AI Gateway

Top AI Gateways to Reduce LLM Cost and Latency

Compare the top AI gateways for reducing LLM cost and latency in production. See how Bifrost, Cloudflare, LiteLLM, Kong, and Vercel stack up on caching, routing, and budget controls.

Enterprise LLM API spending has surged past $8.4 billion, with inference costs projected to reach $15 billion by the end of 2026. For teams running AI in production, every unoptimized API call compounds into wasted budget and degraded user experience. An AI gateway sits between your application and LLM providers, giving you caching, routing, failover, and budget controls 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 hierarchical budget management built for production-grade workloads.

This guide breaks down the top five AI gateways for reducing LLM cost and latency, what each does well, and where each fits in your stack.

Why AI Gateways Are Essential for LLM Cost and Latency Optimization

AI gateways reduce LLM cost and latency by centralizing all model traffic through a single control plane. Instead of each application team implementing its own caching, retry logic, and provider management, a gateway handles these concerns at the infrastructure level. The core optimization levers are:

Caching: Returns stored responses for repeated or semantically similar queries, eliminating redundant provider calls entirely
Failover and load balancing: Routes requests to available or cheaper models when a primary provider fails, rate-limits, or experiences latency spikes
Budget controls: Enforces spending limits at the key, team, or project level before costs escalate
Observability: Surfaces per-model, per-team cost and latency data so teams can make informed routing decisions

According to Menlo Ventures' 2025 mid-year enterprise survey, enterprise LLM API spend jumped from $3.5 billion to $8.4 billion within two quarters. Without a gateway layer, teams overpay for redundant API calls, lack fallback mechanisms during provider outages, and have no centralized visibility into spending patterns.

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 are:

Gateway overhead: The latency a gateway adds to every request. Python-based gateways often add 100 to 500 milliseconds; compiled gateways add microseconds.
Caching strategy: Exact-match caching helps, but semantic caching (matching by meaning, not exact text) captures far more redundant queries and delivers higher cache hit rates.
Provider coverage: More supported providers means more flexibility for cost-optimized routing. Teams that can route between OpenAI, Anthropic, Bedrock, and Vertex AI have more pricing levers.
Budget granularity: The ability to set spending limits at multiple levels (per key, per team, per customer) prevents cost overruns before they happen.
Deployment model: Self-hosted gateways give full control; managed gateways reduce operational overhead. The right choice depends on compliance requirements and team capacity.

Top 5 AI Gateways to Reduce LLM Cost and Latency

1. Bifrost

Bifrost is an open-source, high-performance AI gateway built in Go. It unifies access to 1000+ models through a single OpenAI-compatible API, including OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, Azure OpenAI, Mistral, Groq, and Cohere.

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