AI Platforms & Models

AI API Integration

When internal systems need to call AI capability directly, and that access has to be authenticated, rate limited, and auditable like any other internal service.

Exposing AI through an internal API turns a capability into infrastructure other teams can build on. The requirements are ordinary service requirements: authentication, quotas, versioning, and logs.

The AI-specific addition is cost. Unlike most internal services, each call has a variable price, which makes per-consumer budgets more than a nicety.

What the work includes

  • Internal API surface with authentication and per-consumer quotas
  • Versioning so behaviour changes do not break callers silently
  • Cost attribution per consuming team or application
  • Audit logging consistent with the rest of your service estate
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// Common Questions

Questions
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Common questions about AI API Integration.

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An interface that is authenticated, rate-limited, logged, and versioned — the same requirements as any internal API. The AI-specific part is handling non-deterministic responses and variable latency in a contract other teams can build against.

Per-consumer rate limits and quotas, so a runaway loop in one service degrades that service rather than the shared capability. Without them, the first team to write an unbounded retry becomes everyone's outage.

By authenticating each consumer separately and recording usage against them. Attribution has to be built in from the start, because retrofitting it means a period where cost is visible in aggregate but cannot be assigned to anyone.

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