Top 5 Enterprise AI Gateways to Reduce LLM Cost and Latency
This article explores five enterprise AI gateways that aim to optimize cost and latency for running large language model (LLM) workloads in production.
Why it matters
Enterprises running LLM workloads in production need to balance cost and latency optimization to maintain profitability and user experience. These AI gateways provide a unified solution to manage these tradeoffs.
Key Points
- 1Gateways can handle caching, routing, failover, and budget controls to optimize LLM costs and latency
- 2Bifrost offers low overhead (under 15 microseconds per request), semantic caching, and multi-tier budget controls
- 3OpenRouter provides a unified API to access multiple LLM providers through a single endpoint
- 4Anthropic's Claude API Gateway offers built-in caching, failover, and cost tracking
- 5Cohere's Compose API Gateway focuses on ease of use and seamless integration with Cohere models
Details
The article discusses the importance of optimizing both cost and latency when running LLM workloads in production. It then introduces five enterprise AI gateways that aim to address these challenges: Bifrost, OpenRouter, Anthropic's Claude API Gateway, Cohere's Compose API Gateway, and Hugging Face's Inference API. Each gateway is evaluated based on its cost reduction features (caching, budgeting, cost tracking) and latency-reducing capabilities (low overhead, provider isolation, failover). The article provides technical details, setup instructions, and limitations for the Bifrost gateway, which stands out for its low latency (under 15 microseconds per request) and comprehensive cost controls. The other gateways are also covered, highlighting their unique strengths for different enterprise use cases.
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