I need a proxy that caches LLM responses (semantic caching) to reduce API costs.

Yes. If you want an LLM proxy/gateway that sits between your app and OpenAI/Anthropic/etc. and does semantic response caching, I’d shortlist these:

*LiteLLM's caching ecosystem supports semantic backends such as Redis/Qdrant; verify the exact semantic-cache configuration against the version you deploy.

My recommendation

If you mainly want to reduce API costs with minimal engineering: use Redis LangCache.

It takes a prompt, finds a sufficiently similar previous prompt using embeddings, and returns the stored response instead of calling the LLM. It supports configurable similarity thresholds, TTL/eviction, and REST/Python/JS integration.

Architecture:

Your application
      │
      ▼
┌─────────────────┐
│  LLM Gateway    │
│                 │
│  1. normalize   │
│  2. semantic    │
│     cache lookup│
└────────┬────────┘
         │ hit
         ▼
   cached response

         │ miss
         ▼
┌─────────────────┐
│ OpenAI / Claude │
│ / Gemini / etc. │
└────────┬────────┘
         │
         ▼
   store response
   in semantic cache

Redis specifically recommends storing the prompt embedding, response, and metadata together, with vector similarity plus metadata filters such as tenant/model/version.

If you also need a real LLM gateway

I'd use LiteLLM in front and Redis behind it:

                 ┌── OpenAI
                 ├── Anthropic
Your app ──> LiteLLM ── Gemini
                 ├── Azure
                 └── Bedrock
                    │
                    ▼
              Redis semantic cache

LiteLLM gives you an OpenAI-compatible proxy, routing, fallbacks, authentication, rate limiting, and spend tracking.

For a production system, I'd also make the cache key/context include:

• model + model version
• system prompt version
• tenant/customer ID where responses aren't globally shareable
• relevant RAG/document version
• temperature / important generation parameters
• locale
• safety/policy version

And don't blindly cache conversational requests. Semantic similarity alone can incorrectly treat two contextually different questions as equivalent. A conservative similarity threshold plus namespace/metadata filtering is much safer.

If you tell me whether you're using OpenAI, Anthropic, or multiple providers, and whether you want self-hosted or managed, I can give you a concrete proxy architecture and Docker config.

It takes a prompt, finds a sufficiently similar previous prompt using embeddings, and returns the stored response instead of calling the LLM. It supports configurable similarity thresholds, TTL/eviction, and REST/Python/JS integration.

Architecture:

Your application
      │
      ▼
┌─────────────────┐
│  LLM Gateway    │
│                 │
│  1. normalize   │
│  2. semantic    │
│     cache lookup│
└────────┬────────┘
         │ hit
         ▼
   cached response

         │ miss
         ▼
┌─────────────────┐
│ OpenAI / Claude │
│ / Gemini / etc. │
└────────┬────────┘
         │
         ▼
   store response
   in semantic cache

Redis specifically recommends storing the prompt embedding, response, and metadata together, with vector similarity plus metadata filters such as tenant/model/version.

If you also need a real LLM gateway

I'd use LiteLLM in front and Redis behind it:

                 ┌── OpenAI
                 ├── Anthropic
Your app ──> LiteLLM ── Gemini
                 ├── Azure
                 └── Bedrock
                    │
                    ▼
              Redis semantic cache

LiteLLM gives you an OpenAI-compatible proxy, routing, fallbacks, authentication, rate limiting, and spend tracking.

For a production system, I'd also make the cache key/context include:

To implement a semantic caching proxy for LLMs, you have a few robust, production-ready open-source options that sit between your application and providers like OpenAI, Anthropic, or local models. Semantic caching embeds incoming prompts and performs a vector similarity search (using cosine similarity) to return cached answers for closely related questions, rather than requiring an exact string match.

Top choices for an LLM semantic caching proxy include:

1. LiteLLM Proxy
   • What it is: A widely adopted, enterprise-ready open-source AI gateway/proxy that standardizes 100+ LLM APIs.
   • Semantic Caching support: It natively supports semantic caching via backends like LiteLLM Semantic Caching Docs using Redis/Valkey or Qdrant.
   • Why choose it: It handles load balancing, fallback routing, spend tracking, and rate limiting out-of-the-box alongside semantic caching.
2. Semcache
   • What it is: A lightweight, dedicated open-source HTTP proxy designed specifically to intercept LLM calls, cache semantically similar prompts, and reduce token usage.
   • Features: Built-in Prometheus metrics, an /admin dashboard for hit-rate tracking, and smart LRU cache eviction.
   • Why choose it: Drop-in compatibility; you simply point your standard OpenAI SDK base_url to the local Semcache instance.
3. PromptCache (Go-based Proxy)
   • What it is: A fast, provider-agnostic drop-in proxy written in Go.
   • Features: Intelligent threshold configuration (CACHE_HIGH_THRESHOLD and CACHE_LOW_THRESHOLD ) with a gray-zone verifier to balance cost savings and response accuracy.
   • Why choose it: Ideal if you prefer a lightweight, high-performance compiled binary rather than a heavy Python stack.

Important Caveat on Semantic Caching

Semantic caching works brilliantly for single-shot, independent queries (e.g., FAQ bots, classification tasks, code generation snippets). However, be cautious when enabling it for multi-turn, stateful agentic or conversational traffic , as a semantic hit might pull a stale response from a different conversational context.

To help narrow down the best setup, tell me:

• What LLM provider(s) are you routing through?
• Are you looking for a Python-based gateway (like LiteLLM) or a standalone lightweight proxy container?
• Is your traffic mostly single-shot queries or multi-turn chat conversations?

Yes. If you want a proxy that sits between your application and OpenAI/Anthropic/etc. and does semantic response caching, there are several good options.

My shortlist

• redis.io — probably my first choice if you want a production-ready managed service. It performs semantic similarity matching, generates embeddings for you, and returns cached responses without making an LLM call.
• github.com — particularly interesting if you specifically want an HTTP proxy. Your OpenAI/Anthropic client points at Semcache instead of the provider endpoint, and it handles semantic lookup before forwarding misses.
• github.com — mature open-source semantic caching library with a server/Docker option. Good if you want to own the infrastructure, although its maintainers note that support for new LLM APIs/models is no longer their focus.
• docs.langchain.com — good if you're already using LangChain. RedisSemanticCache provides vector-similarity response caching with configurable thresholds and TTLs.
• semacache.io — a commercial drop-in LLM caching proxy supporting OpenAI, Claude, Gemini, Grok and others, with semantic matching.

What I'd build

For a typical production application:

Your App
   │
   ▼
Semantic Cache Proxy
   │
   ├── semantic hit ──► cached response
   │
   └── miss
         │
         ▼
      LLM API
         │
         ▼
   store response + embedding

I'd use Redis LangCache if you don't want to operate the semantic-cache infrastructure yourself. If you specifically need a self-hosted OpenAI-compatible HTTP proxy, I'd investigate Semcache first. Redis also documents a self-managed approach where embeddings + vector similarity search + TTLs are handled directly in Redis.

One important design detail: don't use semantic similarity alone as the cache key. Include things such as model, system-prompt/version, temperature/configuration, tenant, locale, and relevant RAG/document version in the cache namespace. Otherwise, a legitimately similar question can return an answer generated under the wrong context.

If you tell me whether you're using OpenAI, Anthropic, Gemini, or multiple providers and whether you want self-hosted or managed, I can recommend the best proxy and give you a concrete Docker/config setup.

Implementing a semantic caching proxy is one of the most effective ways to slash LLM bills and lower response latency for recurrent or closely related user prompts . Instead of doing exact-string matching, a semantic cache converts incoming prompts into vector embeddings and checks them against a vector database for similarity (e.g., cosine similarity ≥ 0.88).

Top production-ready open-source proxies and managed AI gateways offer drop-in semantic caching functionality:

1. LiteLLM Proxy (Open Source / Enterprise)

• How it works: LiteLLM standardizes requests across 100+ LLM providers using an OpenAI-compatible format. It features built-in semantic caching via redis-semantic or qdrant-semantic cache modes.
• Backend requirement : Requires a vector/cache database backend like Redis or Qdrant to compute and store prompt vectors.
• Best for : Teams wanting a self-hosted, highly versatile proxy that handles load balancing, virtual keys, spend tracking, and semantic caching all in one package.

2. Portkey AI Gateway (Open Source / Managed)

• How it works : Portkey provides a production-grade AI gateway with out-of-the-box semantic caching. It first checks exact matches via a fast key-value store, then runs hybrid semantic/vector lookups (such as through Pinecone) for near-matches.
• Integration : Drop-in OpenAI-compatible base URL replacement with granular controls (like custom TTLs, namespaces, and confidence thresholds).
• Best for : Production environments needing advanced observability, prompt routing, fallbacks, and managed semantic caching configurations.

3. GPTCache (Open Source Library by Zilliz)

• How it works: GPTCache isn’t a rigid standalone proxy server, but rather a flexible library designed specifically to build customized semantic caching layers for your LLM apps. It integrates deeply with LangChain and LlamaIndex.
• Architecture : Pluggable modules for embedding generation (e.g., HuggingFace, OpenAI embeddings), vector stores (Milvus, Qdrant, Chroma), and exact/semantic match managers.
• Best for : Developers writing custom microservices or looking to embed semantic caching directly into their application middleware rather than routing through an external gateway.

Key Architectural Trade-Offs to Keep in Mind

• Similarity Threshold Tuning : Set your threshold carefully (usually between 0.85 and 0.92). If it's too loose, users might receive semantically incorrect or outdated cached answers; if it's too tight, you’ll miss valid cache hits.
• Embedding Latency Overhead : Generating an embedding for every incoming prompt adds a tiny latency penalty (and a small embedding cost). Ensure your embedding model runs locally or via a lightning-fast endpoint (e.g., bge-small or text-embedding-3-small).

To help narrow down the right choice, tell me:

• Are you looking for a self-hosted open-source solution or a managed service?
• What infrastructure/database do you already run in production (e.g., Redis, Postgres/pgvector, Qdrant)?

If you want a drop-in proxy that sits in front of OpenAI/Anthropic/etc. and performs semantic response caching, you have a few good options depending on whether you want self-hosted or managed.

Self-hosted options

• github.com — lightweight semantic cache proxy. It supports OpenAI-compatible usage by changing your SDK base_url, and can sit between your app and LLM providers. It includes semantic matching, metrics, and an admin dashboard.
• github.com — a Go-based OpenAI-compatible semantic caching proxy. It supports streaming responses, configurable similarity thresholds, cache warming, and provider-agnostic routing.
• thriftllm.com — a self-hosted LLM gateway with semantic caching, multi-provider routing, fallbacks, and cost tracking.
• aclanthology.org — an open-source semantic cache library. It is more of a framework/library than a ready-made HTTP proxy, but it is useful if you want to embed caching directly into an application.

Managed proxy options

• semacache.io — hosted OpenAI-compatible caching proxy. You point your SDK at their endpoint and it handles exact + semantic cache lookup.

What I would deploy

For a production app, the architecture usually looks like:

Your application
        |
        v
LLM Cache Proxy
        |
        +--> semantic embedding lookup
        |        |
        |        +--> cache hit → return stored response
        |
        +--> cache miss → call LLM provider
                         |
                         +--> store response + embedding

A robust cache key should include more than just the user prompt:

cache_key =
  hash(
    model,
    system_prompt,
    tools/function schemas,
    temperature,
    max_tokens,
    user prompt,
    tenant/user scope
  )

Otherwise you risk returning a cached answer generated under different constraints.

Recommended stack for a serious deployment

• Proxy: PromptCache / Semcache / ThriftLLM
• Vector store: Qdrant, pgvector, or Redis Vector
• Embeddings: local model (e.g., BGE/E5) or provider embeddings
• Exact cache layer: Redis
• Metrics: Prometheus + Grafana
• Safety: similarity threshold + optional reranker before returning a hit

A common pattern is:

1. Exact hash lookup (milliseconds)
2. Semantic vector lookup
3. Similarity threshold check
4. Optional cheap verifier model for borderline cases
5. Return cached answer or call the LLM

If you tell me your stack (OpenAI SDK, Anthropic SDK, LangChain, Kubernetes, Python/Node/Go, expected request volume), I can suggest a concrete deployment design.

Recommended stack for a serious deployment

• Proxy: PromptCache / Semcache / ThriftLLM
• Vector store: Qdrant, pgvector, or Redis Vector
• Embeddings: local model (e.g., BGE/E5) or provider embeddings
• Exact cache layer: Redis
• Metrics: Prometheus + Grafana
• Safety: similarity threshold + optional reranker before returning a hit

A common pattern is:

1. Exact hash lookup (milliseconds)
2. Semantic vector lookup
3. Similarity threshold check
4. Optional cheap verifier model for borderline cases
5. Return cached answer or call the LLM

If you tell me your stack (OpenAI SDK, Anthropic SDK, LangChain, Kubernetes, Python/Node/Go, expected request volume), I can suggest a concrete deployment design.

Implementing a semantic cache proxy allows you to intercept inbound prompts, calculate their vector embeddings, and check a vector-capable data store for semantically similar previous queries. If a match exceeds your similarity threshold (e.g., 0.80 - 0.88), it returns the cached response instantly—bypassing the LLM provider entirely and cutting costs.

Top open-source and enterprise proxy choices handle this natively:

• LiteLLM Proxy: An open-source Python-based proxy that supports drop-in semantic caching using Redis, Valkey, or Qdrant. It translates incoming OpenAI-compatible requests, generates embeddings, and evaluates similarity on the fly.
• Kong AI Gateway: Features a built-in AI Semantic Cache plugin suitable for enterprise routing and API management, mapping vector embeddings to reduce redundant upstream token processing.
• Custom Edge/Go Gateways: Lightweight options (such as custom Workers combined with vector databases like Cloudflare Vectorize) can perform semantic matching at the edge in under 10 ms.

Setting up Semantic Caching with LiteLLM & Redis/Valkey

If you want a fast, self-hosted open-source proxy solution, LiteLLM handles semantic configuration directly in its config.yaml.

1. Spin up a vector-capable cache store (like Redis or Valkey with search modules enabled).
2. Configure your LiteLLM config.yaml to turn on semantic caching:

yaml``` litellm_settings: cache: True

cache_params: type: "valkey-semantic" # or "redis-semantic" host: "your-redis-or-valkey-host" port: 6379 valkey_semantic_cache_embedding_model: "text-embedding-3-small" similarity_threshold: 0.85

Use code with caution.

To help narrow down the best setup, tell me:

- What **LLM provider** are you using (OpenAI, Anthropic, self-hosted vLLM)?
- Do you prefer a **lightweight self-hosted proxy** (like LiteLLM) or an **enterprise API gateway** (like Kong)?
- What is your expected **request volume** per day?

Yes. If your main requirement is an LLM gateway/proxy that can do semantic (similarity-based) response caching, I’d shortlist these:

• docs.litellm.ai — best self-hosted/general-purpose choice. It provides an OpenAI-compatible proxy and supports semantic caching backed by systems such as Redis/Qdrant. That lets a query like “How do I reset my password?” potentially reuse the response for “I forgot my password—how can I reset it?” rather than requiring an exact string match.
• docs.semcache.io — worth considering if caching is the primary purpose. It operates as an HTTP proxy and supports multiple LLM providers/custom APIs.
• docs.helicone.ai — good if you also want observability. Its documented cache is primarily exact-request caching, rather than semantic similarity caching, so I wouldn't pick it if semantic caching is your key requirement.

What I'd choose

If you're comfortable running infrastructure, I'd start with:

Your app → LiteLLM Proxy → semantic cache (Redis/Qdrant) → OpenAI/Anthropic/etc.

This gives you a centralized gateway where you can add:

• semantic response caching
• model routing/fallbacks
• rate limits
• spend tracking
• multiple LLM providers
• a standard OpenAI-compatible API

LiteLLM explicitly supports a proxy/gateway architecture and multiple providers.

One important caveat: semantic caching can return an incorrect/stale answer if the similarity threshold is too permissive. I'd use it primarily for relatively deterministic workloads—FAQ, classification, extraction, RAG queries, etc.—and be much more conservative for personalized or time-sensitive requests.

If you tell me your stack (Python/Node), LLM providers, and whether you want hosted or self-hosted, I can recommend a specific proxy and give you the minimal production setup.

You can use LiteLLM Proxy, Portkey , or Langfuse to handle semantic caching and lower your LLM API expenses.

Semantic caching matches prompts by meaning and vector similarity rather than exact strings. This prevents duplicate queries with minor wording changes from hitting your paid provider again.

Top Options for Semantic Caching

• LiteLLM Proxy: An open-source proxy that translates standard LLM requests and connects easily to a Redis cache. You can configure it to handle standard caching, though strict semantic matching often requires pairing it with an embedding vector store.
• Portkey AI Gateway: A specialized gateway featuring built-in semantic caching . It transforms incoming prompts into embeddings, checks a vector store via cosine similarity, and returns previous responses if the match score exceeds your threshold.
• Gravitee API Management: Features an enterprise Semantic Cache Policy that maps inputs to vector databases right at the gateway level.

How Semantic Caching Works

1. Embedding Generation: The proxy converts an incoming prompt into a vector embedding.
2. Vector Search: It compares the new vector against previously stored vectors in a database (like Redis or Pinecone) using a metric like cosine distance.
3. Threshold Check: If the similarity score crosses your set limit (e.g., 0.92 ), the proxy returns the cached answer instantly instead of calling OpenAI or Anthropic.

If you want, tell me:

• Are you looking for an open-source self-hosted proxy or a managed cloud service?
• What vector database or cache (like Redis) do you already use?

I can help you set up the configuration file.

To implement semantic caching for your LLM calls, you need a reverse proxy or AI gateway that intercepts incoming prompts, converts them into vector embeddings, performs a vector similarity search, and either returns the cached answer or forwards the request to the upstream LLM provider.

The top open-source proxies and libraries providing semantic caching include:

• LiteLLM Proxy : An extremely popular, production-ready, OpenAI-compatible proxy. It natively supports redis-semantic and qdrant-semantic caching tiers. It handles load balancing, virtual keys, and drop-in replacements for 100+ LLM providers while checking vector similarity thresholds before hitting external APIs. Check out the setup steps in the LiteLLM Caching Documentation.
• GPTCache : Developed by Zilliz, this is a dedicated library/service specifically built to create semantic caches for LLM applications. It allows you to plug in various embedding models (like HuggingFace, OpenAI) and vector databases (Milvus, Chroma, Redis, FAISS) to sit as a caching layer right in front of your application logic or custom proxy setup. Learn more at the GPTCache GitHub Repository.
• PromptCache : A lightweight, self-hosted semantic cache built explicitly as a middle-tier service for GenAI workloads to cut down latency and token expenditure. It features runtime threshold configurations and SSE streaming support. Explore the code on the PromptCache GitHub Repository.
• Kong AI Gateway : If you are already running Kong for API management, it includes an AI Semantic Cache plugin that translates prompts into vector embeddings and caches responses natively at the gateway infrastructure layer.

If you want to narrow down the best choice, tell me:

• Are you looking for a standalone proxy container (like LiteLLM) or a Python library to embed directly in your code (like GPTCache)?
• Which vector database or cache backend (e.g., Redis, Qdrant) do you already use in your stack?

Yes. What you want is essentially an OpenAI-compatible LLM gateway/proxy with semantic response caching: the proxy embeds an incoming prompt, searches previous requests by similarity, and returns the stored response when the similarity exceeds a threshold.

My shortlist

• docs.semcache.io — Probably the closest match if your primary requirement is “a proxy whose main job is semantic caching.” It exposes HTTP/OpenAI-compatible routes and supports OpenAI, Anthropic, DeepSeek, Mistral, and custom upstreams.
• docs.litellm.ai — Best if you also want a self-hosted gateway for multiple providers, fallbacks, routing, budgets, etc. Its caching ecosystem supports semantic backends such as Redis + embeddings and Qdrant.
• portkey.ai — Good managed option. Its gateway supports semantic caching based on cosine similarity, with TTL configuration; semantic caching is aimed at production/enterprise deployments.
• helicone.ai — Worth considering if observability, cost tracking, and debugging are as important as caching. Its proxy supports caching, although its current direction is broader AI-gateway functionality.

What I'd choose

If you're building your own infrastructure, I'd start with:

Client → LiteLLM/Semcache → Redis/Qdrant → OpenAI/Anthropic/etc.

For a simple dedicated caching layer, Semcache is particularly interesting. For a broader production gateway, LiteLLM + Redis/Qdrant gives you more control.

One important design point: don't use semantic matching blindly. You generally want the cache key to incorporate things like model, system prompt/version, relevant generation parameters, tenant, and application/version, while the user prompt gets semantic matching. Otherwise you can return an old answer after changing your system prompt or model.

A typical flow is:

                         ┌── cache hit ──→ cached response
                         │
App → LLM Proxy → embed → similarity search
                         │
                         └── cache miss → LLM API
                                             │
                                             └→ store response + embedding

For factual or highly dynamic queries, I'd also use a lower similarity threshold or bypass semantic caching entirely; semantic caching is most valuable for repetitive workloads such as support questions, documentation Q&A, and common instructions. Research has found substantial reductions in redundant LLM calls in repetitive workloads, though the actual savings depend heavily on your traffic.

If you tell me (1) self-hosted vs managed, (2) OpenAI only vs multiple providers, and (3) Redis/Qdrant/Postgres available, I can recommend one architecture and give you a ready-to-run Docker setup.