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LiteLLM Router for Production AI

LiteLLM as the routing layer between your application and multiple AI backends — self-hosted, hosted, fallback, retry.

LiteLLM is the open-source router that abstracts your application from specific LLM providers. Point your code at LiteLLM; LiteLLM handles routing to self-hosted vLLM, hosted APIs, fallback, retry, rate limiting. The right primitive for hybrid AI architectures.

TL;DR

LiteLLM exposes a single OpenAI-compatible endpoint. Your app calls it. LiteLLM routes by model name to: self-hosted vLLM (default), Anthropic Claude (fallback), OpenAI (escalation). Handles retry, rate limiting, cost tracking, fallback rules. ~5 minutes to set up; transformative for hybrid AI architecture.

Why a router

  • Single API surface: app code knows one endpoint, not multiple SDKs
  • Centralised fallback logic: routing rules in one config, not scattered through codebase
  • Built-in retry: handles transient failures + rate limits
  • Cost tracking: aggregate cost across providers
  • Easy migration: swap providers by changing config, not code

Config

# litellm_config.yaml
model_list:
  - model_name: production-llm
    litellm_params:
      model: openai/Meta-Llama-3.1-8B-Instruct
      api_base: http://your-vllm:8000/v1
      api_key: dummy

  - model_name: production-llm-fallback
    litellm_params:
      model: anthropic/claude-3-5-sonnet-20241022
      api_key: os.environ/ANTHROPIC_API_KEY

router_settings:
  fallbacks:
    - production-llm: [production-llm-fallback]
  context_window_fallbacks:
    - production-llm: [production-llm-fallback]
  num_retries: 2
  timeout: 30

litellm_settings:
  drop_params: true
  set_verbose: false

Run with litellm --config litellm_config.yaml --port 4000. Your app talks to LiteLLM on port 4000.

Patterns

  • Self-hosted primary + hosted fallback: LiteLLM tries self-hosted; on failure or context-window-exceeded, routes to Claude / GPT-4o
  • Confidence-based routing: route to frontier API when self-hosted output confidence is low (custom header)
  • Per-tenant routing: free tier → self-hosted; premium → hosted; route by API key
  • A/B testing: route 10% to model B for evaluation
  • Rate-limit handling: when one provider rate-limits, automatic failover to next

Verdict

For any production AI architecture beyond single-provider, LiteLLM is the right routing primitive. Open source, OpenAI-compatible, well-maintained, fast. ~5 minutes setup; pays back the first time you need to swap providers or add fallback.

Bottom line

LiteLLM = the router for hybrid AI. See hybrid decision.

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