Part XI — Performance & Cost Optimization (Making It Fast and Affordable)

The Optimization Mindset

So far, we've focused on getting things to work—correctness, reliability, and safety. Now we turn to efficiency. In the world of LLMs, efficiency usually means two things: speed (latency) and money (tokens).

Unlike traditional software where optimization is often about CPU cycles and memory, AI optimization is about context management. Every token you send costs money and takes time to process. Every token you generate costs even more money and takes even more time.

What this part covers

This section breaks down the economics and physics of building with AI Studio:

• Token Economics: Understanding where your money goes, how to calculate costs, and how to reduce them without making your model dumber.
• Latency Optimization: Techniques to make your app feel instant, from streaming and caching to parallelization.

When to optimize (and when to wait)

The best time to optimize is when you move from "prototype" to "pilot."

• Prototype phase: Use the smartest, largest model (e.g., Gemini 1.5 Pro). Ignore token counts. Just verify the logic.
• Pilot phase: Measure the average cost per task. If it's too high, try a smaller model (Flash) or compress your prompts.
• Production phase: Implement aggressive caching, semantic similarity filtering, and strict token budgets.

Where to go next

• 32. Token Economics for Builders
• 33. Latency Optimization