Semantic Caching for LLMs: How It Works and the Tools That Do It

Semantic caching for LLMs matches an incoming request against previously cached responses using vector similarity, rather than exact-string comparison, so a rephrased duplicate query can be served from cache instead of triggering a new call to the model provider. This is different from a plain key-value cache, which only returns a hit when the new request is character-for-character identical to something already stored. Bifrost, the open-source AI gateway built in Go by Maxim AI, implements semantic caching at the gateway layer, so every provider behind it benefits from the same cache without any application-level integration work. This post covers how semantic caching works, what it costs in latency and infrastructure, and how the tools that implement it compare.

What Is Semantic Caching for LLMs

Semantic caching for LLMs is a technique that stores a model response alongside a vector embedding of the request, then serves that stored response when a new request's embedding falls within a similarity threshold of one already cached, even if the wording is different.

Most implementations, including Bifrost's semantic caching, actually combine two lookup paths:

• Direct (hash) matching: the request is normalized and hashed; an identical request is served instantly, with no embedding step involved.
• Semantic (similarity) matching: an embedding-based lookup that serves a cached answer when a new request is close enough to a previous one in vector space, even when the exact phrasing differs.

Bifrost runs direct matching first and falls back to semantic matching only on a direct miss, so exact repeats never pay the cost of an embedding call. Direct-only mode is available with no embedding provider configured at all, which is useful for workloads with stable, repeated prompts rather than varied paraphrasing.

Why Semantic Caching Matters for LLM Costs and Latency

Cutting duplicate model calls is the direct payoff, and the scale of that cut can be substantial. A study on semantic caching systems for LLM workloads measured cache hit rates between 61.6% and 68.8% across varied query categories, meaning a similar share of API calls never had to reach the model provider at all. Every one of those hits is a model call that never happens.

The practical benefits break down into three areas:

• Cost reduction: a cache hit replaces a full model call with a stored response, which is the largest lever available for workloads with repeated or paraphrased questions (FAQ bots, support assistants, internal knowledge tools).
• Lower latency: a cache read is a vector store round-trip, typically sub-millisecond to a few milliseconds with a local store, versus a multi-second round trip to a model provider.
• Reduced provider load: fewer calls per unique piece of information reduces exposure to provider-side rate limits during traffic spikes.

The tradeoff is that semantic lookups are not free. A semantic hit still costs one embedding call before the similarity search runs, and a semantic miss pays that embedding cost on top of the full model call that follows, which is why the direct-then-semantic ordering matters operationally.

How Semantic Caching Works

A semantic cache sits in the request path and follows the same general flow across most implementations:

1. A request arrives carrying a cache key (in Bifrost, a header or SDK context value). Requests without a cache key bypass the cache entirely.
2. Direct hash lookup runs first. If the normalized request matches a stored hash exactly, the cached response is served immediately.
3. On a direct miss, the request is embedded using a configured embedding model, and a vector similarity search runs against the cache store.
4. If the similarity score clears the configured threshold, the cached response is served. If not, the request proceeds to the model provider as normal.
5. The new response is written back to the cache asynchronously, so the first occurrence of a new question is never slowed down by the cache write itself.

Threshold tuning is the main operational lever: too loose a threshold returns answers for questions that are only superficially similar, while too tight a threshold misses obvious paraphrases and rarely produces a hit at all. Most teams tune this against a sample of real query logs before shipping to production rather than picking a value up front.

A vector store is required as the backing storage layer for both direct and semantic modes. Bifrost's vector store supports Redis and Valkey for in-memory deployments, along with Weaviate, Qdrant, and Pinecone for teams already standardized on one of those. Redis's own vector search documentation covers how K-nearest-neighbor lookups work at the storage layer, which is the same underlying mechanism a semantic cache relies on.

The Tools That Implement Semantic Caching

Semantic caching for LLMs shows up at three different layers of the stack, and which one fits depends on where the caching logic should live.

Gateway-native caching. Bifrost applies semantic caching as an AI gateway that already sits between an application and every model provider it uses. Because Bifrost is already in the request path for routing and failover, adding caching does not require a separate integration; it is a plugin that runs on the same traffic. This means every application behind the gateway shares one cache, cache hit and miss data shows up in the same observability dashboards as request volume and cost, and turning caching on or off is a configuration change rather than a code change. Bifrost supports both direct and semantic modes, per-model and per-provider cache scoping, and streaming responses cached and replayed chunk-by-chunk.

Library-level caching. GPTCache, an open-source project maintained by Zilliz, is the most common library-level option. It wraps directly into Python application code, with a modular architecture where the embedding model, vector store, and eviction policy can each be swapped independently. This fits teams that want caching logic to live inside a single application rather than at shared infrastructure, though each service running its own GPTCache instance means a separate cache per deployment rather than one shared cache across an organization's traffic.

Vector database infrastructure. Redis, Weaviate, Qdrant, and Pinecone are the storage layer underneath most semantic caching implementations, gateway-native or library-level. None of these products implement request routing, threshold logic, or cache-key composition on their own; they provide the vector index that a caching layer (like Bifrost or GPTCache) is built on top of.

The practical difference between these categories is where caching logic lives and how many places it has to be configured. A single organization running several services behind one gateway gets one cache and one place to tune it with a gateway-native approach; the same organization using a library-level approach configures caching separately in every service that needs it.

Key Considerations When Implementing Semantic Caching

A few details determine whether semantic caching helps or introduces new problems:

• Personalized or user-specific responses should not share a cache scope. If a prompt encodes user-specific context, two structurally similar requests can be semantically distinct, and a shared cache can return the wrong user's data.
• A similarity threshold has to be tuned, not assumed. The right value depends on the vocabulary and phrasing diversity of real traffic, and it should be validated against logged queries before enabling caching in production.
• TTL and cache-key composition affect correctness as much as hit rate. A cache key that ignores model or provider differences can serve a response generated by one model in place of another; Bifrost's semantic caching configuration includes per-model and per-provider cache-key composition for exactly this reason.
• Streaming responses need explicit support. Caching a streamed response requires replaying it chunk-by-chunk rather than caching only the final assembled output.

Teams evaluating tools for this should start from the Bifrost resources hub, which also covers how caching fits alongside the rest of an AI gateway's governance and routing capabilities. Cost reduction from caching also pairs directly with other gateway-level cost controls; the MCP Gateway covers a similar reduction achieved through tool-call token optimization rather than response caching.

Getting Started with Semantic Caching in Bifrost

Semantic caching for LLMs is one of the fastest ways to cut both cost and latency for applications with any amount of repeated or paraphrased traffic, and it works best when it sits at the same layer already handling provider routing and governance rather than as a separate integration per service. Bifrost deploys with zero configuration as a drop-in replacement for existing OpenAI or Anthropic SDK calls, so semantic caching can be enabled on existing traffic by changing a base URL rather than rewriting application code. To see semantic caching running alongside routing, failover, and cost governance in one gateway, book a demo with the Bifrost team.