Claude Code Gateway: Route, Secure, and Monitor

The gateway sits between Claude Code instances and the Anthropic API. Every request to /v1/messages (or any API endpoint) is intercepted, authenticated, authorized, and routed. The gateway enforces quotas, logs usage, and proxies the request to Anthropic. Responses are logged and returned to the client.

Behind the scenes, the gateway keeps no state. It's stateless, horizontally scalable, and sits behind a load balancer. Multiple gateway instances share a backend: a Redis cache (for quotas and session data) and a database (for audit logs). If one gateway crashes, others take the traffic.

Routing logic: request → authenticate user → check team membership → verify quota → route to Anthropic → log response → return to client.

Claude Code must not store passwords. Instead, it delegates authentication to the company's identity provider (Okta, AzureAD, Ping, etc.) via OIDC or SAML. The gateway handles the handshake: if a user is not authenticated, redirect to the IdP login, capture the returned token, extract user and team info, and continue.

OIDC (OpenID Connect) is preferred for REST APIs. The gateway exchanges an authorization code for an access token, validates the token signature, extracts claims (sub, email, groups), and creates a session.

SAML is common in enterprises with legacy infrastructure. The gateway validates SAML assertions, maps group membership, and logs in the user.

Each group (team or department) has a quota: spend cap, token limit, and model allowlist. The gateway checks these on every request. If a user's group has exhausted their monthly budget, requests are rejected until the cycle resets.

Quotas are stored in Redis with TTL for performance. A background job (running every minute) reconciles Redis quotas with the database and updates cumulative cost. If a quota is exceeded, an alert is sent to Slack or email.

For fair-share usage, you can define per-user sub-quotas within a group quota. A group has $1000/month; an individual in that group gets $100/month. If the group is tracking to overspend, you can lower individual limits proportionally without re-deploying.

API keys (for Anthropic, database, etc.) must be rotated regularly. The gateway supports key rotation without downtime: a new key is added alongside the old one, both work, and after a grace period, the old key is removed.

Health checks ensure the gateway is alive and the backend services (Anthropic, database, Redis) are reachable. A health endpoint returns the status of each dependency. Load balancers use this to remove unhealthy instances.

If Anthropic API becomes unavailable, the gateway returns 503 with a cached recent response (if available) to reduce disruption. Circuit breaker logic: if Anthropic rejects 5 requests in a row, assume it's down, cache all requests for 30 seconds, then retry.

For high-traffic deployments, run the gateway on Kubernetes or Google Cloud Run. Kubernetes gives you fine-grained control: persistent volumes for logs, horizontal pod autoscaling, network policies, and multi-region failover. Cloud Run is simpler: push a container, set replicas and concurrency, and it auto-scales.

On Kubernetes, the gateway runs as a Deployment with 3+ replicas (for high availability). A Service exposes it to the load balancer. Redis and the database run outside K8s (managed services are more reliable). The gateway mounts a secret volume for API keys.

On Cloud Run, the gateway is a stateless container. Each request is routed to an available instance. Secrets are stored in Secret Manager. If traffic spikes, Cloud Run spawns new instances; if it drops, instances are torn down.

A single gateway instance handling 1000 concurrent users will eventually crash. High availability (HA) means the system remains operational even if some components fail.

Pattern 1: Multi-zone deployment. Run gateway instances in 3 availability zones (e.g., us-east-1a, 1b, 1c). If one zone fails, the other two continue. Use a regional load balancer to distribute traffic.

Pattern 2: Active-active multi-region. Run gateways in us-east and eu-west. Each region is fully independent; users in Europe hit eu-west, users in US hit us-east. If one region is unavailable, users failover to the other (DNS TTL = 60s, so failover is semi-automatic).

Pattern 3: Cache-aside with failover. If the primary gateway becomes unavailable, secondary gateways take over using shared Redis state. No data loss; users might see a 2–5 second blip.

Observability means you can understand what's happening inside the system by querying logs, metrics, and traces. The gateway emits all three.

Logs: every request produces a structured log (JSON) with timestamp, user ID, request path, model, response status, latency, and tokens consumed. Logs are shipped to a central sink (Datadog, GCP Logging, Splunk).

Metrics: counters for requests, errors, latency (p50, p95, p99), and cost. Dashboards alert if error rate exceeds 1% or latency p95 > 500ms.

Traces: a request generates a trace with spans for authentication, quota check, routing to Anthropic, and response. Traces help diagnose slow or erroring requests.

Implement using OpenTelemetry: a vendor-neutral standard for instrumentation.