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AI as SaaS: The Technical Foundation

Build AI Products That Actually Scale

You validated the idea. You ran the cost math. Now build it correctly — the architecture patterns, prompt caching, observability, and iteration loops that separate AI products that scale from ones that collapse under their first real users. Course 2 of the AI as SaaS series.

12 lessons~96 min total

Lessons are shown in recommended order. Complete them in sequence for the best experience — or jump to any lesson.

Lesson 512·7 min read

Single Agent vs. Multi-Agent — Choosing the Right Shape

The first architectural decision in Course 2 is also the most consequential: should this AI feature use one LLM call or many? Most teams choose wrong — not because multi-agent is bad, but because they skip the cost math.

Lesson 513·8 min read

The Classifier-Router-Specialist Pattern

The most powerful pattern in production AI SaaS is also one of the most underused. When one prompt tries to do everything, it gets worse at everything. Classify the input, route to a specialist, get a focused answer.

Lesson 514·8 min read

Tool Calling as Capability Design

A search tool does not make your AI better at searching — it makes your AI a completely different product. Before writing a tool, ask what capability it gives the AI that changes the user experience. That question is the whole design.

Lesson 515·7 min read

Prompt Caching — Cutting Your AI Bill by 50%+

Anthropic stores the prefix of your request. On subsequent calls with the same prefix, you pay 10% of normal input token price. Clarity cut its daily input-token cost from roughly $8 to about $2 — a 70%+ reduction — from one architectural decision. Here is exactly how.

Lesson 516·8 min read

Request Design and Token Budgeting

Every token costs money. Request design is cost control. Here is the anatomy of a real request, three strategies for managing conversation history, and the context compression pattern that cuts your bill by two-thirds before you touch the AI call.

Lesson 517·9 min read

Semantic Cache vs. Response Cache vs. Prefix Cache

Three caches. Each catches a different class of repeat. Most AI products add one and wonder why costs are still high. Here is how all three work, what each one catches, and how to stack them for maximum savings.

Lesson 518·9 min read

Auth and Rate Limiting for AI SaaS

In traditional SaaS, attackers want your data. In AI SaaS, they want your API credits. The threat model is different — and so is the architecture. Here are the four auth mistakes that cost money, the gate sequence that stops them, and a token-aware rate limiter you can drop in today.

Lesson 519·9 min read

Observability for AI Systems

Standard APM tracks latency, errors, and throughput. That is necessary but not sufficient for AI systems. You also need to observe what you asked, what it said, what it cost, and whether it was any good. Here is the four-pillar framework and the one alert that pays for itself every month.

Lesson 520·8 min read

Error Handling and Graceful Degradation

AI APIs fail. OpenAI had four major outages in 2024. Anthropic has had rate limit events. Your product will hit 503s, 429s, timeouts, and content policy refusals. The question is not if — it is when, and what happens to your users.

Lesson 521·7 min read

System Prompt Versioning and Regression Testing

Most teams treat system prompts like environment variables — set it and forget it. The teams that win treat them like code: versioned, tested, and reviewed before every change. This lesson is the discipline no one teaches.

Lesson 522·7 min read

A/B Testing AI Behavior and Safe Rollout

Shipping a prompt change is shipping a behavior change to every user simultaneously. That is riskier than shipping a UI change. Treat it with the same rigor: feature flags, staged rollout, kill switch ready before the test runs.

Lesson 523·9 min read

Shipping Production AI SaaS — The Full Checklist

The deployment checklist no one gives you before you charge money. Every item represents a real incident that happened to a real AI SaaS product. All five sections green means ready to launch. Any red means wait.