AI Gateway

Top AI Gateways to Reduce LLM Cost and Latency

Compare the top AI gateways to reduce LLM cost and latency in production. See how Bifrost, Cloudflare, LiteLLM, Kong, and Vercel handle caching, routing, and governance.

Enterprise spending on large language models reached $8.4 billion by mid-2025 and tripled across the full year, with foundation model API spend alone hitting $12.5 billion in 2025. For teams running AI in production, every unoptimized API call compounds into wasted budget and degraded user experience. An AI gateway sits between applications and LLM providers, centralizing caching, routing, failover, governance, and observability in a single infrastructure layer. Bifrost, the open-source AI gateway by Maxim AI, leads this category with 11 microsecond overhead per request, semantic caching, and budget controls. Bifrost is open source on GitHub, and the full documentation covers setup in under a minute.

This guide breaks down the top five AI gateways to reduce LLM cost and latency and where each fits in a production stack.

Why AI Gateways Matter for LLM Cost and Latency

AI gateways reduce LLM cost and latency by centralizing all model traffic through a single control plane. Without one, every application team rebuilds the same caching, retry, and provider-management logic, and finance leaders cannot answer which team or feature is driving the bill.

The economic case has sharpened in 2026. Token unit prices have fallen sharply over two years, yet agentic models consume 5 to 30 times more tokens per task than standard chatbots, and RAG architectures inflate context windows further. Usage growth is outpacing price reduction, and unit savings only materialize for teams that own the infrastructure layer.

The optimization levers a gateway provides are:

Caching: Returns stored responses for repeated or semantically similar queries, eliminating redundant provider calls
Failover and load balancing: Routes requests to alternate or cheaper models when a primary provider rate-limits or slows
Budget controls: Enforces spending limits per key, team, customer, and provider before costs accumulate
Observability: Surfaces per-model and per-team cost and latency data for informed routing decisions
Governance: Replaces shared provider keys with scoped virtual keys carrying budgets, rate limits, and model allowlists

These levers stack. A 40% semantic cache hit rate plus weighted routing toward lower-cost providers and budget caps that fire before a runaway agent loop completes can cut monthly inference spend by half with no application code changes.

Key Criteria for Evaluating AI Gateways

Before comparing specific tools, it helps to understand what separates a production-grade AI gateway from a basic proxy. The criteria that matter most for cost and latency reduction:

Gateway overhead: The latency a gateway adds to every request. Python-based gateways often add hundreds of microseconds to milliseconds under load; compiled Go gateways add microseconds.
Caching strategy: Exact-match caching helps, but semantic caching, which matches by meaning rather than exact text, captures far more redundant queries and delivers higher cache hit rates in production.
Provider coverage: More supported providers means more flexibility for cost-optimized routing across OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, Google Vertex AI, and open-source endpoints.
Budget granularity: Spending limits set at multiple levels (per key, team, customer, provider) prevent cost overruns before they happen. The LLM Gateway Buyer's Guide outlines what to look for in detail.
Observability depth: Native Prometheus metrics, OpenTelemetry traces, and per-virtual-key telemetry are required for accurate cost attribution in production.
Deployment model: Self-hosted gateways give full control over data residency and air-gapped operation; managed gateways reduce operational overhead at the cost of compliance flexibility.
MCP support: Native Model Context Protocol handling, including tool filtering and token-optimization modes, is the difference between governable and ungovernable agent traffic.

Top 5 AI Gateways to Reduce LLM Cost and Latency

1. Bifrost

Bifrost is a high-performance, open-source AI gateway built in Go under Apache 2.0. It unifies 1000+ models across 20+ providers (OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, Azure OpenAI, Mistral, Groq, Cohere, Ollama, and more) through a single OpenAI-compatible API.

Raw performance is what sets Bifrost apart. In sustained benchmarks at 5,000 requests per second, Bifrost adds 11 microseconds of overhead per request, delivers 9.5x higher throughput than Python-based alternatives, 54x lower P99 latency, and uses 68% less memory.