Integrating AI APIs into Production Systems

AI integration is no longer optional for modern SaaS products. But integrating AI APIs into production systems requires careful consideration of reliability, cost, and user experience.

API Gateway Pattern
Never call AI APIs directly from your frontend. Use a backend gateway that handles authentication, rate limiting, and response caching. This also gives you the flexibility to switch providers without changing client code.

Streaming Responses
For chat-like interfaces, implement streaming responses using Server-Sent Events or WebSockets. Users expect real-time feedback, and waiting for a complete response creates a poor experience.

Cost Management
AI API costs can spiral quickly. Implement token counting, set per-user limits, and cache common responses. Consider using smaller models for simple tasks and reserving larger models for complex operations.

Fallback Strategies
AI APIs can fail or return unexpected results. Always have fallback strategies — whether it's retrying with a different model, returning cached results, or gracefully degrading the feature.

Prompt Engineering as Code
Treat your prompts as code. Version them, test them, and review changes. A well-structured prompt template system makes it easy to iterate and improve AI features over time.