The four pillars
Different teams reach for different names for the same four things: "access control" and "credential management" describe the same requirement from two angles, and so do "cost attribution" and "spend attribution." Axemere's product is organized around these four, under whichever name you use for them:
Credential management (also called access control)
Provider API keys are stored and scoped in one place, not scattered across services and developer machines. A team, project, or workload gets exactly the credentials it needs, and revoking or rotating a key doesn't require a code deploy.
Policy enforcement (also called access control)
Rules about which models, providers, and request patterns are allowed are evaluated centrally, on every request, before it reaches a provider, not left to each application to implement its own version, inconsistently.
Spend attribution (also called cost attribution)
Every request is tied to the org, project, and workload that made it, plus, for multi-tenant or customer-facing products, the account and customer it should bill to. Spend can be broken down and reported at whichever level the question is actually being asked at, in real time, not reconstructed after the invoice arrives.
Audit trail
Every execution (provider, model, cost, and the policy decision that allowed or denied it) is written into a cryptographically signed, Merkle-anchored record, not just a log line. Anyone outside engineering, a compliance reviewer or an auditor, can independently verify what happened instead of taking it on trust.
A fifth property cuts across all four rather than standing alone: it only counts as governance if it's consistent across every provider an organization uses, one policy engine and one audit trail per organization, not one per AI tool it adopted.
Why it matters now
AI adoption inside most organizations has outpaced the controls around it: API keys live on individual developer machines, or worse, checked into a source repository where anyone with read access can find them; spend is invisible until the monthly bill; and there's no consistent answer to "which requests were allowed to happen and why." That gap is exactly where the incidents companies are now publicly reacting to come from: surprise invoices, hard spending caps introduced after the fact, finance teams asking engineering for numbers nobody collected. Operational governance is the set of controls that closes it before it becomes a headline instead of after.
How Axemere implements it
Axemere is a gateway and control plane that sits in front of every AI provider an organization uses. Credentials are stored and scoped centrally. Policy is a cross-provider rule set evaluated on every request. Spend is attributed and capped in real time, at the org, project, workload, account, or customer level. Every execution generates a signed record. It runs self-hosted, managed by Axemere, or both across a fleet; the governance model doesn't change based on who operates the infrastructure.
See governance applied to your own traffic
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