LLM Gateway

Table of Contents * TL;DR * Understanding AI Gateways * Why Organizations Need AI Gateways * Introducing Bifrost by Maxim AI * Portkey Overview * Bifrost vs Portkey: Feature Comparison * Performance Benchmarks * Architecture and Deployment * Enterprise Capabilities * Integration Ecosystem * Pricing and Value * Why Bifrost is the Better Choice * Migration Path * Conclusion TL;DR Organizations seeking

TL;DR: As enterprise LLM spending surges past $8.4 billion and organizations deploy AI applications at scale, LLM gateways have become critical infrastructure. These routing and control layers sit between applications and model providers, offering unified APIs, automatic failover, cost optimization, and comprehensive observability. This guide evaluates the top

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

TL;DR LLM gateways have quietly become the backbone of serious AI stacks. They give you one API across providers, central key management, routing logic, and observability without forcing every team to rewrite code for each new model. (See “What is an LLM Gateway?”) If you care about reliability, spend,

TL;DRLLM gateways unify provider APIs, add failover and load balancing, enforce budgets, and give you observability.Your evaluation should focus on reliability, performance, governance, deployment model, and developer experience.Bifrost stands out for low overhead, automatic fallbacks, virtual keys with budgets, OpenTelemetry, VPC deployment, and an open-source core you

Large Language Models (LLMs) have rapidly transformed how organizations build, deploy, and scale AI-powered applications. From intelligent chatbots to advanced document processing, LLMs are at the heart of modern automation and digital intelligence. Yet, as teams move from experimentation to production, a new set of infrastructure challenges emerges, chief among