https://txtfetch.com/compare/per-page-pricing/
Per-page pricing punishes long documents.
Most extraction APIs meter by the page. That's a fair way to price OCR compute. But it means your bill scales with document length, not with how many documents you process.
Almost every document-extraction vendor bills per page. That list includes Unstructured.io, LlamaParse, AWS Textract, Azure AI Document Intelligence, and Mindee. Per-page pricing is a fair proxy for the OCR and inference cost of a page. But it has a side effect. The price of extracting a document depends on the document's length, not on how much work it is for you. To you, it's one file, fetched or uploaded, and handled once.
Consider two customers sending 100 documents a month. Customer A sends one-page invoices. Customer B sends 300-page compliance reports. Under per-page billing, Customer B pays 300x more for the same "100 documents a month" workload. Under per-document billing, both customers pay the same amount. The unit of billing matches the unit of work: one document extracted, once.
This isn't an argument that per-page pricing is dishonest. It maps cleanly to compute cost. For vendors selling OCR as commodity infrastructure, it's a defensible model. The real argument is different: the pricing unit should match how you think about your workload. If you ingest long documents, per-page billing makes your costs unpredictable. Your bill depends on content you don't control. A vendor could send you a 50-page PDF instead of a 5-page one, and your bill jumps 10x. txtfetch bills per document instead. A 300-page PDF and a one-page memo cost the same to extract. From your side, they're both "one thing I needed turned into text."
The math, worked out
Below is a representative per-page rate: the Read/OCR tier that AWS Textract and Azure AI Document Intelligence both charge. This is roughly what "commodity OCR" costs across the market. Try your own document length and monthly volume.
a typical per-page OCR vendordirectional
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Representative OCR-tier rate (AWS Textract / Azure AI Document Intelligence) · $0.0015/page
txtfetch
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a typical per-page OCR vendor'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 per-page pricing is the right call
Per-page pricing can be the better deal in some cases. If your documents are uniformly short, such as single-page forms, receipts, or IDs, per-page billing may cost less than a flat document quota. The same is true if you need per-feature pricing. Then you pay only for the specific analysis you use, such as tables, key-value pairs, or a single prebuilt model. Per-page pricing is also more transparent for pure infrastructure: you buy compute, not a product tier. See the individual comparisons below for where each vendor's model wins for your workload.
Read the individual comparisons
- txtfetch vs Unstructured.io: Open-core ETL for RAG pipelines, priced per page.
- txtfetch vs LlamaParse: Credit-metered parsing, tuned for complex PDFs.
- txtfetch vs AWS Textract: AWS-native OCR and document analysis, billed per 1,000 pages.
- txtfetch vs Azure AI Document Intelligence: Microsoft's prebuilt-model document API, billed per 1,000 pages.
- txtfetch vs Mindee: Subscription + credits for structured field extraction.
Full plan details, including the durable free tier, are on the pricing page.
Sources
- Unstructured.io: Pricing
- LlamaIndex: LlamaParse pricing
- LlamaParse free-tier credits (secondary source)
- AWS Textract: Pricing
- Azure AI Document Intelligence: Pricing
- Microsoft Q&A: Document Intelligence per-page rate consensus
- Mindee: Pricing
- Mindee: derived effective per-page rate (not a published figure)
Check the numbers yourself.
The benchmark runs against a committed corpus. You can re-run it.
See the benchmarks →