Software Architecture for AI Startups

AI startups face a unique architectural challenge: you're building on top of rapidly evolving APIs, models, and frameworks while trying to ship a stable product. The key is designing for change without over-abstracting.

Separate your AI layer from your application layer. Your business logic, user management, billing, and UI should not depend directly on a specific LLM provider or framework. Use an abstraction layer — even a simple one — so you can swap models without rewriting your app.

Start monolithic, but with clear module boundaries. Microservices are overkill for a 2-person team, but having distinct modules for ingestion, retrieval, generation, and evaluation makes future extraction painless.

Invest in observability early. Track latency, token usage, cost per request, and user satisfaction signals. AI products fail silently — users get bad answers and leave without complaining.

Cache aggressively. Embedding results, common queries, and even full LLM responses (with appropriate TTLs) can cut costs by 40–60% in many applications.

Plan for model upgrades. GPT-4 today, GPT-5 tomorrow. Write evaluation suites that run automatically when you switch models, so you know immediately if quality regresses.