TokenDate
Make every AI request pass through a policy you can explain.
TokenDate 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.
Permissions are visible before they become incidents.
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.
A limit that reacts while work is happening.
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.
Integrate once. See every decision immediately.
Keep the OpenAI-compatible call your application already knows. TokenDate evaluates the key and balance before routing, then returns an outcome your team can act on.
One request. Four accountable checkpoints.
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.
Make the operating boundary explicit.
TokenDate 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.
Approved access
Teams and members can be assigned to the models your organization has approved.
Checks before delivery
Configured access and quota conditions are evaluated before a request is sent upstream.
Operational evidence
Request records retain the caller, model, status, usage, and cost context for review.
Clear responsibility
Your organization owns its policies, users, prompts, and decisions about model output.
One request. Four accountable checkpoints.
- Identify. Resolve the member, team, and key.
- Evaluate. Check model access and quota.
- Deliver. Route to an approved endpoint.
- Record. Keep status, usage, and cost context.
How can access be granted without slowing teams down?
Define model access by team or member, then let approved users work through familiar APIs or the workspace. Administrators retain a clear authorization boundary.
How can a company keep AI spend predictable?
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.
What can teams review after a request is made?
Request records provide the caller, model, status, and token usage, giving technical and business owners a shared starting point for operational review.
Can governance be introduced gradually?
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.
How should API keys be managed across a team?
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.
What happens when a request is not authorized?
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.
Who needs visibility into AI usage?
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.
How can a team adjust controls over time?
Review usage patterns and operating needs, then adjust model access, quotas, and team scope as policies and adoption mature.