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 →
Why not just send the file to the model?
GPT, Claude, and Gemini read PDFs natively. Here is the documented token cost and what still slips past them.
see the token math →
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 →