Top 5 AI Gateways with Semantic Caching to Reduce OpenAI and Anthropic API Costs

API costs are one of the fastest-growing line items for teams building production AI applications. When an application receives hundreds of thousands of requests per day, a significant portion of those requests are semantically identical or near-identical variations of each other. Without an intelligent caching layer, every one of those requests hits the LLM provider and incurs a full token cost.

Semantic caching solves this by storing AI responses and matching future requests based on meaning rather than exact string equality. A user asking "What is your return policy?" and another asking "How do I return an item?" can both be served from the same cached response if their semantic similarity exceeds a configured threshold. The result is lower API spend, lower latency, and less pressure on provider rate limits.

This post covers five AI gateways that offer semantic caching as a core capability, with a focus on how each implements it and what teams should consider when choosing one.

What Semantic Caching Actually Does in an AI Gateway

Before evaluating options, it helps to understand the mechanics. Semantic caching in an AI gateway typically works as follows:

• Embedding generation: Incoming prompts are converted into vector embeddings using an embedding model.
• Similarity search: The embedding is compared against a vector store of previously cached responses using a configurable similarity threshold.
• Cache hit or miss: If a sufficiently similar request is found, the cached response is returned immediately. If not, the request is forwarded to the LLM provider and the response is stored for future use.
• Cost and latency savings: Cache hits avoid LLM API calls entirely, delivering sub-millisecond retrieval instead of multi-second provider round-trips.

The quality of semantic caching depends on the threshold configuration, the embedding model used, the vector store backend, and how the gateway handles edge cases like long conversation histories or per-model cache isolation.

Top 5 AI Gateways with Semantic Caching

1. Bifrost

Bifrost is an open-source, high-performance AI gateway built in Go that provides one of the most technically complete semantic caching implementations available. It is designed for teams running production-scale LLM workloads who need cost control alongside enterprise-grade reliability.

How Bifrost's semantic caching works:

• Dual-layer caching: Bifrost uses both exact hash matching and semantic similarity search in combination. Exact matches are served first for zero-overhead retrieval; semantic matching handles near-identical variations. Teams can also run direct hash mode without an embedding provider for pure exact-match deduplication at the lowest possible latency.
• Vector store support: Bifrost supports Weaviate, Redis/Valkey, Qdrant, and Pinecone as vector store backends, giving teams flexibility to use managed or self-hosted infrastructure they already operate.
• Configurable thresholds and TTLs: The similarity threshold (default 0.8) and TTL can be set globally or overridden per-request using HTTP headers (x-bf-cache-threshold, x-bf-cache-ttl), enabling fine-grained control without code changes.
• Per-model and per-provider isolation: Cache entries are keyed separately by model and provider combination by default, preventing cross-model cache contamination.
• Streaming support: Bifrost caches full streaming responses with proper chunk ordering, which most gateway implementations do not handle correctly.
• Conversation history threshold: A configurable conversation_history_threshold parameter skips caching for long conversations where semantic false positives are more likely, preventing stale or mismatched responses from being served.
• Cache metadata in responses: Every response includes a cache_debug object with cache_hit, hit_type (semantic or direct), similarity score, and cache entry ID for management and debugging.

Bifrost also layers semantic caching on top of a broader infrastructure stack that includes automatic fallbacks, adaptive load balancing, enterprise governance via virtual keys, and an MCP Gateway for agentic workloads. Teams get cost reduction alongside the full reliability and governance stack needed for production deployments.

At 5,000 requests per second in sustained benchmarks, Bifrost adds only 11 microseconds of overhead per request, meaning the caching layer does not introduce meaningful latency when serving cache misses.

Book a demo to see Bifrost's semantic caching in a production context.

2. LiteLLM Proxy

LiteLLM's proxy layer includes a caching module that supports Redis as a backend. It can be configured for semantic caching using an embedding model alongside a Redis instance, with a similarity threshold setting that controls cache hit sensitivity.

Key considerations:

• Semantic caching is available but requires additional configuration on top of the base proxy setup.
• The Python-based architecture introduces higher baseline latency compared to Go-based alternatives, which compounds at high request volumes.
• Cache configuration is global rather than per-request, limiting dynamic control for applications with variable caching requirements.
• There is no native dual-layer caching (exact hash plus semantic fallback) in the open-source version.

LiteLLM's semantic caching works well for teams already using LiteLLM at moderate scale who want basic cost reduction without changing their gateway infrastructure.

3. Kong AI Gateway

Kong's AI Gateway, built on the Kong API Gateway platform, includes an AI Semantic Caching plugin that integrates with Redis as the vector store. It supports OpenAI-compatible embedding models for similarity search and can be deployed as part of Kong's broader API management stack.

Key considerations:

• Semantic caching is implemented as a plugin within Kong's declarative configuration model, which fits naturally for teams already running Kong for API management.
• Kong's enterprise tier is required for full AI Gateway feature access, including advanced semantic caching controls.
• The operational overhead of managing Kong as an API gateway adds complexity for teams that only need an LLM gateway, not a full API management platform.
• Redis is the primary vector store backend; support for other vector databases requires custom plugin development.

Kong AI Gateway is a reasonable choice for enterprises with existing Kong infrastructure looking to add LLM cost controls without adopting a separate gateway.

4. NVIDIA NIM Microservices with API Gateway Caching

NVIDIA's NIM microservices architecture supports response caching at the inference layer, with semantic similarity-based retrieval available for teams running NVIDIA-hosted or self-hosted model endpoints. This is oriented toward teams running inference on NVIDIA hardware rather than routing to third-party providers like OpenAI or Anthropic.

Key considerations:

• Caching is tightly coupled to NVIDIA's inference stack; it is not a standalone gateway solution for routing to external LLM APIs.
• Best suited for teams running their own model deployments on NVIDIA infrastructure.
• Does not address multi-provider routing, fallback logic, or governance in the way a purpose-built LLM gateway does.
• Operationally complex to deploy for teams without existing NVIDIA infrastructure.

This option is relevant primarily for AI teams running on-premises or private cloud inference at scale with NVIDIA hardware.

5. AWS API Gateway with Bedrock Caching

AWS API Gateway combined with Amazon Bedrock's prompt caching feature provides a caching layer for teams already operating within the AWS ecosystem. Bedrock's prompt caching reduces costs by reusing context across requests that share a common prefix, functioning as a form of structured caching.

Key considerations:

• Bedrock prompt caching is prefix-based rather than semantic similarity-based, meaning it captures exact or near-exact prompt repetitions rather than semantically similar queries across different phrasings.
• Integration requires AWS-native tooling (Lambda, API Gateway, IAM roles), which adds setup complexity for teams not already on AWS.
• Limited to Bedrock-supported models; multi-provider routing to OpenAI, Anthropic direct, or other providers is not supported in the same gateway layer.
• No built-in similarity threshold configuration or per-request cache control headers.

AWS Bedrock caching is a useful cost control mechanism for teams already using Bedrock models at scale, but it does not replicate the semantic similarity matching that purpose-built LLM gateways provide.

Choosing the Right Gateway for Semantic Caching

The right choice depends on the scale, infrastructure preferences, and governance requirements of each team. For teams evaluating options, these are the key questions to answer:

• Does the gateway support both exact hash and semantic similarity caching, or only one mode?
• Can similarity thresholds and TTLs be overridden per-request, or only set globally?
• What vector store backends are supported, and do they match existing infrastructure?
• Does the caching layer integrate with the gateway's broader routing, fallback, and governance stack?
• How does the implementation handle streaming responses and long conversation histories?

For teams running production workloads that send high volumes of requests to OpenAI or Anthropic, Bifrost provides the most complete semantic caching implementation alongside the infrastructure features needed to run reliably at scale. The dual-layer caching model, per-request configuration controls, multi-vector-store support, and streaming compatibility make it the most flexible and production-ready option in this list.

Explore Bifrost's documentation or book a demo to see how semantic caching fits into your LLM infrastructure.