Guide
Governance and cost control, built into every request
mo secures, budgets, and enforces the AI work your engineers already do, without asking them to change tools. Most toolchains grow one tool at a time, so nobody ends up owning the layer between agent and model. When a task finishes there is no consistent record of what ran it, what it cost, or whether it was approved.
- Where the gap actually is. Budget, enforcement, and security: the three exposures that open up once work happens in personal accounts rather than corporate ones.
- Budgets that catch a runaway session. Daily and monthly ceilings per user, team, or org, remaining usage returned in every response header, and a logged record of tool, model, cost, and outcome.
- Enforcement that does not rely on a written policy. Approval-gated actions, a model-backed safety judge, hard-deny rules local config cannot override, and automatic failover when an approved provider goes down.
- What a security team usually asks. Straight answers on what the gateway stores, where traffic can go, what happens if the key store is unreachable, and how offboarding works.
