Building Your First AI SaaS Product: A Developer's Guide

Architecture Fundamentals

Building AI SaaS requires careful consideration of scalability, cost, and user experience. The architecture must handle variable workloads while maintaining performance.

Model Selection Strategy

Choose models based on task requirements: use smaller models for simple tasks, larger models only when necessary. Cost scales with model size, so optimize for your use case.

API Design Best Practices

Design APIs that handle async processing gracefully. AI operations can be slow—implement job queues, webhooks, and status endpoints for better UX.

Cost Optimization

Implement caching, batch processing, and model quantization to reduce inference costs. Monitor usage patterns to optimize spending without impacting user experience.

Deployment Considerations

Use containerized deployments for consistency. Consider serverless for variable workloads and dedicated infrastructure for high-volume applications.

Security & Privacy

Implement proper authentication, rate limiting, and data encryption. For sensitive use cases, consider on-premise deployment options.

Scaling Strategies

Start with MVP using managed services, then optimize based on usage patterns. Plan for horizontal scaling from day one.