Extend vs Datalab vs Other Document Parsers

Every vendor here can turn an invoice into JSON. That's not what decides it.

What decides it is everything around that step. Can it split a stack of scanned invoices into separate documents? Can it show you where on the page each value came from? Can it tell you whether this month's version is more accurate than last month's? And can it send the bad ones to a person before they get paid?

This is part 5 of the Document AI series. It compares Extend, Datalab, Google Document AI, Azure Document Intelligence and Mistral on one clearly described job, using only their docs and published prices, read on September 26, 2026. Nothing here was run. There are no accuracy numbers because none were measured, and a vendor's own benchmark isn't a test of whether it works on your invoices.

The job

Every row below answers the same question: how well does this vendor handle this one job?

• Volume. 1,000 invoices a month, two pages each on average, so 2,000 pages.
• What comes in. Mostly PDFs created on a computer and downloaded from supplier portals. Some phone photos and scans. Every so often, one scanned PDF holding several invoices and a credit note.
• What comes out. The ten fields from part 4: invoice number, vendor, PO, invoice date, due date, document type, line items, subtotal, tax, and total due. The running example is the made-up Harbor Lane invoice from part 2.
• Safety checks. Every value can be traced to the page, the math gets checked, and anything that fails is reviewed by a person before it reaches the books.

Your job will be different, and so will the right answer. Put your own volume and page count into the document processing cost calculator before you trust any cost below.

What each vendor's docs say it can do

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.

A few terms in that table: "bounding boxes" are the boxes marking where a value sits on the page. "Ground-truth evaluation" means testing against documents where you already know the right answers. "BYOC" means bring your own cloud, running the vendor's software in your own cloud account.

"Not in the pages read" means exactly that. It doesn't mean the feature doesn't exist. Ask the vendor, and ask for the link to where it's documented.

Who each one suits

Extend covers the most of this job in one product. Splitting, classifying and extracting all use the same versions. Its evaluation sets score each version field by field. And a workflow can run a check and park anything that fails in a review queue, which keeps both the original and the corrected values.

The cost is setup. You need a schema file, an evaluation set and a workflow before the first invoice reaches your books. It suits a team that'll run this pipeline for years and wants testing built in.

Datalab is the builder's choice. The API can convert, extract with a citation for each field, and split documents, and published pipeline versions never change. Its underlying tools are public: Marker and Chandra are on GitHub, and part 3 of this series walks through them.

What its docs don't describe is a review queue, so you build that part yourself. Part 8 covers how. It suits a team that already owns the app around the extraction, or that needs to run everything on its own servers through the on-prem container or the open weights.

Google Document AI has a ready-made Invoice parser and a custom extractor that uses generative AI. Its console scores accuracy for each field (precision, recall and F1) and picks the confidence cutoff that scores best. The catch is that the Invoice parser's list of fields is Google's, not yours. It suits a team already on Google Cloud that would rather use a standard invoice format than write its own.

Azure Document Intelligence offers prebuilt-invoice, with a list of fields Microsoft maintains. The invoice page, updated August 15, 2026, says it supports 27 languages, and its custom classification can find where one document ends and the next begins in a batch file. Microsoft's own overview now pitches it for predictable extraction from structured documents, and points to Content Understanding for analysis done by LLMs. It suits a team already on Azure with standard invoices.

Mistral is the lightest option here. It's a document model that returns location boxes, block types and confidence scores, plus a Document AI tier that returns JSON for your own schema. Everything around the extraction is yours to build. It suits a team that wants a cheap, capable model and already has its own pipeline, like the n8n pipeline in part 6.

What the job costs at list price

These figures multiply each vendor's published per-page price by 2,000 pages a month, as of September 26, 2026. They leave out taxes, free allowances, extra compute fees, and, most importantly, the cost of people reviewing results.

Rate sources: Datalab pricing and extraction modes, Extend credits, Google Document AI pricing, Mistral OCR 4, and Azure pricing, whose per-page figures didn't show up in the page as served on September 26, 2026.

Splitting batch scans costs extra on the Extend and Datalab rows. Extend's Light split with parse is $0.0125 a page, and Datalab's page-level splitting is $0.50 per 1,000 pages.

The range looks wide, from $10 to $150. It stops mattering as soon as a person starts checking invoices.

Use the calculator's defaults: 8% of documents need review, six minutes each, at $45 an hour. Review on this job then costs $360 a month, more than any row in the table. A vendor that sends half as many invoices to review is worth several times its higher page price. That's why the next three parts of the series are about measuring accuracy and running review, not about turning pages into text.

How to decide with your own documents

The only fair comparison is the one you run yourself. Keep it small, and write down exactly what you did.

1. Build a test set. Fifty invoices is enough to start. Include your worst scans, a file with several invoices in it, a credit note, and at least one invoice where the printed total doesn't match its lines. Write down the correct ten fields for each one by hand.
2. Try two vendors, not five. Pick from the notes above. Use the same schema, lock each vendor to a specific model version, and use the settings you'd actually run in production.
3. Score each field, then each document. Part 7 gives you a scorer that does both. A vendor that gets 97% of fields right but gets total_due wrong on 1 invoice in 10 fails the job.
4. Count how many would go to review. Run the same math check on both vendors' output and count the failures. Put that rate into the calculator.
5. Write down what you did. The date, the versions, how you picked the invoices, and the exact settings. Rerun the same invoices before you switch to any new version.

For the buying questions around this, like how long data is kept, where it's processed, and how to undo records that were already written, the series started with how to choose an enterprise document tool. For the same approach applied to choosing an agent testing tool, see comparing agent evaluation tools.

Next in the series: an invoice pipeline in n8n. The previous part is Extend in practice: schemas, evals and workflows.