Top 5 AI Gateways with Semantic Caching for LLM Cost Reduction

LLM API spend across production applications typically includes a substantial share of semantically redundant queries, questions that are paraphrased versions of ones already answered minutes or hours earlier. Semantic caching at the gateway layer intercepts these before they reach any provider, serving stored responses without incurring a new API call. Bifrost, the Go-based open-source AI gateway built by Maxim AI, implements semantic caching natively with a configurable vector store backend, giving enterprise teams a centralized cache that spans providers and consumers from a single control point.

What Makes Semantic Caching Effective for LLM Cost Reduction

Semantic caching uses vector embeddings to represent incoming queries as numerical vectors and compares them against stored query vectors using similarity search. When a new request's similarity score meets or exceeds a configured threshold, the gateway serves the previously cached response instead of forwarding the request to the LLM provider. This approach catches paraphrases, rewordings, and synonym-substituted variants of the same underlying question, not just exact string matches. It is most effective in support bots handling repeated customer questions, FAQ agents, document analysis pipelines where similar documents recur, and code review workflows where the same patterns appear across files.

What to Look for in an AI Gateway's Semantic Cache

Before comparing specific AI gateways with semantic caching, evaluate each option against these criteria:

• Similarity threshold configuration: the ability to tune how aggressively the cache matches, controlling the trade-off between hit rate and response accuracy
• Cross-provider caching: a cached response from an OpenAI call should be servable when a semantically identical query arrives for an Anthropic or Bedrock endpoint
• TTL and cache invalidation controls: per-entry expiry ensures stale responses are not served when underlying data has changed
• Cache hit metrics and observability: hit rate, miss rate, and latency impact must be visible for teams to justify and tune the cache
• Integration with routing and governance: per-consumer cache policies (where some virtual keys cache aggressively and others bypass the cache) require the cache to be aware of the governance layer

1. Bifrost

Bifrost implements semantic caching through a vector store backend that supports Redis/Valkey, Weaviate, Qdrant, and Pinecone. The implementation runs two complementary lookup paths: a direct hash match for exact query repetitions (served without any embedding call), and an embedding-based similarity search that activates on direct-match misses. Similarity thresholds are configurable per deployment, and cache writes are asynchronous, the first request for any query returns immediately from the provider while the response is stored in the background.

Cross-provider cache applicability means a response cached from an OpenAI call can be served when the same semantically equivalent query arrives via Anthropic or Bedrock endpoints, provided the same cache key is in scope. This makes Bifrost's semantic caching immediately effective in multi-provider deployments without per-provider cache configuration.

Virtual keys integrate directly with the cache layer, enabling per-consumer cache behavior. Teams can configure specific virtual keys to bypass the cache (for latency-sensitive or audit-required workloads), use direct-only caching, or enable full semantic matching, all controlled through the same governance layer that manages budget and rate limits.

For MCP-heavy agentic workloads, Code Mode addresses token cost reduction through a different mechanism: instead of caching responses, it reduces input token volume by up to 92.8% by replacing large tool-definition catalogs with four compact meta-tools that orchestrate all connected MCP servers. Across benchmarks with 508 tools across 16 MCP servers, Code Mode brought average input tokens per query from 1.15M to 83K. This pairs with semantic caching to reduce costs across both LLM completions and agentic tool-calling workloads. The MCP Gateway resource page and the Code Mode benchmark writeup detail these results.

Bifrost adds 11 microseconds of overhead per request at 5,000 RPS, meaning the cache layer does not introduce meaningful latency on cache hits. Enterprise deployments on the Bifrost Enterprise page include VPC isolation, HA clustering, RBAC, and immutable audit logs for compliance with SOC 2, GDPR, HIPAA, and ISO 27001 requirements.

Best for: Bifrost is built for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. It serves as a centralized AI gateway to route, govern, and secure all AI traffic across models and environments with ultra low latency. Bifrost unifies LLM gateway, MCP gateway, and Agents gateway capabilities into a single platform. Designed for regulated industries and strict enterprise requirements, it supports air-gapped deployments, VPC isolation, and on-prem infrastructure. It provides full control over data, access, and execution, along with robust security, policy enforcement, and governance capabilities.

2. AWS Bedrock with Knowledge Base Caching

AWS Bedrock provides response caching integrated into the Bedrock managed AI service. Teams building entirely within AWS can configure caching through Bedrock Knowledge Bases, which store and retrieve responses within the AWS ecosystem.

Best for: Organizations running AI workloads natively on AWS who want response caching integrated into the Bedrock ecosystem without deploying a separate gateway.

Limitations: Caching is scoped to Bedrock, no cross-provider applicability; per-consumer budget governance is not available; semantic (vector-based) caching requires additional configuration with Bedrock Knowledge Bases rather than being natively built into the gateway layer.

3. Azure API Management with Response Caching

Azure API Management (APIM) can be extended with response caching policies for AI endpoints, making it a candidate for enterprises already operating Azure OpenAI deployments through an existing APIM infrastructure.

Best for: Enterprises using Azure OpenAI who want to extend existing APIM deployments with basic response caching for AI endpoints, keeping infrastructure consolidated within the Azure ecosystem.

Limitations: Azure APIM's built-in caching is exact-match by default. Implementing semantic similarity matching requires custom policy development, an external embedding infrastructure, and a separate vector store, none of which are provided natively. Per-consumer cache governance is not available.

4. Kong AI Gateway with AI Cache Plugin

Kong AI Gateway provides an AI Cache plugin for LLM request caching as part of the Kong Enterprise API gateway product. Teams with an existing Kong deployment can add LLM response caching alongside their existing API management configuration.

Best for: Teams with an existing Kong Enterprise API gateway who want consistent tooling across all API types including AI endpoints. Kong's AI Cache plugin provides response caching for LLM requests within a familiar operational framework.

Limitations: Semantic caching capabilities depend on plugin version and backend configuration. Per-consumer cache governance and MCP token reduction via Code Mode-equivalent tooling are not natively available. Teams need to evaluate whether semantic (vector-based) similarity matching is supported in their specific Kong deployment.

5. Self-Hosted Vector Cache (Redis + Embedding Model)

The fully custom approach: deploy Redis as a vector store, run an embedding model to generate query vectors, and build cache lookup logic into the application layer or into a thin middleware gateway. Teams write the similarity-threshold logic, TTL management, and cache invalidation themselves.

Best for: Platform engineering teams with the capacity to build and operate a custom caching solution who need maximum control over similarity thresholds, cache backends, and data locality constraints.

Limitations: Significant engineering and operational overhead, teams must build monitoring, cache invalidation logic, embedding pipeline management, and failure recovery independently. No bundled routing, governance, fallback chains, or MCP support. Operational burden remains entirely with the team indefinitely.

Semantic Caching Feature Comparison

Choosing an AI Gateway for Semantic Caching

Among the AI gateways with semantic caching evaluated here, Bifrost is the only option with native vector-based semantic caching built directly into the gateway, cross-provider cache applicability, per-consumer cache governance through virtual keys, and Code Mode for MCP token reduction. The LLM Gateway Buyer's Guide provides a detailed capability matrix for teams evaluating AI gateway options beyond caching alone.

For teams in regulated industries, the Bifrost governance resource page covers how virtual keys, budget controls, audit logs, and guardrails work together as a unified governance layer, the same layer that controls cache behavior per consumer. Self-hosted approaches and cloud-native caching integrations can address specific constraints, but they require teams to build and maintain the surrounding infrastructure that Bifrost provides out of the box.

Semantic caching is most effective when it operates at the gateway layer with full visibility into all LLM traffic, access to consumer identity for per-key policies, and integration with the routing logic that decides which provider handles each request. Bolt-on caching layers outside the gateway miss that context.

Reduce LLM Costs with Bifrost

Bifrost combines gateway-level semantic caching, cross-provider cache applicability, per-consumer cache governance, and Code Mode for MCP token reduction in a single open-source platform. For enterprise teams evaluating AI gateways with semantic caching as part of a broader cost reduction strategy, book a demo with the Bifrost team to see how the full platform fits your infrastructure.