Governance at Scale: Quotas, Allowlists, and RBAC

AI governance is not a slide deck. It is enforced on every API call. When traffic flows through Yaha, policy is applied automatically, not left to documentation.

This post covers the three pillars platform teams ask for first: role-based access, model allowlists, and quotas.

Workspace isolation

Yaha organizes resources as organizations → projects → API keys. Each project has its own keys, settings, and usage. A staging chatbot cannot accidentally spend against a production budget when they are separate projects with separate caps.

Provider credentials stay in Yaha. Developers receive project keys (yaha.*), not raw OpenAI or Anthropic secrets. Offboarding a contractor means revoking a key, not rotating every provider token in the company.

Role-based access control

The console supports roles that match how teams already work:

• Member: Use assigned projects and view own usage
• Manager: Manage project settings and keys
• Admin: Manage org billing, members, and provider keys

RBAC ensures configuration changes (enabling a new model, raising a quota) require the right approver, not whoever found the settings page first.

Model allowlists

Allowlists are the simplest high-leverage control. Define which models a project may call, and the gateway rejects anything else before it reaches a provider.

This protects against:

• Agent configs that default to the newest (priciest) model
• Mistyped model names
• Shadow IT experiments using unapproved vendors

Combine allowlists with smart routing and the gateway picks the cheapest allowed model, not the cheapest on the internet.

Quotas and rate limits

Set spending caps and request rate limits per project. When a cap is hit, the gateway returns a clear error. Your app can degrade gracefully instead of silently running up a bill.

Quotas are especially valuable for:

• Shared internal tools with unpredictable adoption
• Customer-facing features with free tiers
• CI jobs that invoke LLMs on every pull request

Usage dashboards show burn rate so you can raise limits before production incidents, not after finance sends an email.

Audit and compliance

Every request log includes timestamp, model, project, and estimated cost. Export or connect these logs to your existing tools when you need retention beyond the console UI.

For enterprise customers, separate Data Processing Agreements and dedicated environments are available. Contact us through the marketing site when you are ready.

Start with one project, grow into policy

You do not need every control on day one. A practical rollout:

1. Create one project per application or team
2. Enable allowlists with your approved models
3. Set monthly quotas from historical spend plus headroom
4. Assign console roles aligned with your existing IT structure

Governance works when it is default-on, not a quarterly audit. Yaha puts those defaults at the gateway, the one place all your AI traffic already passes through.

Set up your org: Sign in to the console.