AI SDK by Vercel is a powerful toolkit for building AI-powered applications in TypeScript. It’s particularly popular for Next.js and React developers.
Let’s explore how AI SDK handles structured extraction and where the complexity creeps in.
AI SDK makes structured data generation look elegant at first:
Clean and simple! But let’s make it more realistic by adding education:
Still works! But… what’s the actual prompt being sent? How many tokens is this costing?
Your manager asks: “Why did the extraction fail for this particular resume?”
You start digging through the AI SDK source code to understand the prompt construction…
Now your PM wants to classify resumes by seniority level:
But wait… how do you tell the model what “junior” vs “senior” means? Zod enums are just string literals:
Your clean abstraction is leaking…
Your company wants to use different models for different use cases:
You want to test your extraction logic:
As your app grows, you need:
Your code evolves into:
The simple AI SDK call is now buried in layers of infrastructure code.
BAML was designed for the reality of production LLM applications. Here’s the same resume extraction:
Notice what you get immediately:
Use it in TypeScript:
With BAML’s VSCode extension, you can:
No mocking required - you’re testing the actual prompt and parsing logic.
AI SDK is fantastic for building streaming AI applications in Next.js. But for structured extraction, you end up fighting the abstractions.
BAML’s advantages over AI SDK:
What this means for your TypeScript apps:
AI SDK is great for: Rapid prototyping, simple use cases BAML is great for: Production structured extraction, multi-model apps, cost optimization, streaming UIs with semantic streaming
We built BAML because we were tired of elegant APIs that fall apart when you need production reliability and control.
BAML does have some limitations:
Ready for bulletproof structured extraction with full control? Try BAML.