AI Gateway

TL;DR * LLM infrastructure is the full set of compute, serving, data, routing, and operational systems that take a large language model from a model file to a reliable production service. * The stack has five layers: GPU compute, model serving and inference, the retrieval and data layer (vector databases), the

TL;DR * AI usage tracking by team is the practice of attributing LLM tokens, requests, and cost to the team, project, or user that generated them, so finance and platform owners can report and control spend per group. * The six tools and approaches in this roundup are Bifrost, native provider

TL;DR * Claude Pro costs $20 per month and gives at least five times the per-session usage of the free tier, but that usage is still capped by a five-hour session window and a weekly limit that applies across all models, per Anthropic's Help Center. * Claude

TL;DR * Security is now the top barrier to enterprise AI rollout, and because every request can carry PII, secrets, or regulated data, the gateway is the enforcement point that decides whether a deployment is defensible. * Five capabilities define an enterprise security gateway: content guardrails, access control, secrets management, audit

TL;DR * A Claude rate limit exceeded error is an HTTP 429 returned when your organization crosses one of three per-minute ceilings: requests, input tokens, or output tokens. * Anthropic applies rate limits separately per model class, so a 429 on one Claude model leaves capacity on the others untouched,

TL;DR: An open-source AI gateway sits between your applications and LLM providers on a publicly licensed codebase, letting teams keep prompt data, completions, and audit logs inside their own infrastructure. For self-hosted deployments in regulated industries, that open license is the deciding property.Bifrost is the fastest