Top 5 AI Gateways with Semantic Caching to Cut LLM API Calls
TL;DR: Semantic caching lets AI gateways recognize when incoming prompts mean the same thing as previous ones, even when worded differently, and return cached responses instead of making a new LLM API call. This cuts token spend and latency significantly. This article covers five AI gateways that support semantic caching: Bifrost, LiteLLM, Kong AI Gateway, TrueFoundry, and Cloudflare AI Gateway.
Every LLM API call costs tokens and adds latency. In production environments, a large chunk of those calls are semantically redundant. A support bot answering "How do I reset my password?" processes nearly identical intent whether the user types "password reset help" or "can't get into my account." Without semantic caching, each variation triggers a full inference cycle.
Traditional exact-match caching only catches character-identical prompts, which is rare in natural language. Semantic caching changes this by converting prompts into vector embeddings and comparing their meaning using cosine similarity. When a match exceeds a configured threshold, the cached response returns instantly without hitting the provider. Cache hits typically return in under 5 milliseconds compared to 2 to 5 seconds for a full model call. Even a 30 to 40 percent cache hit rate translates into meaningful cost savings and faster response times.
The best place to implement semantic caching is at the AI gateway layer. A centralized gateway ensures every request across all services benefits from a shared cache, improving hit rates as usage scales.
Here are five AI gateways that support semantic caching in 2026.
1. Bifrost
Bifrost is an open-source AI gateway built in Go by Maxim AI. It provides a single OpenAI-compatible API for 12+ providers including OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, Azure OpenAI, Cohere, Mistral, Groq, and Ollama.
Bifrost ships semantic caching as a first-class, built-in plugin with a dual-layer architecture. The first layer uses exact hash matching to return responses for identical prompts instantly with zero embedding overhead. The second layer performs vector similarity comparisons, recognizing semantically equivalent prompts and reusing previously generated outputs.
The system supports multiple vector database backends including Weaviate, Redis/Valkey, Qdrant, and Pinecone. Similarity thresholds are configurable (default 0.8) with the option to override values per request through HTTP headers. Cache scoping happens automatically by model and provider to avoid conflicts between different LLM configurations.
For teams dealing with multi-turn conversations, Bifrost includes configurable conversation thresholds that automatically skip caching when conversations exceed a set message count (default: 3 messages). This prevents false matches in extended dialogues where long histories create high semantic overlap between unrelated sessions.
An embedding-free direct hash mode is also available for scenarios where only exact-match deduplication is needed, eliminating the requirement for an embedding provider entirely.
Beyond caching, Bifrost brings automatic failbacks across providers and models, adaptive load balancing, native MCP gateway support for agentic workflows, virtual key budget management with hierarchical controls, and native Prometheus-based observability. It integrates directly with Maxim's AI evaluation and observability platform for end-to-end production monitoring, connecting cost data to trace monitoring, evaluation workflows, and quality dashboards.
Published benchmarks show 11 microseconds of overhead at 5,000 requests per second, making it the lowest-latency AI gateway currently available. Deployment takes under 30 seconds with zero configuration via npx -y @maximhq/bifrost or Docker.
Best for: Teams running high-throughput production workloads that need sub-millisecond overhead, self-hosted deployment, built-in semantic caching, and integrated observability without stitching together separate tools.
2. LiteLLM
LiteLLM is an open-source Python-based proxy that standardizes access to 100+ LLM providers through a unified OpenAI-compatible interface.
Overview: LiteLLM provides semantic caching through Redis or Qdrant-based vector search. Developers can configure redis-semantic or qdrant-semantic cache modes, which compare prompt embeddings to identify semantically similar queries. Similarity thresholds and TTL settings are adjustable depending on accuracy requirements.
Features: Supports seven cache backends including in-memory, disk, Redis, S3, GCS, and Qdrant. Offers a dual-cache design with L1 in-memory and L2 Redis tiers. Per-request cache control through headers and namespaces. Virtual key management, spend tracking, and rate limiting included.
Best for: Python-centric teams that need wide provider coverage and quick LLM call unification for development and moderate-scale production. Semantic caching requires external vector databases and embedding services, which adds operational complexity compared to gateways with built-in caching layers.
3. Kong AI Gateway
Kong AI Gateway extends the well-known Kong API management platform with AI-specific plugins for model routing, semantic caching, and prompt control.
Overview: Since version 3.8, Kong has introduced semantic intelligence capabilities powered by vector databases. The AI Semantic Cache plugin generates embeddings for incoming prompts and stores them in a vector store like Redis. New prompts get compared against stored vectors to find semantically similar requests. Kong reports that cache hits can reduce response latency by up to 20x.
Features: Semantic caching via dedicated plugin with configurable similarity thresholds. Semantic routing that analyzes prompt content to determine the best model for a request. Supports OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, and Mistral. Load balancing with health checks and circuit breaking.
Best for: Enterprises already standardized on Kong for API management that want to consolidate traditional API and LLM traffic governance under a single platform. Teams without existing Kong infrastructure may find the setup complexity significant for AI-only use cases.
4. TrueFoundry
TrueFoundry is a full-stack ML platform that includes an LLM gateway as part of its broader infrastructure for deploying and monitoring AI applications. It was recognized in the 2025 Gartner Market Guide for AI Gateways.
Overview: TrueFoundry integrates semantic caching as a centralized gateway capability. The gateway generates embeddings for incoming prompts, performs similarity lookups against a vector index, and returns cached responses when the match crosses a configured threshold. All LLM traffic benefits from a shared cache without requiring changes to application logic.
Features: Exact-match and semantic caching with configurable thresholds, TTLs, and namespaces. Centralized control applied consistently across environments. Supports both self-hosted and external models. Handles 350+ RPS on a single vCPU with 3 to 4 ms latency overhead. Includes automatic retries, failover, rate limiting, and load balancing.
Best for: Teams that need an end-to-end ML platform combining model deployment, serving, and gateway capabilities in a single solution, particularly organizations with on-premise or data sovereignty requirements.
5. Cloudflare AI Gateway
Cloudflare AI Gateway is a fully managed service that runs on Cloudflare's global edge network with 250+ points of presence worldwide.
Overview: Cloudflare provides exact-match caching from its edge network with configurable TTL and per-request cache control. It supports 20+ providers with real-time analytics and cost tracking. Core features including dashboard analytics, caching, rate limiting, and basic logging are available for free on all Cloudflare plans. Note that Cloudflare currently supports exact-match caching only, not embedding-based semantic caching.
Features: Zero infrastructure management. Real-time logging and usage analytics. Token-based authentication and API key management. Unified billing for third-party model usage through Cloudflare invoices. Custom metadata tagging for filtering.
Best for: Teams already in the Cloudflare ecosystem that need lightweight, free observability and caching for AI traffic with limited prompt variability. Organizations needing semantic similarity matching or self-hosted deployment will need to look elsewhere.
Which Gateway Should You Pick?
The right choice depends on where your primary pain point sits. If you need the lowest possible overhead with built-in semantic caching that works out of the box, Bifrost is the strongest open-source option. LiteLLM works well for Python-heavy teams that want maximum provider breadth and are comfortable managing external dependencies. Kong fits naturally for organizations already running Kong for API management. TrueFoundry is the play for teams wanting caching tightly integrated with model deployment and serving. And Cloudflare is the fastest on-ramp for teams that just need basic exact-match caching with zero infrastructure.
Whichever gateway you choose, pairing it with a robust observability layer is critical. Cache hit rates, latency distributions, and cost-per-request metrics need continuous monitoring to ensure your caching strategy delivers real value in production.
