Dramatically reduce API costs and improve response times with intelligent response caching. Semantic similarity matching, smart invalidation, and automatic cache warming for optimal performance.
Enterprise-grade caching for optimal LLM API performance
Match semantically similar queries using embeddings. Different phrasings of the same question get cached responses.
Set cache expiration times per endpoint, model, or content type. Balance freshness with performance.
Automatic cache invalidation when models update, parameters change, or content becomes stale.
Pre-populate cache with common queries during off-peak hours for instant responses during traffic spikes.
Store cached responses in Redis, Memcached, S3, or in-memory for flexibility across deployment scenarios.
Real-time metrics on hit rates, latency improvements, cost savings, and cache efficiency.
Choose the right caching strategy for your use case
Based on typical usage patterns
# Response caching configuration caching: enabled: true backend: type: "redis" host: "redis.example.com" port: 6379 db: 0 strategies: - type: "semantic" similarity_threshold: 0.95 embedding_model: "text-embedding-3-small" ttl: 86400 # 24 hours - type: "exact" ttl: 3600 # 1 hour invalidation: on_model_change: true on_parameter_change: true max_entries: 1000000 warming: enabled: true schedule: "0 3 * * *" # Daily at 3 AM queries: "common_queries.json"
Combine caching with multi-provider routing for maximum efficiency.
Track savings from caching in your cost analytics dashboard.
Log cache hits and misses for performance analysis and optimization.
Edge-deployed caching for ultra-low latency response delivery.
Implement intelligent response caching and see immediate cost reductions and performance improvements.