Control plane
New API turns access, spend, delivery, and review into one visible operating path. Teams move quickly because the guardrails are applied before a request leaves your organization.
01 / Access policy
Map teams and members to approved models, keep exceptions explicit, and stop an unauthorized call at the gateway instead of discovering it in a provider bill.
| ChatGPT | Claude | Gemini | |
|---|---|---|---|
| Product | Allowed | Allowed | Allowed |
| Engineering | Allowed | Allowed | Allowed |
| Data | Allowed | Not allowed | Allowed |
| Interns | Allowed | Not allowed | Not allowed |
02 / Live budget
Watch a team or key approach its budget, then let the gateway throttle new requests at the configured boundary. No spreadsheet needs to be the last line of defense.
gpt-5.6-solSuccess03 / Request outcome
Keep the OpenAI-compatible call your application already knows. New API evaluates the key and balance before routing, then returns an outcome your team can act on.
04 / Operating flow
The same path works for an employee in the workspace and an engineer using the API: identify the caller, evaluate the policy, deliver to an approved model, and leave a record for review.
05 / Governance boundary
New API governs the managed path to approved AI services. It gives teams evidence for access and operating decisions, while each organization remains responsible for its own policies, users, prompts, and use of model output.
Teams and members can be assigned to the models your organization has approved.
Configured access and quota conditions are evaluated before a request is sent upstream.
Request records retain the caller, model, status, usage, and cost context for review.
Your organization owns its policies, users, prompts, and decisions about model output.
Governance questions
Governance should support day-to-day adoption, not create a separate process around every model request.
Define model access by team or member, then let approved users work through familiar APIs or the workspace. Administrators retain a clear authorization boundary.
Set quotas for keys and teams, review usage as it changes, and stop requests when a configured limit is reached. This creates a practical spending boundary before invoices arrive.
Request records provide the caller, model, status, and token usage, giving technical and business owners a shared starting point for operational review.
Yes. Start with the models and teams that need access first, apply the controls that matter now, and extend the rollout as internal AI use grows.
Issue keys according to the team or purpose they serve, apply appropriate quotas, and use the request history to understand how each access path is being used.
The gateway can evaluate configured access and quota rules before sending a request upstream, so unapproved or over-limit use can be stopped at the managed boundary.
Engineering needs operational signals, while finance and security often need spend and access context. A shared record lets each team work from the same facts.
Review usage patterns and operating needs, then adjust model access, quotas, and team scope as policies and adoption mature.