https://txtfetch.com/compare/azure-document-intelligence/
txtfetch vs Azure AI Document Intelligence
Azure AI Document Intelligence is microsoft's prebuilt-model document api, billed per 1,000 pages. Here's an honest comparison: pricing math, capabilities, and where each tool wins.
at a glance
Azure AI Document Intelligence
- Billing
- per 1,000 pages (tiered by model)
- Rate used below
- $0.0015/pagedirectional
- Free tier
- 500 pages/month free.
txtfetch
- Billing
- per document, regardless of page count
- Rate used below
- flat monthly quota (see plans)
- Free tier
- 500 documents/month, free, ongoing (Hobby plan)
capability table
| Capability | Azure AI Document Intelligence | txtfetch |
|---|---|---|
| Billing unit | per 1,000 pages | per document |
| Format coverage | PDF, images, limited Office formats | 615 formats (Apache Tika, checked) |
| OCR for scans / images | + | + |
| Table extraction | + | +* |
| Complex layout fidelity | ~ | ~* |
| Structured field extraction | + | not yet* |
| Self-hosted option | – | –* |
| Single HTTP endpoint | ~ | + |
Vendor-published benchmarks are marked directional throughout. See sources below. * txtfetch notes: Tables come back as structured rows and cells, not flattened text. Use ?format=markdown for GFM pipe tables, or ?format=json for table elements with a cells array. Output is clean linear or structured text, not a visual layout reconstruction. That's by design, but worth knowing. Structured markdown and element-JSON document output ship today (?format=markdown / ?format=json). Schema-defined field extraction means pulling typed fields per your own schema. That feature is on the roadmap, not shipped yet. We won't claim it early. No shipped self-host or VPC artifact today. It is on the roadmap. Apache Tika itself is free to run yourself. See the page below for what that takes.
Weighing self-hosting against either API? See what running Apache Tika yourself actually takes.
the cost calculator
Per page vs per document, worked out.
A 300-page PDF is 300 units of billing to Azure AI Document Intelligence. It's one document to txtfetch. Adjust the numbers to your own workload.
Azure AI Document Intelligencedirectional
—
Read/OCR tier ($1.50 per 1,000 pages; consensus figure, see caveat) · $0.0015/page
txtfetch
—
Azure AI Document Intelligence's figure is their cheapest OCR/base tier — forms, tables, and higher-fidelity models cost more on top. txtfetch bills a flat monthly document quota: the page count inside a document doesn't change your bill.
where document intelligence wins
- Prebuilt models for common structured documents (invoices, receipts, IDs, W-2s) that extract named fields out of the box.
- Deep integration with the rest of Azure Cognitive Services and enterprise Microsoft procurement.
- This matters for regulated organizations already committed to that stack.
- Confidence scores and bounding boxes per extracted field for auditability.
where txtfetch wins
- Breadth: 615 formats, each with a real Apache Tika parser behind it. That's checked against the exact build we run, not a curated list of a dozen file types.
- One HTTP endpoint. Pass a URL or upload a file, and get text back. No SDK and no async job polling required.
- Priced per document, so a 300-page report doesn't cost 300x a one-pager.
- A durable free tier (500 documents/month, ongoing) rather than a time-boxed trial.
What we don't claim yet: schema-defined structured field extraction, or an uptime SLA. Those ship later. See the capability table above.
next step
Ready to switch? See what your code looks like after migrating from Azure AI Document Intelligence: the call you run today, the call that replaces it, and a drop-in adapter.
Check the numbers yourself.
The benchmark runs against a committed corpus. You can re-run it.
See the benchmarks →