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Architect · AI Architecture

Budget-Aware LLM Routing & Fallback

Built cost guardrails into the LLM layer — per-tenant budgets, model downgrades under load, response caching, and graceful multi-provider fallback.

Context: LLM costs can spiral and providers fail. Decision: route every call through a budget-aware layer that enforces per-company rate limits, downgrades to cheaper models when appropriate, caches by content hash, and degrades gracefully instead of erroring. Trade-off: some routing complexity for predictable cost and reliability.

litellmOpenRouterRedis cacheOllama

What it does

  • Per-tenant rate limits & budgets; hard stops before overspend.
  • Model downgrade & hash-based caching to cut duplicate calls.
  • Graceful fallback across providers (OpenAI / Claude / OpenRouter / Ollama).
Next step

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Budget-Aware LLM Routing & Fallback | Codigeni