Caching, Limits, and Costs
Explore how to optimize large language model APIs by implementing caching strategies, setting rate limits, and monitoring request costs. Understand which computations are safe to cache and how to balance latency, cost, and accuracy. Learn to design effective policy layers that protect performance budgets and maintain stable AI service operation under load.
A burst of 40 requests in 10 seconds pushes your FastAPI LLM endpoint past a 2-second p95 latency budget, and your logs show long prompts and retries driving up token usage. Nothing is broken, but the service becomes expensive and slow under load because many requests repeat and each provider call holds a connection open, especially with streaming.
The scope is a thin policy layer around the existing endpoint that adds caching, rate limits, and per-request cost signals without changing the core generation function.
Before changing anything, decide which metric the policy layer protects, because each control trades one resource for another. A cache spends memory to save provider calls, a limiter rejects traffic to protect latency, and a spend guard trims outputs to reduce tokens. The baseline signals you can observe at the API boundary are usually enough to pick a target.
Request rate and concurrency from the web server and load balancer
Median and p95 latency measured at the handler boundary
Error rate split by 4xx, 5xx, and provider timeouts
Token usage fields, if your provider SDK returns them, often split into input and output
Cache hit or ...