We Reduced AI Chatbot Latency by 30% with Streaming Responses and FastAPI 0.115
Patch: migrate chatbot API from Flask to FastAPI 0.115 and stream LLM tokens via StreamingResponse to cut latency by 30%.
Patch: replace Flask endpoint with FastAPI StreamingResponse, batch tokens, add heartbeats, enable HTTP/2, and deploy to production.
Summary
The article documents a migration from a Flask‑based chatbot API that returned full LLM responses to a FastAPI 0.115 stack that streams tokens in real time. The original stack had a 2.8 s time‑to‑first‑token and 9.2 s total response time for 500‑token replies, causing high bounce rates. FastAPI 0.115’s async generator and Server‑Sent Events support enabled token buffering (3‑5 tokens per chunk) and keep‑alive heartbeats, reducing TTFT to 0.12 s and total response time to 6.44 s—a 90 % and 30 % improvement respectively. The migration also cut API overhead from 0.8 s to 0.28 s (65 % reduction). Additional optimizations included HTTP/2 on Nginx, background task logging, and prompt caching. Benchmark results showed a 22 % drop in bounce rate and an 18 % increase in session length.
The implementation uses FastAPI’s StreamingResponse, async generators, and SSE comments to keep the connection alive. Token buffering balances latency and throughput, while error handling ensures graceful failures. The article concludes that streaming responses paired with FastAPI’s lightweight async stack deliver measurable UX gains.
This case study is a practical guide for teams looking to improve chatbot responsiveness without a major rewrite.
Key changes
- FastAPI 0.115 supports async generators and SSE for streaming
- LLM inference TTFT reduced from 1.2 s to 0.12 s
- Total response time dropped from 9.2 s to 6.44 s
- API overhead cut from 0.8 s to 0.28 s
- Token buffering batches 3‑5 tokens per chunk
- Keep‑alive heartbeats prevent timeouts
- Error handling via try/except in generator
- HTTP/2 on Nginx improves multiplexing and reduces overhead