Caching, Limits and Costs
Explore techniques to optimize large language model endpoints by implementing caching to reduce redundant provider calls, applying rate limits to manage traffic load, and using cost signals to control token usage. This lesson helps you understand how to balance latency, cost, and request accuracy while monitoring system performance and mitigating risks like stale cache or service overload.
A burst of 40 requests in 10 seconds pushes our FastAPI LLM endpoint past a 2 second p95 latency budget, and our logs show long prompts and retries driving token usage up. Nothing is broken, but the service becomes expensive and slow under load because many requests repeat and each provider call holds open a connection, especially with streaming.
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 what 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 we 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 returned by our provider SDK, often separated into input and output
Cache hit or miss once caching exists ...