AI Gateway

5 Best OpenRouter Alternatives in 2026

5 Best OpenRouter Alternatives in 2026

Compare the top OpenRouter alternatives in 2026 on performance, governance, self-hosting, and enterprise readiness. Find the right AI gateway for production workloads. Teams evaluating OpenRouter alternatives in 2026 are almost always responding to the same set of production constraints: no self-hosting, a credit markup that compounds at enterprise

Claude Opus 4.5 vs GPT 5.2: Which AI Model Leads in 2026?

Claude Opus 4.5 vs GPT 5.2: Which AI Model Leads in 2026?

TL;DR: Claude Opus 4.5 and GPT 5.2 represent the cutting edge of AI capabilities in early 2026, each excelling in distinct areas. Claude Opus 4.5 dominates software engineering with 80.9% on SWE-bench Verified and superior long-horizon coding tasks, while GPT 5.2 excels

From LiteLLM to a Gateway with Native Semantic Caching: A Migration Guide

From LiteLLM to a Gateway with Native Semantic Caching: A Migration Guide

A step-by-step migration guide from LiteLLM to Bifrost, the AI gateway with native semantic caching, dual-layer hit matching, and zero application code changes. Teams running LiteLLM in production often hit the same wall: response caching works in isolation, but wiring semantic caching into the proxy introduces Redis

How AI Gateways Tackle Rate Limiting for LLM Apps

How AI Gateways Tackle Rate Limiting for LLM Apps

Every LLM application runs into the same production wall: provider rate limits. A spike in traffic, a long context window, or a runaway agent loop trips a requests-per-minute or tokens-per-minute ceiling, and the API returns a 429 Too Many Requests error. Exponential backoff delays the problem

Smart LLM Routing: Picking the Optimal Model per Request

Smart LLM Routing: Picking the Optimal Model per Request

Smart LLM routing in production cuts cost and latency by matching each request to the right model. Learn how Bifrost routes at the gateway layer. Every production AI application eventually hits the same wall: the team picked one default model, costs keep climbing, and latency on simple requests is indistinguishable

Enterprise AI Gateway: Per-User Rate Limiting, Budget Controls, and Automatic Fallbacks

Enterprise AI Gateway: Per-User Rate Limiting, Budget Controls, and Automatic Fallbacks

How an enterprise AI gateway enforces per-user rate limiting, budget controls, and automatic fallbacks to keep LLM spend and uptime predictable at scale. Enterprise LLM usage is scaling faster than the controls built to govern it. Platform teams are fielding the same three problems at once: one agent or

AI Agent Audit Logs: Full Visibility Over Tool Usage

AI Agent Audit Logs: Full Visibility Over Tool Usage

AI agent audit logs give enterprise security teams full visibility over which tools agents call, with what arguments, and who authorized each execution. Security teams now own a new attack surface: autonomous AI agents calling tools through Model Context Protocol (MCP) servers, vendor APIs, and internal systems. Without AI agent