Reducing LLM Cost and Latency Using Semantic Caching

Running large language models in production quickly exposes two operational realities: every request costs money, and every request introduces latency. In applications where users repeatedly ask similar questions, a meaningful share of those model calls are unnecessary.

Semantic caching addresses this inefficiency by returning previously generated responses when new queries are meaningfully similar to earlier ones. Instead of requiring identical phrasing, semantic caching recognizes shared intent between requests. This allows applications to respond faster while significantly reducing LLM API usage.

In this guide, we explain how semantic caching works, when it is most useful, and how to implement it with Bifrost, the open-source AI gateway designed for production LLM infrastructure.

The Rising Cost and Latency of LLM Applications

As AI systems scale, two operational pressures become increasingly visible: infrastructure cost and response latency.

Rising API spend is usually the first signal. LLM providers price their APIs based on token usage, meaning every request directly contributes to operating costs. In high‑traffic environments processing thousands of queries per hour, even small inefficiencies can translate into significant monthly spend.

Latency constraints present the second challenge. Most LLM responses require anywhere from one to several seconds to generate. For interactive applications such as copilots, support bots, or search assistants, this delay can noticeably impact the user experience.

A third issue often emerges in production: duplicate or near‑duplicate queries. Real traffic frequently contains multiple variations of the same question. Without an intelligent caching layer, each of these requests triggers a full model inference, consuming both compute time and budget.

Traditional caching mechanisms only help in limited situations because they rely on exact text matches. Slight variations like "What is your refund policy?" versus "How can I return an item?" would bypass an exact-match cache despite asking the same thing. Semantic caching solves this limitation.

Understanding Semantic Caching

Semantic caching works by comparing the meaning of requests rather than their literal wording. Instead of matching raw text strings, the system converts each query into a numerical embedding vector and compares it to vectors from previously cached requests.

A similarity metric—typically cosine similarity—is then used to determine whether two queries are close enough in meaning. If the similarity score crosses a predefined threshold, the system returns the cached response instead of calling the LLM again.

This approach enables several advantages:

• Intent-aware matching that detects meaning rather than identical wording
• Extremely fast lookups that return responses far faster than model inference
• Configurable similarity thresholds to balance accuracy and cache hit rate
• Streaming compatibility, allowing cached outputs to be delivered in the same format as live responses

Because embedding comparisons are lightweight compared to full model inference, semantic caching can dramatically improve both cost efficiency and response time.

How Bifrost Enables Semantic Caching

The semantic caching plugin in Bifrost provides a production-ready implementation that combines two complementary caching strategies.

Two-Layer Caching Strategy

When a request reaches Bifrost, it passes through two caching checks.

The first stage performs an exact hash lookup. If the request perfectly matches a previously cached query, the stored response is returned immediately.

If no exact match exists, Bifrost performs a semantic similarity search. The system generates an embedding for the incoming request and searches a vector store for previously cached queries that exceed the configured similarity threshold.

This layered approach allows the system to process identical requests at maximum speed while still capturing benefits from semantically similar queries.

Supported Vector Databases

To support semantic similarity search, Bifrost integrates with several widely used vector databases:

• Weaviate – a scalable vector database with gRPC support
• Redis – an in-memory store with RediSearch vector capabilities
• Qdrant – a Rust-based vector search engine optimized for filtering
• Pinecone – a fully managed serverless vector database service

These integrations allow teams to deploy semantic caching using infrastructure that fits their existing architecture.

Request-Level Cache Controls

One of the design goals of Bifrost is flexibility. Developers can adjust caching behavior dynamically through HTTP headers for each request.

Key headers include:

• x-bf-cache-key – enables caching for the request
• x-bf-cache-ttl – overrides the default cache expiration time
• x-bf-cache-threshold – defines the similarity threshold used for matching
• x-bf-cache-type – selects between direct or semantic caching
• x-bf-cache-no-store – retrieves cached responses without storing new ones

This level of control allows different endpoints or user groups to follow distinct caching strategies without modifying the gateway configuration.

Direct Hash Mode (Embedding-Free Caching)

Not every workload requires semantic matching. Some systems mainly generate identical requests where exact-match deduplication is sufficient.

For these scenarios, Bifrost provides a direct hash mode that eliminates the need for embeddings entirely.

Direct hash mode is useful when:

• Requests are frequently identical
• Embedding generation costs need to be avoided
• Ultra-low latency is required
• The application runs automated pipelines or batch workloads

When no embedding provider is configured, Bifrost automatically defaults to this direct caching approach.

Handling Conversational Context

Caching becomes more complex when dealing with multi-turn conversations. Long message histories can introduce high semantic similarity across unrelated sessions, which can cause incorrect cache matches.

Bifrost addresses this with conversation-aware configuration options.

• History thresholds allow caching to be skipped once conversations exceed a defined number of messages (default: three). This reduces the risk of false positives in long interactions.
• System prompt controls determine whether system prompts should influence cache key generation. Excluding them can improve reuse when prompt variations do not materially change the output.

These safeguards make semantic caching safer to deploy in chatbot or agent-based systems.

Observability and Cache Management

Understanding how the cache behaves is essential for optimization. Bifrost includes built-in observability capabilities that expose detailed metadata for each request.

Teams can monitor:

• Whether a response was served from cache
• Whether the match came from direct hashing or semantic similarity
• The similarity score between the request and cached entry
• Token usage related to embedding generation

Cache lifecycle management features include:

• TTL-based automatic expiration
• Manual cache invalidation by request ID or cache key
• Namespace isolation between Bifrost instances
• Optional cleanup for ephemeral deployments

These tools make it easier to monitor performance and continuously tune caching behavior.

Production Best Practices for Semantic Caching

Teams deploying semantic caching in production typically follow several practical guidelines:

• Begin with a similarity threshold around 0.8. This provides a good balance between accuracy and cache hit rate. Increase it for precision-sensitive workloads or decrease it for more aggressive caching.
• Choose TTLs based on content volatility. Dynamic responses benefit from short expiration windows (30 seconds to a few minutes), while static information can be cached for hours or days.
• Combine caching with reliability features such as fallbacks and load balancing to build a resilient LLM infrastructure layer.
• Continuously monitor hit rates and similarity scores to refine thresholds based on real traffic patterns.
• Keep conversation thresholds conservative for agent systems to avoid incorrect cache reuse.

Applying these practices ensures semantic caching delivers meaningful savings without degrading output quality.

Cutting LLM Costs Without Sacrificing Performance

Semantic caching is one of the most effective ways to reduce both cost and latency in large-scale LLM deployments. By detecting semantically equivalent queries, systems can avoid unnecessary model calls while still returning accurate responses.

With its dual-layer caching architecture, flexible request-level configuration, and integrations with production vector databases, Bifrost provides a practical way to implement semantic caching in real-world AI systems.

Whether powering chatbots, AI copilots, or complex agent workflows, semantic caching can deliver measurable improvements in efficiency from the very beginning.

To see how this works in practice, you can book a demo with Bifrost.