Semantic Caching for LLMs: How to Cut Token Spend with AI Gateways

TL;DR

Semantic caching matches LLM requests by meaning rather than exact text, enabling AI gateways to serve cached responses for semantically similar prompts. This can reduce token spend and latency dramatically. This article breaks down how semantic caching works at the gateway layer, then compares five platforms: Bifrost, Cloudflare AI Gateway, LiteLLM, Kong AI Gateway, and TrueFoundry LLM Gateway.

Production LLM applications have a recurring cost problem: a large portion of the requests sent to model providers are semantically redundant. A customer support bot answering "How do I reset my password?" processes nearly identical intent whether the user types "password reset help," "I forgot my login credentials," or "can't get into my account." Each variation triggers a fresh API call, burns tokens, and adds latency.

Traditional exact-match caching only helps when prompts are character-for-character identical, which is rare in natural language. Semantic caching solves this by comparing the meaning of incoming requests against previously cached ones using vector embeddings and similarity search. When a match exceeds a configurable similarity threshold, the cached response is returned instantly and no LLM call is made.

The impact is significant. Cache hits typically return in under 5 milliseconds compared to 2-5 seconds for a full inference call. Even a modest cache hit rate of 30-40% translates into meaningful cost savings and a noticeably faster user experience.

How It Works at the Gateway Layer

The most effective 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.

The typical flow involves converting the incoming prompt into a vector embedding, running a similarity search against stored embeddings in a vector database, evaluating whether cosine similarity exceeds a defined threshold (commonly 0.90-0.98), and either returning the cached response or forwarding the request to the LLM provider and caching the new result.

The key tuning parameter is the similarity threshold. A strict threshold (0.98) minimizes false positives but limits hit rates. A relaxed threshold (0.85) maximizes savings but risks returning generic answers for subtly different queries.

1. Bifrost

Platform Overview

Bifrost is a high-performance, open-source AI gateway built in Go by Maxim AI. It unifies access to 15+ providers (OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, and more) through a single OpenAI-compatible API, delivering approximately 11 microseconds of gateway overhead at 5,000 requests per second.

Features

Bifrost ships semantic caching as a first-class, built-in plugin with a dual-layer system: exact hash matching plus vector similarity search. Cache hits return in roughly 5 milliseconds, and the system supports multiple vector store backends including Weaviate, Redis/Valkey, Qdrant, and Pinecone. Teams can tune the similarity threshold per use case, and governance features enable multi-tenant cache isolation using tenant or user IDs, preventing data leakage in SaaS deployments. Cached responses also support full streaming with proper chunk ordering.

Beyond caching, Bifrost provides automatic fallbacks, adaptive load balancing, MCP gateway support, budget management with virtual keys, and native Prometheus-based observability. It integrates directly with Maxim's AI evaluation and observability platform for end-to-end production monitoring.

Best For

Teams running high-throughput production workloads that need sub-millisecond overhead, self-hosted deployment, and built-in semantic caching without managing a separate embedding pipeline. Get started in seconds with npx -y @maximhq/bifrost or via GitHub.

2. Cloudflare AI Gateway

Platform Overview

Cloudflare AI Gateway is a managed proxy service that leverages Cloudflare's global edge network to add caching, rate limiting, retries, and analytics with a single line of code.

Features

Provides exact-match caching from its edge network with configurable TTL and per-request cache control. Supports 20+ providers with real-time analytics and cost tracking. Core features are free on all plans. However, Cloudflare currently does not support semantic caching; only character-identical requests trigger cache hits.

Best For

Teams already on Cloudflare that need lightweight, free observability and caching for AI traffic with limited prompt variability.

3. LiteLLM

Platform Overview

LiteLLM is an open-source Python-based gateway providing unified access to 100+ LLM providers through OpenAI-compatible APIs.

Features

Supports exact-match caching via Redis and in-memory backends, along with a semantic caching option using embedding-based similarity search. Also provides virtual key management, spend tracking, rate limiting, and basic load balancing.

Best For

Python-centric teams that need wide provider coverage and quick unification of LLM calls for development and moderate-scale production.

4. Kong AI Gateway

Platform Overview

Kong AI Gateway extends Kong's API management platform with AI-specific capabilities including prompt engineering guardrails, multi-LLM routing, and token-level rate limiting.

Features

Provides semantic caching through a dedicated plugin that uses vector embeddings and configurable similarity thresholds. Supports both open-source and enterprise tiers with an extensive plugin ecosystem.

Best For

Organizations already running Kong as their API gateway that want to extend existing infrastructure to manage LLM traffic.

5. TrueFoundry LLM Gateway

Platform Overview

TrueFoundry is a full-stack ML platform that includes an LLM gateway as part of its broader infrastructure for deploying and monitoring AI applications.

Features

Integrates semantic caching as a centralized gateway capability with a shared cache across teams, centralized threshold and TTL controls, and model-agnostic optimization that works across self-hosted and external models.

Best For

Teams that need an end-to-end ML platform combining model deployment, serving, and gateway capabilities in a single solution.

Choosing the Right Gateway

If you need the lowest possible overhead with built-in semantic caching and self-hosted deployment, Bifrost is the strongest option. If you are on Cloudflare, their gateway offers solid free exact-match caching. LiteLLM is ideal for Python-heavy teams. Kong makes sense for extending existing API infrastructure. And TrueFoundry fits teams wanting caching tightly integrated with model serving.

Whichever gateway you choose, pairing it with a robust observability platform is critical. Cache hit rates, latency distributions, and cost-per-request metrics need continuous monitoring to ensure your caching strategy delivers real value. Platforms like Maxim AI provide the production monitoring and evaluation workflows necessary to close the loop between gateway optimization and application quality.