Enterprise AI Governance Platform for Your Entire AI Lifecycle

AI governance is the set of policies, controls, and audit processes an organization uses to manage how AI models are accessed, used, and paid for. In practice it covers four things: who can use which models, what data those models can see, how much each team can spend, and whether every request is logged for compliance.

Most governance frameworks stop at policy documents. Enforcing them requires a control point in the request path which is where an LLM gateway comes in.

An AI gateway is a control layer that sits between your applications and every LLM provider. Because all traffic passes through it, it can enforce access rules, budgets, rate limits, and logging on every request, without changing application code.

Bifrost applies identity, access profiles, virtual keys, RBAC, spend limits, and audit logging at this layer, so a policy set once applies across every model, team, and provider.

Without a single control point, each team wires its own provider keys, and the organization loses visibility into who is calling which model and what it costs. That creates shadow AI, unbounded spend, and no audit trail when a regulator asks.

A unified platform consolidates key management, access control, budgets, and logging into one place so governance is enforced by default rather than by policy memo.

Evaluate on five criteria: deployment model (can it run in your VPC, on-prem, or air-gapped?), enforcement depth (does it block policy violations or only report them?), identity integration (SSO, SCIM, existing IdP groups), compliance coverage (audit-log export, PII redaction, framework mapping), and latency overhead in the request path.

Ask specifically whether governance is enforced inline or asynchronously. After-the-fact reporting will not stop a budget overrun or a PII leak.

By tracking token usage per request and attributing it to a team, user, or application, then enforcing hard limits. Bifrost supports hierarchical budgets, per-key and per-team rate limits, and configurable behaviour when a limit is hit: throttle, reject, or fall back to a cheaper model.

Semantic caching reduces spend further by serving repeat requests without a provider call.

Every request through the gateway produces an immutable audit record with user identity, model, token counts, and policy decisions exportable to your SIEM or object storage. PII detection and redaction run inline on request and response bodies. Self-hosted deployment keeps prompts and keys inside your own network boundary, which is often the deciding factor for HIPAA and GDPR workloads.

Budgets cascade from Customer to Team to Virtual Key to Provider. All applicable budgets must pass for a request to proceed. When a transaction occurs, the same cost deducts from every relevant level simultaneously. A single exhausted budget at any tier blocks the entire request.

To know more, read the budgets and rate limits documentation.

Requests return specific HTTP status codes: 402 for budget exceeded, 429 for rate limits exceeded. Virtual keys remain functional for other operations but block LLM requests until budgets reset (based on configured duration) or rate limit windows expire.

See how virtual keys enforce spend and access on every call.