AI Gateway

TL;DR * An LLM gateway sits in front of coding agents like Claude Code, Codex CLI, Gemini CLI, and Cursor to give every tool one API, multi-provider routing, automatic failover, and shared cost and governance controls. * Bifrost, the open-source AI gateway, ranks first: it exposes 100% compatible OpenAI,

TL;DR * AI access control is the practice of governing which people, teams, and applications can call which LLM models and providers, enforced centrally rather than through scattered raw API keys. * The strongest tools combine a policy layer (virtual keys, RBAC, access profiles) with identity integration (SSO and OIDC) so

TL;DR * Automatic failover keeps an LLM application available by rerouting a failed request to the next provider in a fallback chain once retries against the primary provider are exhausted. * Load balancing spreads requests across multiple API keys and providers to avoid rate limits, while failover redirects traffic only after

TL;DRGenerative AI security protects models, applications, and the data they process from misuse, manipulation, and compromise. LLMs add failure modes, like prompt injection and data leakage through prompts, that traditional application security was not built to catch.IBM's 2025 report found 13% of organizations had breaches involving

TL;DR * LLM API rate limiting caps how many requests and tokens a consumer can send in a time window, while budgets cap how much money that consumer can spend; you need both to prevent 429 errors and runaway cost. * In Bifrost, the open-source AI gateway by Maxim AI,

TL;DR * LLM monitoring is the continuous collection of cost, latency, token, and error-rate signals for every model call, so engineering and compliance teams can see what happened and who did it. * The core metrics to track are cost per request, latency (including time to first token), token consumption,