AI Gateway

As AI applications scale from prototypes to production systems handling millions of requests, the infrastructure layer between your application and LLM providers becomes critical. Enterprise AI gateways solve a core architectural challenge: how to route, balance, and govern AI traffic across multiple providers without sacrificing performance or reliability. According to

Enterprise AI teams rarely rely on a single model. Most production applications orchestrate across OpenAI for reasoning, Anthropic for nuanced conversation, Google Gemini for multimodal capabilities, and providers like Groq or Cerebras for latency-sensitive operations. Managing these integrations directly means juggling different SDKs, authentication schemes, rate limits, and response

TL;DR: LLM costs scale unpredictably in production. Token-based billing, variable output lengths, and multi-provider architectures make it easy to overshoot budgets without warning. AI gateways solve this by enforcing hierarchical budget limits, tracking spend in real time, and triggering alerts before overruns happen. This guide evaluates the

TL;DR: LLM rate limiting is one of the biggest blockers to scaling AI applications in production. Enterprise AI gateways solve this by intelligently distributing requests across providers, keys, and models, ensuring uninterrupted service even under heavy load. This article breaks down the top 5 AI gateways purpose-built to

This guide outlines a practical framework for making LLM token usage traceable and governable, and explains how Bifrost solves this at the infrastructure layer so platform teams can understand where tokens go, who consumes them, and how to control it. Every interaction with a large language model costs money. Tokens

Running LLM applications in production without observability is operationally reckless. When costs spike, teams struggle to determine whether traffic increased or an agent entered a recursive loop. When quality drops, it is unclear whether prompts regressed, retrieval failed, or a new model version introduced subtle behavior changes. And when compliance