AI API Development

We use two strategies depending on the use case: streaming (SSE) delivers tokens as they arrive for interactive UIs with zero perceived latency; and async job queues (SQS + webhook callbacks) handle long-running LLM tasks where the caller polls for completion. Both approaches keep the API responsive regardless of model latency.

We implement per-API-key token budgets enforced at the gateway level, with hard limits that reject requests when the budget is exhausted. Usage dashboards give you visibility into which callers are consuming most tokens. For monetised APIs, we integrate with Stripe metering to charge based on actual token consumption.

Yes. Every project ships with an OpenAPI 3.0 specification, a Swagger UI endpoint at /api/docs in staging, and a Postman collection. For public developer APIs, we also produce a developer portal with authentication guides, code samples in Node.js, Python, and curl, and a sandbox environment.

We use URL-based versioning (/api/v1/, /api/v2/) and follow semantic versioning principles: backward-compatible changes are additive within a version; breaking changes require a new version with a defined deprecation window (minimum 6 months). Sunset headers notify integrators of upcoming deprecations.

Yes. We implement tenant isolation at the API key level: each tenant has separate API keys, rate limits, and token budgets. At the model level, RAG indexes are namespaced per tenant so each customer only retrieves from their own knowledge base. Conversation memory is scoped per user within each tenant.