LLM Gateway

Production AI applications face a brutal scaling reality. A customer support agent handling 10,000 daily conversations can rack up $7,500+ monthly in API costs. Factor in response latencies of 3-5 seconds that test user patience, and engineering teams find themselves trapped between quality and sustainability. This isn'

TL;DR: AI gateways have evolved from simple API proxies to critical infrastructure for LLM applications in production. This comprehensive guide evaluates the top 5 AI gateways for 2026: Bifrost by Maxim AI leads with unmatched performance (11µs overhead at 5,000 RPS), zero-config deployment, and enterprise-grade features. Portkey excels

TL;DR: As AI applications scale, teams need high-performance LLM gateways that deliver speed, reliability, and enterprise features. Bifrost by Maxim AI leads with 50x faster performance than traditional gateways, adding just 11µs overhead at 5,000 RPS. LiteLLM offers extensive provider support with strong community backing. Portkey provides deep

TL;DR * TrueFoundry is a comprehensive MLOps platform with AI gateway as one component among training, deployment, and infrastructure management capabilities * Bifrost is a purpose-built AI gateway designed exclusively for high-performance model access, routing, and management * Zero-config deployment: Bifrost starts in seconds without complex Kubernetes setup or infrastructure management * Performance-first

TL;DR: Model Context Protocol (MCP) gateways have become essential infrastructure for production AI agents. This guide evaluates the top 5 MCP gateway solutions in 2025: Bifrost leads with its developer-first approach and comprehensive feature set including sub-3ms latency, built-in tool registry, and seamless integration capabilities. TrueFoundry offers unified AI

TL;DR As enterprise LLM spending surges past $8.4 billion in 2025, teams building production AI applications need LLM gateways that scale without becoming bottlenecks. While LiteLLM has been a popular choice for multi-provider routing, production teams increasingly face performance degradation, memory leaks, and latency overhead issues at scale.