Semantic Caching: Stop Paying for Repeated LLM Queries

Yuki Haramoto — Thu, 02 Jul 2026 16:59:46 +0000

Many LLM requests are semantically identical, costing enterprises millions in redundant token usage. Bifrost implements semantic caching to reduce LLM costs and latency by reusing responses to similar prompts.

Every interaction with a large language model (LLM) incurs a cost, typically measured per token, and introduces latency. For applications with high request volumes or frequently asked questions, many of these queries are semantically similar, if not identical, even if their exact wording differs. This redundancy translates directly into unnecessary expenditures and slower user experiences. To address this, many engineering teams are adopting semantic caching, an advanced technique that stores and reuses responses based on meaning rather than exact text matching.

What is Semantic Caching?

Semantic caching extends traditional caching mechanisms by understanding the underlying meaning of a query rather than just its literal string. While a traditional cache stores responses for exact text matches, a semantic cache processes incoming queries to understand their intent. If a semantically similar query has been processed before, and its response cached, the system can return the cached response, bypassing a call to the LLM. This distinction is critical for LLMs, which are often queried with rephrased questions that convey the same underlying meaning. For example, "What is semantic caching?" and "Tell me about semantic caching" are different text strings but semantically identical requests.

The Hidden Costs of Redundant LLM Inferences

The operational costs associated with LLM usage can quickly escalate, especially for enterprises. Each API call to a provider, whether OpenAI, Anthropic, or others, consumes tokens and contributes to billing. As AI applications scale, even minor overlaps in query intent can lead to substantial redundant spending. A study by MosaicML highlighted that LLM inference costs can account for up to 90% of a company's total LLM spending. Beyond direct monetary costs, repeated inferences introduce unnecessary latency, degrading the user experience, and consume valuable API rate limits, which can lead to service disruptions during peak demand.

How Semantic Caching Works

At its core, semantic caching operates on the principle of vector embeddings and similarity search. When a new query arrives, the system first converts it into a numerical vector representation, known as an embedding. This embedding captures the semantic meaning of the query in a high-dimensional space. The system then searches a dedicated vector database for existing query embeddings that are sufficiently "close" or similar to the new query's embedding.

If a cached query's embedding falls within a predefined similarity threshold, the corresponding cached response is retrieved and returned immediately, avoiding an LLM call. If no sufficiently similar query is found, the new query is forwarded to the LLM. Upon receiving the LLM's response, both the new query's embedding and its response are stored in the cache for future use. This process ensures that subsequent semantically similar queries benefit from the prior computation.

Implementing an effective semantic cache requires careful consideration of the embedding model, the similarity metric, and the threshold for determining a cache "hit." Techniques such as cache invalidation (e.g., time-to-live or least recently used) and cold-start strategies are also vital for maintaining cache freshness and performance.

Implementing Semantic Caching with Bifrost

For organizations seeking to optimize LLM operations, tools like Bifrost, an open-source AI gateway from Maxim AI, offer integrated semantic caching capabilities designed for production environments. Bifrost acts as a unified entry point to various LLM providers, allowing teams to consolidate their AI infrastructure and apply policies consistently.

Bifrost's semantic caching feature can be configured to reduce costs and latency on repeated queries by storing and reusing responses based on semantic similarity. This is particularly beneficial for applications where users frequently ask similar questions. By simply enabling the feature, organizations can start seeing immediate reductions in token consumption and improvements in response times without modifying their application code. The gateway handles the embedding generation, similarity search, and cache management transparently.

Beyond semantic caching, Bifrost provides a comprehensive suite of features essential for robust AI applications. Its automatic fallbacks ensure service continuity during provider outages, while intelligent load balancing optimizes request distribution across API keys and providers. Bifrost also enables robust governance with virtual keys, budgets, and rate limits, along with sophisticated guardrails for content safety. These controls are not limited to gateway traffic; Bifrost Edge extends this same governance and security to AI traffic on employee machines, with endpoint enforcement on each device, effectively combating shadow AI.

The benchmarks published by Bifrost demonstrate its minimal overhead, adding only 11 microseconds per request at 5,000 requests per second. This low-latency profile means that even when a cache miss occurs, the overhead of routing through Bifrost remains negligible, preserving overall application performance.

Key Benefits for Production AI Applications

Implementing semantic caching, particularly through an AI gateway like Bifrost, offers several critical advantages for production AI applications:

Significant Cost Reduction: By avoiding redundant LLM calls, organizations can drastically cut down on token usage and associated API costs.
Improved Latency: Cache hits deliver near-instantaneous responses, dramatically reducing the end-to-end latency for frequently accessed information.
Increased Throughput: Offloading requests from LLM providers through caching allows applications to handle a higher volume of queries without hitting rate limits or requiring additional model capacity.
Enhanced Resilience: Reduced reliance on external LLM APIs for common queries makes applications more resilient to provider outages or performance degradation.

Considerations for Adopting Semantic Caching

While highly beneficial, semantic caching requires careful planning. Teams must consider the optimal similarity threshold to balance cache hit rates with response accuracy. Too low a threshold might lead to irrelevant cached responses, while too high a threshold could negate the cost-saving benefits. Cache invalidation strategies, such as time-to-live (TTL) or a least recently used (LRU) policy, are crucial to ensure that responses remain fresh and relevant as underlying data or models evolve. Additionally, choosing an appropriate embedding model is vital, as its quality directly impacts the cache's effectiveness.

Next Steps

Semantic caching represents a powerful strategy for optimizing LLM performance and cost efficiency in production. By intelligently reusing responses to semantically similar queries, organizations can significantly reduce expenses, lower latency, and build more resilient AI applications. Teams evaluating AI gateways to implement such advanced features can request a Bifrost demo or review the open-source repository.

DEV Community: Yuki Haramoto