LLM Proxy with Prompt Caching
Slash your AI API costs by up to 90% with intelligent prompt caching. Our proxy layer identifies similar requests, caches responses, and serves instant results for repeated queries while maintaining response quality and freshness.
Caching Features
Enterprise-grade caching capabilities that optimize costs without compromising on response quality or user experience.
Semantic Similarity
Cache responses for semantically similar prompts, not just exact matches. Using vector embeddings to identify when different phrasings ask the same question, maximizing cache utilization.
TTL Management
Flexible time-to-live settings for different content types. Set shorter TTLs for time-sensitive queries and longer durations for stable, factual information to optimize cache freshness.
Smart Invalidation
Intelligent cache invalidation based on content changes, model updates, and custom triggers. Automatic detection of when cached responses become stale or outdated.
Cache Analytics
Detailed metrics on cache hit rates, cost savings, and performance gains. Understand which prompts benefit most from caching and optimize your caching strategy accordingly.
Context-Aware
Cache responses considering user context, conversation history, and session state. Maintain personalization while maximizing cache hits across similar user queries.
Multi-Layer Storage
Hierarchical caching with in-memory, Redis, and persistent storage layers. Balance speed and capacity with automatic promotion and demotion of cached content.
Caching Strategies
Choose the right caching approach based on your use case, content type, and performance requirements.
Traditional caching that stores responses for identical prompts. Fastest lookup speed with zero false positives. Ideal for frequently repeated, identical queries.
- O(1) lookup complexity
- 100% accuracy guarantee
- Lowest computational overhead
- Best for FAQs and templates
Uses vector embeddings to identify semantically similar prompts. Catches rephrased questions and variations, dramatically increasing cache hit rates for natural language queries.
- Embedding-based similarity
- Configurable threshold (0.8-0.99)
- Handles paraphrasing naturally
- Best for conversational AI
Extracts templates from prompts and caches responses for template patterns. Efficient for structured queries with variable parameters that produce similar responses.
- Pattern-based matching
- Variable extraction
- Partial response caching
- Best for structured queries
Combines multiple caching strategies for optimal coverage. Falls back from exact match to semantic search, maximizing cache hits while maintaining accuracy.
- Multi-tier fallback
- Adaptive selection
- Highest hit rates
- Best for production systems
Cache Architecture
High-performance caching layer designed for minimal latency and maximum throughput.
Lookup
Return
Call
import hashlib from sentence_transformers import SentenceTransformer class SemanticCache: def __init__(self, threshold=0.85): self.model = SentenceTransformer('all-MiniLM-L6-v2') self.cache = {} self.threshold = threshold def get_embedding(self, prompt): # Generate semantic embedding return self.model.encode(prompt) def find_similar(self, prompt): # Search for semantically similar cached prompts query_emb = self.get_embedding(prompt) for cached_prompt, (response, embedding) in self.cache.items(): similarity = cosine_similarity(query_emb, embedding) if similarity >= self.threshold: return response, similarity return None, 0.0 def set(self, prompt, response): # Cache prompt with embedding embedding = self.get_embedding(prompt) self.cache[prompt] = (response, embedding)
Cache Backend Comparison
Select the optimal storage backend for your caching requirements.
| Feature | In-Memory | Redis | PostgreSQL | Vector DB |
|---|---|---|---|---|
| Lookup Speed | <1ms ✓ | <5ms ✓ | ~20ms | ~50ms |
| Capacity | Limited | Large ✓ | Unlimited ✓ | Large ✓ |
| Semantic Search | No | Limited | No | Yes ✓ |
| Persistence | No | Yes ✓ | Yes ✓ | Yes ✓ |
| Distributed | No | Yes ✓ | Yes ✓ | Yes ✓ |
| Best For | Hot data | General use | Analytics | Semantic |
Caching Best Practices
Optimize your caching strategy with proven patterns and techniques.
Configure time-to-live values based on content type. Factual information can have longer TTLs, while time-sensitive data needs shorter durations.
- Facts & definitions: 24-48 hours
- Code examples: 12-24 hours
- News & current events: 1-4 hours
- Personalized content: 15-60 minutes
Adjust semantic similarity thresholds to balance cache hits against response relevance. Higher thresholds mean fewer false positives.
- 0.99: Near-exact matches only
- 0.95: High precision, moderate recall
- 0.85: Balanced approach
- 0.75: Aggressive caching
Pre-populate your cache with common queries during off-peak hours. Ensures high hit rates from the moment users start interacting.
- Identify top 100 common queries
- Schedule pre-generation jobs
- Monitor cache hit ratios
- Update warm cache regularly
Track cache performance metrics continuously. Identify optimization opportunities and detect when cache quality degrades.
- Track hit/miss ratios
- Measure cost savings
- Monitor response quality
- Alert on performance drops
Start Saving on AI Costs Today
Implement intelligent prompt caching in your LLM proxy and reduce API costs by up to 90%. Our comprehensive guides and examples help you get started in minutes.
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