Why BAML? (docs.boundaryml.com)

BAML is a developer platform for building robust, production-grade LLM data pipelines that replaces the dozens or hundreds of glue-code lines you’d normally write for prompt testing, error handling, multi-model support and type safety. Instead of paying for repeated API calls while iterating, BAML provides a VSCode playground that runs exact prompts locally (no tokens consumed), shows the prompt and token usage, saves regression test cases, and previews how prompts change when types change. It also bundles built-in production features — retry/fallback policies, cost tracking, A/B testing, monitoring — removing much of the operational overhead teams normally reimplement. Technically, BAML’s standout is Schema-Aligned Parsing (SAP): rather than failing on malformed outputs it applies custom edit-distance transforms to coerce LLM outputs into a target schema, following Postel’s Law. That plus token-aware prompt engineering yields claimed wins — SAP+GPT-3.5 turbo can outperform GPT-4o structured outputs while cutting cost. Other highlights: an 80% more token-efficient schema format vs JSON Schema, automatic generation of fully typed clients for multiple languages, and “semantic streaming” that streams structured partial data with type guarantees for UI loading states. For teams extracting structured data (e.g., resumes), BAML promises faster iterations, lower costs, and fewer brittle plumbing layers.