https://txtfetch.com/compare/
Per document vs per page, compared honestly.
Named comparisons against the document-extraction tools people actually evaluate us against — capability tables, cited pricing, and a calculator for your own workload.
txtfetch vs Unstructured.io
Open-core ETL for RAG pipelines, priced per page.
per page (pay-as-you-go)
txtfetch vs LlamaParse
Credit-metered parsing, tuned for complex PDFs.
credits (per page, tiered by fidelity)
txtfetch vs AWS Textract
AWS-native OCR and document analysis, billed per 1,000 pages.
per 1,000 pages (feature-stacked)
txtfetch vs Azure AI Document Intelligence
Microsoft's prebuilt-model document API, billed per 1,000 pages.
per 1,000 pages (tiered by model)
txtfetch vs Mindee
Subscription + credits for structured field extraction.
subscription + credits (1 credit = 1 page)
Why per-page pricing punishes long documents
The explainer — the math behind every comparison above, worked out in general.
read the explainer →
Why not just run Apache Tika myself?
Tika is free. Here is the real build and runtime work behind it, sourced from our own recipe.
see what it takes →
Docling, MarkItDown, PyMuPDF4LLM, Marker, and MinerU
Five open-source parsers you'd run yourself, compared on licence, GPU need, and what self-hosting actually costs.
see the comparison →
Ready to switch? See what your code looks like after.
The call you run today, the call that replaces it, and a drop-in adapter, per vendor.
see the migration guides →
How accurate is txtfetch, really?
Pricing and capabilities are only half the picture — see the measured accuracy numbers and honest caveats.
see the benchmarks →
Check the numbers yourself.
The benchmark runs against a committed corpus. You can re-run it.
See the benchmarks →