AI Gateway

TL;DR * An AI audit trail for LLM traffic is a signed, retained record of who called which model, with what prompt and response, at what cost, and which policy fired, produced at the gateway so it covers every request. * The runtime controls that belong at the LLM traffic layer

TL;DR * Enterprise LLM access control governs which users, teams, and applications can reach which models, tools, and data across an entire AI footprint, at a scale where shared API keys stop working. * Five building blocks carry it: federated identity, fine-grained authorization, row-level data scoping, cost governance, and

TL;DR * Gateway-level PII redaction detects sensitive values in an LLM request and rewrites them before the request leaves your network, so no application has to sanitize its own prompts. * Bifrost implements this through guardrails: rules written in Common Expression Language decide when to evaluate, and profiles decide how,

TL;DR * AI agent skills are reusable SKILL.md folders that give coding agents like Claude Code and Codex domain-specific instructions, and teams accumulate dozens of them fast once more than one person is writing skills. * Bifrost's Skills Repository lets a team create, publish, and version agent

TL;DR * Adaptive load balancing distributes AI traffic across providers and API keys using live performance data instead of a fixed weight or round-robin rule. * Bifrost Enterprise recalculates route weights every 5 seconds from error rate, latency, and utilization, adding less than 10 microseconds to routing overhead. * Routes move

Bifrost, Kong, Cloudflare, LiteLLM and OpenRouter compared on virtual keys, model allowlists, budgets, RBAC and data scoping.