LLM Caching Tools: GPTCache vs Redis vs Momento Compared

GPTCache

GPTCache is an open-source library built specifically for LLM response caching. It provides a complete pipeline: embedding generation, similarity search, cache storage, and eviction policies, all configurable through a Python API.

How it works: GPTCache wraps your LLM client. When you call the wrapped client, GPTCache intercepts the request, generates an embedding of the query, searches its configured vector store for similar cached queries, and returns the cached response on a hit. On a miss, the request passes through to the LLM and the response is stored.

Storage backends: Supports SQLite (default, single-node), MySQL, PostgreSQL for response storage. For vector search, it supports FAISS (in-memory), Milvus, Qdrant, and ChromaDB. The modular design lets you swap backends without changing application code.

Strengths: Fastest path to a working semantic cache. Built-in support for multiple embedding models (OpenAI, Hugging Face, Cohere). Handles the entire caching pipeline so you do not need to build middleware yourself. Active open-source community.

Limitations: Python only. The default configuration (SQLite + FAISS) does not scale beyond a single process. Production deployments need external storage backends, which adds infrastructure complexity that approaches building a custom solution. Limited invalidation capabilities beyond TTL. Documentation can be sparse for advanced configuration.

Best for: Python projects that want semantic caching with minimal implementation effort. Prototyping and validating whether caching makes sense for your workload before investing in a custom solution.

Redis Stack with Vector Similarity

Redis Stack extends Redis with native vector similarity search, JSON document storage, and full-text search. This makes a single Redis instance capable of both exact match and semantic caching, plus serving as your general application cache.

How it works: Store each cache entry as a Redis hash containing the query embedding (as a vector field), the original query text, and the LLM response. Create an HNSW or flat vector index on the embedding field. Cache lookup performs a KNN search against this index, returning the nearest cached embedding with its similarity score.

Strengths: If you already use Redis, adding caching requires no new infrastructure. Sub-millisecond exact match lookups, sub-5ms vector searches. Native TTL support through Redis key expiration. Excellent client library support across every language. Battle-tested operational tooling (Redis Sentinel, Redis Cluster) for production reliability.

Limitations: All data lives in memory. At 1536-dimension embeddings (6KB per vector) plus response text (average 2KB), each cache entry uses roughly 8-10KB. One million entries require approximately 10GB of RAM. Managed Redis at this scale costs $100-300/month depending on provider. The vector search module is relatively new and less feature-rich than dedicated vector databases.

Pricing: Open source for self-hosted. Redis Cloud pricing starts at $0.881/hour for a 12GB instance (roughly $640/month). AWS ElastiCache and Azure Cache for Redis offer similar pricing. For smaller caches (under 1GB), costs are $15-50/month.

Best for: Teams already using Redis that want to add caching without new infrastructure. Applications where sub-5ms cache hit latency matters. Moderate-scale caches (under 5 million entries).

Momento

Momento is a serverless caching service that recently added vector index capabilities. It provides both traditional key-value caching and vector similarity search through a managed service with pay-per-use pricing.

How it works: Momento provides two services relevant to LLM caching. Momento Cache handles exact match caching with sub-millisecond latency. Momento Vector Index handles semantic caching with ANN search. Both are fully managed, requiring no infrastructure provisioning or scaling decisions.

Strengths: Zero infrastructure management. True serverless pricing, you pay per operation and per GB stored, not for provisioned capacity. Scales automatically from zero to millions of operations. SDK support for Python, JavaScript, Go, Java, Rust, and more. Built-in TTL management.

Limitations: Vendor lock-in to Momento's API. The vector index feature is newer and less mature than Redis or dedicated vector databases. Limited query filtering capabilities compared to Qdrant or Weaviate. No self-hosted option for organizations with strict data residency requirements.

Pricing: Free tier includes 5GB transfer and 1GB storage per month. Beyond that, $0.50 per GB data transfer, $0.10 per GB storage per month. For most LLM caching workloads, monthly cost falls between $10-100, significantly less than provisioned Redis for low-to-medium traffic applications.

Best for: Startups and small teams that want caching without managing infrastructure. Applications with variable traffic where serverless pay-per-use pricing saves money over provisioned resources. Multi-language projects that benefit from broad SDK support.

LangChain Cache Integration

LangChain includes built-in caching abstractions that work with multiple backends. If your application already uses LangChain, adding caching is a configuration change rather than an implementation project.

How it works: LangChain provides InMemoryCache, SQLiteCache, RedisCache, and GPTCache backends. Set the global cache once and all LLM calls made through LangChain automatically check cache first. The semantic caching option uses embeddings to match similar queries, configurable through the GPTCache backend integration.

Strengths: If you use LangChain, it is the lowest-effort path to caching. The abstraction layer lets you swap backends without changing LLM call code. InMemoryCache works instantly for prototyping.

Limitations: Only works within the LangChain ecosystem. The caching layer is tightly coupled to LangChain's LLM abstraction, so direct API calls bypass the cache. Limited control over caching logic compared to building your own middleware. SQLiteCache does not support semantic matching. For semantic caching through LangChain, you still need GPTCache or a custom implementation underneath.

Best for: LangChain-based applications that want basic caching with minimal code changes.

Dedicated Vector Databases as Cache Backends

Vector databases like Qdrant, Weaviate, Pinecone, and Milvus can serve as the semantic search component of an LLM cache. They provide the vector storage and search capability while you build the caching middleware logic yourself.

Qdrant: Excellent for LLM caching because it supports payload filtering (search only within a specific TTL window or tenant), has a generous free tier, and provides both in-memory and disk-backed storage. The gRPC API delivers sub-5ms search latency. Run it as a single Docker container for development or use Qdrant Cloud for production.

Weaviate: Includes built-in vectorization (you can send raw text and Weaviate generates embeddings), which simplifies the caching pipeline. Also supports hybrid search combining vector and keyword matching, useful for improving cache accuracy. Heavier infrastructure footprint than Qdrant.

Pinecone: Fully managed, serverless vector database. No infrastructure to manage. But per-query pricing ($8 per million queries for serverless) adds up for high-traffic caching workloads where you query the cache on every incoming request.

Best for: Teams that want full control over caching behavior and are willing to build the middleware layer themselves. Organizations that already use a vector database for other purposes (RAG, search) and want to consolidate infrastructure.

Custom Implementation

Building your own LLM cache from scratch gives you maximum control over every decision but requires the most engineering effort.

When to build custom: When no existing tool supports your specific requirements (unusual invalidation patterns, complex multi-tenant isolation, integration with proprietary systems). When your team needs deep understanding of the caching layer for debugging and optimization. When you are processing millions of requests per day and need to squeeze every millisecond of latency.

Typical stack: Redis for exact match caching, pgvector or Qdrant for semantic matching, a thin middleware service (FastAPI or Express) that handles the lookup and store logic, Prometheus for metrics, and Grafana for dashboards.

Effort estimate: A basic custom cache takes 2-3 days for a senior engineer. Adding semantic matching adds another 2-3 days. Production hardening (monitoring, alerting, graceful degradation, cache warming) adds a week. Total: 2-3 weeks for a production-ready custom implementation.

Best for: Teams with specific requirements that existing tools do not address. High-scale applications where the 10-20% performance overhead of abstraction layers matters. Organizations that want to avoid external dependencies for core infrastructure.

Decision Matrix

Want caching in an afternoon? Use GPTCache (Python) or LangChain CacheBackend (if already using LangChain). Accept default configurations and optimize later.

Already use Redis? Add Redis Stack vector module. One infrastructure component handles both exact match and semantic caching. Lowest marginal infrastructure cost.

Want zero infrastructure management? Use Momento for serverless caching or Pinecone for serverless vector search. Pay per use, scale automatically.

Need maximum control? Build custom on Redis + Qdrant/pgvector. More effort upfront but total control over every caching decision.

Processing millions of daily requests? Custom implementation or Redis Stack. At this scale, the overhead of abstraction layers and the per-query costs of managed services become material considerations.