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Use stable model aliases, contract tests, canary traffic, and rollback gates to migrate LLM providers without coupling applications to constant model churn.

Learn how AI gateway rate limiting controls requests, tokens, concurrency, models, providers, users, teams, streaming workloads, retries, and AI spending.

Observe AI gateway traffic across models, providers, tokens, streaming latency, cost, retries, agents, and MCP tools while protecting sensitive data.

Test an OpenAI-compatible AI gateway across auth, model discovery, streaming, tool calls, errors, usage, and provider translation.

Plan and operate an AI gateway for production across deployment, request lifecycle, routing, security, observability, cost controls, and governance.

Compare LLM Gateway, AI Gateway, and API Gateway architectures, use cases, and enterprise requirements for governing AI traffic.

Envoy AI Gateway 1.0 and new LLM gateway projects show why production AI traffic needs policy, observability, failover, and cost control.