Extend vs Datalab vs Other Document Parsers
ZarifEvery vendor in this comparison can turn an invoice into JSON. The differences that decide a choice are what happens around the extraction: splitting batches, tracing a value to the page, measuring accuracy over time, and routing the bad ones to a person.
This is part 5 of the Document AI series. It compares Extend, Datalab, Google Document AI, Azure Document Intelligence, and Mistral against one disclosed task, using only their documentation and published pricing, read on September 26, 2026. Nothing here was run. There are no accuracy numbers because none were measured, and a vendor's own benchmark is not your acceptance test.
The task
The comparison uses one job so every row answers the same question.
- Volume. 1,000 invoices a month, averaging two pages, so 2,000 pages.
- Inputs. Mostly born-digital PDFs from supplier portals, some phone photos and scans, and now and then a scanned PDF with several invoices and a credit note in it.
- Output. The ten-field schema from part 4: invoice number, vendor, PO, invoice and due dates, document type, line items, subtotal, tax, and total due. The running example is the synthetic Harbor Lane invoice from part 2.
- Controls. Every value traceable to the page, arithmetic checked, failures reviewed by a person before anything reaches the ledger.
Your task will differ, and the right answer will differ with it. Swap in your own volume and page count in the document processing cost calculator before trusting any cost figure below.
What each vendor documents for this task
| Need | Extend | Datalab API | Google Document AI | Azure Document Intelligence | Mistral |
|---|---|---|---|---|---|
| Your own schema | JSON Schema with nullable fields, currency and date types | JSON Schema via page_schema | Custom extractor, or the fixed Invoice parser | Fixed prebuilt-invoice schema, custom and generative extraction | Custom schema through Document AI |
| Split a batch scan | /split with typed splits and page ranges | /segment at page or block level | Custom splitter | Custom classification finds boundaries | Not in the pages read |
| Tell invoice from credit note | /classify, or an enum in the schema | Named segments, or an enum in the schema | Custom classifier | Custom classifier | Not in the pages read |
| Trace a value to the page | Citations with bounding boxes when enabled | Citations to block IDs per field | Not in the pages read | Tables and cells with bounding boxes | Bounding boxes on OCR output |
| Confidence per field | ocrConfidence, optional Review Agent score | Balanced and accurate modes add verification and reasoning, plus a confidence-scoring recipe | Confidence threshold tuned during evaluation | Confidence on tables and cells | Per-page and per-word confidence |
| Ground-truth evaluation | Evaluation sets per processor, API and Studio | Forge Evals compares parse configurations side by side | Per-label precision, recall, F1 in the console | Not in the pages read | Not in the pages read |
| Versioned configuration | Draft and immutable processor and workflow versions | Immutable pipeline versions | Processor versions | Model versions per API release | Not in the pages read |
| Human review built in | HUMAN_REVIEW workflow step with a dashboard queue | Not in the pages read | Not in the pages read | Not in the pages read | Not in the pages read |
| Self-host | BYOC and hybrid deployment options | On-prem container, open-source Marker and Chandra | Not in the pages read | Docker containers listed for v2.1 models | Self-hosted container |
Sources, read September 26, 2026. Extend: agent context, human review, confidence scores. Datalab: extraction, segmentation, Forge Evals, pipeline versioning, on-prem. Google: overview, evaluation. Azure: overview, invoice model. Mistral: OCR 4 announcement.
"Not in the pages read" means exactly that. It is not a claim that the feature does not exist. Ask the vendor, and ask for the documentation link.
Where each one fits the task
Extend documents the most of this task in one product. Split, classify, and extract share one versioning model, evaluation sets score each processor per field, and a workflow can run a validation rule and park failures in a review queue that records the original and corrected values. The price of that is setup: a schema file, an evaluation set, and a workflow before the first invoice reaches your ledger. It fits a team that will run this pipeline for years and wants the evaluation loop built in.
Datalab is the builder's option. The API covers convert, extract with per-field block citations, and segment, and published pipelines are immutable. Its parsing lineage is public: Marker and Chandra are on GitHub, and part 3 of this series walks through them. What it does not document is a review queue, so you build that step yourself, which part 8 of the series covers. It fits a team that already owns the surrounding application, or that needs self-hosting through the on-prem container or the open weights.
Google Document AI has a prebuilt Invoice parser and a generative custom extractor, plus a console that computes per-label precision, recall, and F1 and picks a confidence threshold that maximizes F1. The Invoice parser's schema is Google's, not yours. It fits a team already on Google Cloud that would rather adopt a standard invoice schema than write one.
Azure Document Intelligence offers prebuilt-invoice with a field schema maintained by Microsoft. The invoice page, updated August 15, 2026, says it supports 27 languages, and custom classification can find boundaries in a batch file. Microsoft's own overview now positions it for deterministic extraction from structured documents and points to Content Understanding for LLM-powered analysis. It fits a team already on Azure with standard invoices.
Mistral is the thinnest layer here: a document model with bounding boxes, block types, and confidence scores, and a Document AI tier that returns JSON for a custom schema. Everything around extraction is yours to build. It fits a team that wants a cheap, capable model and has its own orchestration, such as the n8n pipeline in part 6.
What the task costs at list price
These figures are arithmetic on each vendor's published per-page rates for 2,000 pages a month, as of September 26, 2026. They exclude taxes, free allowances, compute surcharges, and, most importantly, human review.
| Vendor and configuration | Published rate | Monthly base cost |
|---|---|---|
| Datalab, fast extraction with its fast parse | $6 + $4 per 1,000 pages | $20 |
| Datalab, balanced extraction with the default accurate parse | $15 + $10 per 1,000 pages, plus possible compute fees | $50 |
| Extend, Light extract with automatic parse | $0.015 per page | $30 |
| Extend, Performance extract with automatic parse | $0.0625 per page | $125 |
| Extend, Performance plus Review Agent | $0.075 per page | $150 |
| Google, Invoice parser | $0.10 per document of up to 10 pages | $100 |
| Google, Custom extractor | $30 per 1,000 pages, first tier | $60 |
| Mistral, Document AI | $5 per 1,000 pages | $10 |
| Azure, prebuilt invoice | No rate in the static pricing page on this date | Check the Azure calculator |
Rate sources: Datalab pricing and extraction modes, Extend credits, Google Document AI pricing, Mistral OCR 4, and Azure pricing, whose per-page figures did not appear in the page as served on September 26, 2026. Batch splitting adds to the Extend and Datalab rows: Extend's Light split with parse is $0.0125 a page, and Datalab's page-level segmentation is $0.50 per 1,000 pages.
The spread looks large, from $10 to $150. It stops mattering the moment a person looks at invoices. At the calculator's default of 8% of documents needing review, six minutes each, and $45 an hour, review on this task costs $360 a month, more than any row above. A vendor that sends half as many invoices to review is worth several times its per-page premium. That is why the next three parts of the series are about measurement and review rather than parsing.
How to decide with your own documents
The only fair comparison is the one you run. Keep it small and disclosed.
- Build a labeled sample. Fifty invoices is enough to start, chosen to include your worst scans, a multi-invoice batch, a credit note, and at least one invoice where the printed total disagrees with its lines. Write the correct ten fields for each by hand.
- Run two vendors, not five. Pick from the fit notes above. Use the same schema, pinned model versions, and the default settings you would actually ship.
- Score per field, then per document. Part 7 gives a scorer that does both. A vendor that is right on 97% of fields but wrong on
total_duefor one invoice in ten fails the task. - Count the review load. Apply the same validation rule to both outputs and count the failures. Put that rate into the calculator.
- Record what you did. Date, versions, sample selection, and the exact settings. Rerun the same sample before any version change.
For the buying questions that sit around this, such as retention, processing location, and rollback of records already written, the series began with where Datalab fits among enterprise tools. For the general pattern of choosing between evaluation platforms on one task, see comparing agent evaluation tools.
Previous: Extend in practice: schemas, evals and workflows. Next: An invoice pipeline in n8n.