigation, and the observability practices your ops team already expects of any production dependency.
One place for cost attribution. Because every request carries an application identity, the gateway can meter token usage per team, per feature, even per customer. When finance asks which product line is driving the Claude bill, you answer from data instead of estimates.
One place for policy. Rate limits per team, allowed models per environment, default timeouts, request-size caps, PII screening on the way out — enforced once, in code, rather than requested politely in a wiki page.
What the gateway should — and shouldn't — do
The strongest gateways stay thin. Good responsibilities: authentication and authorization, request/response logging, usage metering, rate limiting, retries for transient errors, and routing to the right platform and model. Risky responsibilities: rewriting prompts on the fly, caching responses without the application's knowledge, or accumulating business logic that belongs in the applications. A gateway that silently alters requests becomes the hardest-to-debug component in your stack.
Preserve the underlying Messages API shape as closely as possible. If your gateway exposes an interface that looks like the platform API, teams can develop against official SDK documentation, and moving an application from direct access to gateway access is a configuration change rather than a rewrite. This also keeps streaming working — interactive applications need tokens flowing through the gateway as they are generated, so build streaming pass-through in from day one rather than retrofitting it.
Multi-platform routing, honestly assessed
A gateway is also the natural seam for multi-platform strategies: it can route traffic to Bedrock or Vertex AI by policy, fail over between platforms, or steer specific workloads to Claude Platform on AWS for features unavailable elsewhere. This is genuinely valuable — and genuinely more work than it sounds, because platforms differ in feature availability (see the feature gaps) and in model ID conventions (Bedrock prefixes IDs with anthropic.; other platforms use bare IDs). If you route across platforms, your gateway must own those differences, and your evaluation process must cover each destination. Most organizations should start with one platform behind the gateway and add a second only when a concrete requirement — redundancy, procurement, feature access — justifies it.
Build, adopt, or start smaller
You do not need to build a large system on day one. A minimal viable gateway is a modest internal service: authenticate caller, log request metadata, forward to the platform SDK, log usage, return the response. Cloud-native primitives (your existing API gateway product, service mesh, or proxy layer) can supply parts of this. There are also commercial and open-source LLM gateway products; evaluate them like any other infrastructure dependency, with attention to whether they support the platforms and features you actually use.
The one mistake to avoid is deferring the pattern until "later." Retrofitting a gateway under ten live integrations is far more painful than starting with a thin one under your first two.
Where to go next
The gateway is where several operational threads meet — continue with observability for Claude apps for what to log and watch, or running Claude on two clouds if routing across platforms is on your roadmap. The platform overview summarizes the four destinations your gateway can front.