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LLM Apps in Production (RAG + Vector DB + Caching) · Lesson

Load Testing and Capacity Planning

Learn to simulate realistic traffic against an LLM application, find its breaking point, and plan capacity so production stays fast and within budget under load.

Why Load Test LLM Apps?

LLM apps behave differently under load than typical web services: token generation is slow, requests are long-lived, and upstream provider rate limits add a hard ceiling.

Load testing reveals how your system degrades before real users do.

Key Metrics

Track these under load:

  • Throughput — requests or tokens per second
  • Latency percentiles — p50, p95, p99
  • Error rate — timeouts, 429s
  • Time to first token for streaming

All lessons in this course

  1. Horizontal Scaling of RAG Components
  2. Observability: Logging, Metrics, Tracing
  3. Alerting and Incident Response for LLM Ops
  4. Load Testing and Capacity Planning
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