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If your product touches PDFs or scanned documents, this 24 minutes will save you from an expensive vendor lock-in mistake. Barrow lays out a clean two-axis decision framework (text vs. full structure, API vs. self-hosted) with real per-page pricing across cloud providers, startups, and open VLMs. The core advice is refreshingly anti-benchmark: test on your own pages and look at the output.

• OCR is sticky infrastructure — once your app is built on a specific model's output quirks, switching vendors is painful, so choose deliberately.
• Pricing spread is huge: cloud providers charge $0.60–$1.50 per 1,000 pages for text but $10–$15 with tables/forms; self-hosted VLMs on an H100 run $0.20–$3 per 1,000 pages at ~10,000 pages/hour.
• Ignore public benchmarks — run 50–100 representative pages from your actual data through candidates and visually inspect the results.
• Preserving structure as markdown/HTML is a free downstream performance win, because LLMs are trained on structured text, not jumbled line output. Also check licenses: some models carry a $2M revenue cap.

Parte de Edición Nº 003: Evals Are the New PRDs: How Anthropic, Bridgewater, and SonderMind Actually Ship AI