Phase 8: Production Hardening & Operations · 55 min · Python · Redis · Kubernetes

Performance Optimization & Scaling

Fast enough for production means p99 under the SLO.

What you will learn

Optimize inference: model quantization, batching, speculative decoding, prompt caching
Implement semantic caching: caching responses to semantically similar queries for 60-80% cost reduction
Configure connection pooling: database, API client reuse, vector DB connection management
Scale horizontally: stateless API servers, queue-based workers, auto-scaling on request volume
Profile performance: identifying bottlenecks (embedding vs. retrieval vs. reranking vs. generation), p99 optimization

Objective

What you'll learn

This lesson takes approximately 55 min. By the end, you will be able to:

Optimize inference: model quantization, batching, speculative decoding, prompt caching
Implement semantic caching: caching responses to semantically similar queries for 60-80% cost reduction
Configure connection pooling: database, API client reuse, vector DB connection management
Scale horizontally: stateless API servers, queue-based workers, auto-scaling on request volume
Profile performance: identifying bottlenecks (embedding vs. retrieval vs. reranking vs. generation), p99 optimization

The Problem

"Fast enough for production" means p99 latency under the SLO — not average, not p50, but the 99th percentile. Achieving this with LLM systems requires specific techniques: response streaming, semantic caching, batch processing, model routing (fast model for easy queries, frontier model for hard ones), and prompt length optimization.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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