AI Gateway

Top AI Gateways for Semantic Caching in 2026

Top AI Gateways for Semantic Caching in 2026

As LLM-powered applications move into production, inference costs and response latency become two of the most pressing infrastructure challenges. Every API call to a model provider consumes tokens and adds latency, and users rarely phrase the same question identically. Traditional exact-match caching fails to address this because natural language queries are almost never worded the same way twice.

Semantic caching solves this by matching requests based on meaning rather than exact text. When a user asks "What is TCP?" and another asks "Explain the TCP protocol," a semantic cache recognizes the shared intent and serves a cached response instead of making a redundant model call. Implemented at the gateway layer, this technique can reduce inference costs by 40% to 70% while cutting response times from hundreds of milliseconds down to single-digit latencies.

This guide reviews the top AI gateways that support semantic caching, comparing their architectures, flexibility, and production readiness.

What Makes Semantic Caching Different from Traditional Caching

Standard caching relies on exact string matching. If the prompt text is identical, the cache returns a stored response. In practice, this yields very low hit rates for conversational and search-oriented AI applications because users phrase requests differently each time.

Semantic caching introduces a vector similarity layer. Each incoming prompt is converted into an embedding, and the cache checks whether a stored embedding falls within a configurable similarity threshold. If the match is strong enough, the cached response is returned without calling the LLM provider. The core workflow looks like this:

  • The gateway receives an incoming prompt from the application
  • An embedding model converts the prompt into a high-dimensional vector
  • The vector is compared against stored embeddings in a vector database
  • If similarity exceeds the threshold, the cached response is served instantly
  • If no match is found, the request is forwarded to the model provider and the new response is stored for future lookups

This approach works particularly well for customer support bots, knowledge base applications, search assistants, and agentic systems where similar questions surface repeatedly.

Top AI Gateways with Semantic Caching Support

Bifrost is an open-source, high-performance AI gateway written in Go that unifies access to 20+ providers through a single OpenAI-compatible API. In sustained benchmarks at 5,000 requests per second, Bifrost adds only 11 microseconds of overhead per request, making it one of the fastest AI gateways available.

Bifrost's semantic caching plugin implements a dual-layer caching strategy that sets it apart from other gateways:

  • Exact hash matching (Layer 1): Identical prompts are matched instantly with zero embedding overhead, consuming no additional tokens
  • Vector similarity search (Layer 2): Semantically similar prompts are matched using configurable similarity thresholds (default 0.8), catching the long tail of rephrased queries
  • Multiple vector store backends: Supports Weaviate, Redis, Qdrant, and Pinecone, giving teams flexibility to use their existing vector infrastructure
  • Per-request overrides: TTL, similarity threshold, and cache type can all be overridden on a per-request basis via HTTP headers (x-bf-cache-ttl, x-bf-cache-threshold, x-bf-cache-type)
  • Direct hash mode: For teams that only need exact-match deduplication without an embedding provider, Bifrost supports an embedding-free direct hash mode that eliminates embedding costs entirely
  • Model and provider isolation: Cache entries are scoped per model and provider combination, preventing cross-contamination of cached responses
  • Full streaming support: Cached streaming responses are served with proper chunk ordering, so applications using streaming APIs see no behavioral difference between cache hits and live responses

Beyond caching, Bifrost provides automatic fallbacks across providers, intelligent load balancing, virtual key governance with budgets and rate limits, an MCP gateway for tool execution, and a custom plugin architecture for extensibility. Enterprise features include guardrails, adaptive load balancing, clustering for high availability, vault-backed key management, and audit logs.

For teams evaluating Bifrost, you can book a demo or explore the open-source repository on GitHub.

2. Kong AI Gateway

Kong AI Gateway extends the widely adopted Kong API Gateway platform with AI-specific plugins for model routing and caching. Since version 3.8, Kong has included an AI Semantic Cache plugin that generates embeddings for incoming prompts and stores them in a vector database such as Redis. When a new prompt arrives, the gateway compares its embedding against stored vectors and returns cached responses if the similarity threshold is met.

  • Builds on top of a mature API gateway ecosystem with broad plugin support
  • Supports Redis as the primary vector store for cached embeddings
  • Integrates with Kong's existing rate limiting, authentication, and analytics features
  • Available as self-hosted or managed SaaS through Kong Konnect

Kong is a strong option for organizations already running Kong for traditional API management that want to extend it to AI workloads. However, it lacks Bifrost's dual-layer caching strategy and per-request cache control granularity.

3. Cloudflare AI Gateway

Cloudflare AI Gateway provides a unified interface for connecting to major providers including OpenAI, Anthropic, Google, and more. It offers caching mechanisms to reduce redundant model calls along with rate limiting, request retries, and real-time analytics.

  • Leverages Cloudflare's global edge network for low-latency cache retrieval
  • Provides real-time analytics covering request counts, token usage, and costs
  • Supports model fallback for reliability
  • Comprehensive logging with capacity for up to 100 million logs

Cloudflare's gateway excels as a lightweight, managed solution for teams that want quick setup without managing infrastructure. Its caching capabilities, however, are more focused on exact-match patterns and lack the depth of vector similarity configuration that Bifrost offers.

4. TrueFoundry AI Gateway

TrueFoundry AI Gateway is a commercially available gateway that supports semantic caching alongside model routing and access control. It is designed for production workloads and emphasizes performance, handling 350+ requests per second on a single vCPU.

  • Supports semantic caching with embedding-based similarity matching
  • Includes routing, access control, and observability in a unified package
  • Focuses on Kubernetes-native deployment patterns
  • Offers RAG pipeline automation and PII sanitization at the gateway layer

TrueFoundry is a solid choice for teams already invested in its MLOps ecosystem, but it is a commercial product without the open-source flexibility that Bifrost provides.

5. Azure API Management (APIM) with Semantic Caching

For organizations running Azure infrastructure, Azure API Management offers semantic caching through dedicated policies (azure-openai-semantic-cache-lookup and azure-openai-semantic-cache-store). This approach uses Azure Redis Enterprise with the RediSearch module for vector similarity search.

  • Tight integration with Azure OpenAI Service and Azure infrastructure
  • Uses managed identity authentication for secure, credential-free access
  • Provides built-in visualization for cache hit/miss metrics
  • Leverages Azure's enterprise compliance and security controls

This option works well for Azure-first organizations but locks you into the Azure ecosystem and lacks the provider-agnostic flexibility of Bifrost's multi-backend approach.

How to Choose the Right Gateway for Semantic Caching

When evaluating AI gateways for semantic caching, consider these factors:

  • Caching depth: Does the gateway support both exact-match and semantic similarity, or only one? Dual-layer approaches like Bifrost's yield higher cache hit rates across diverse query patterns
  • Configuration granularity: Can you override TTL, thresholds, and cache behavior per request? This matters for applications with mixed query types
  • Vector store flexibility: Being locked into a single vector database limits future architecture choices
  • Performance overhead: Every millisecond the gateway adds to the request path reduces the latency benefit of caching
  • Open source vs. managed: Open-source gateways give you full control over data residency and customization, while managed solutions trade flexibility for operational simplicity

Getting Started with Bifrost

Bifrost's combination of dual-layer caching, sub-microsecond gateway overhead, multi-backend vector store support, and per-request cache control makes it the most complete option for teams serious about reducing LLM inference costs through semantic caching. Its open-source foundation ensures full transparency and extensibility, while enterprise features cover the governance and security requirements of production deployments.

To see how Bifrost's semantic caching and AI gateway capabilities can optimize your LLM infrastructure, book a demo today.

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