Advanced Techniques: Caching, Multi Model Routing, and Cost Optimization

Semantic Caching

Many queries are semantically equivalent: "What is the weather?" and "Tell me today's weather" should return the same cached response. Semantic caching embeds queries into vectors and returns cached results for queries within a similarity threshold. This can reduce API costs by 30-50% for applications with repetitive query patterns like customer support or FAQ systems.

Cache invalidation matters. Weather data stales in hours; product information might be valid for days; fundamental definitions can be cached indefinitely. Design TTL (time to live) based on how quickly the underlying information changes.

Multi Model Routing

Different requests need different models. Simple queries (greetings, FAQ lookups) can use fast, cheap models. Complex queries (analysis, reasoning) need capable, expensive models. A router classifies incoming requests and directs them to appropriate models. This might reduce costs 60-70% while maintaining quality: 80% of requests go to cheap models, 20% to expensive ones.

Router implementation options: rule-based (keyword matching, query length), ML classifier trained on query complexity, or cascade (try cheap model first, escalate if confidence is low). Cascading adds latency but maximizes cost savings.

Cost Optimization

Every token costs money. Optimization strategies: trim prompt length by removing unnecessary examples once the model learns the pattern, use shorter instructions when possible without sacrificing clarity, truncate or summarize long context instead of including everything, batch requests where latency allows (reduces per-request overhead). Track cost per request type and optimize the highest-cost flows first.