Key Takeaway
By the end of this blueprint you will have a production LLM gateway built on LiteLLM that provides a unified OpenAI-compatible API across Anthropic, OpenAI, and open-source providers, with per-team rate limiting, budget enforcement, automatic failover, and structured telemetry for every request.
Prerequisites
- Python 3.11+ or Docker for running LiteLLM Proxy
- PostgreSQL or Redis for rate limit state and usage tracking
- API keys for at least two LLM providers (Anthropic, OpenAI, or self-hosted)
- Basic understanding of reverse proxies and HTTP middleware patterns
- A monitoring stack (Prometheus + Grafana or equivalent) for dashboarding
Why a Centralized LLM Gateway?
Without a gateway, every team manages their own API keys, rate limits, and provider integrations. This creates three problems that compound as you scale: cost visibility disappears because spend is scattered across dozens of API keys with no central attribution; security weakens because API keys are embedded in application configs across repositories; and reliability suffers because each application must implement its own retry and failover logic. A gateway centralizes all of this into a single layer that platform teams operate.
Architecture Overview
The gateway sits as a reverse proxy between application teams and LLM providers. Incoming requests pass through an authentication layer, a rate-limit and budget-enforcement layer, and a routing layer that selects the optimal provider based on model requirements, latency, and cost. Response streams are proxied back with injected telemetry headers for downstream observability.
Setting Up LiteLLM Proxy
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