What developers should compare
OCR accuracy matters, but production teams should also compare table extraction, JSON schemas, confidence calibration, webhook support, SDK quality, rate limits, evidence links, pricing, and whether the API can route uncertain fields to review.
| Vendor type | Good fit | Watch closely |
|---|---|---|
| Cloud OCR primitives | High-scale text and table recognition | You build validation, review, and audit trail |
| Developer extraction APIs | Invoices, receipts, IDs, bank statements, and typed JSON | Custom document support and citation quality |
| IDP platforms | Workflow-heavy operations teams | Implementation time, seats, minimums, and API ergonomics |
| Cogneris | API-first extraction with validation, citations, and review routing | Best fit when structured output matters more than raw text |
OCR API checklist
Before buying, run a benchmark with real documents. Include scanned PDFs, mobile photos, tables, handwriting, checkboxes, long packets, missing fields, and documents that should fail validation. Measure clean-through rate, reviewer time, webhook reliability, schema stability, and cost per resolved case.