PurchaseOrders is a Google Document AI alternative built only for purchase orders. Document AI is a powerful platform, but it ships no purchase order processor: the supported route for POs is the Custom Extractor, at $30 per 1,000 pages plus hosting, and the output is JSON you still have to turn into a spreadsheet. Here the PO fields are pre-defined and Excel, CSV, JSON, and an API are ready on day one. Try it below.
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Google Cloud Document AI is a serious document platform with excellent OCR and deep custom training. For purchase orders specifically, four things push small and mid-size US teams to look for something more finished. Details below reflect Google's published documentation and pricing as of July 2026.
Document AI's published processor list covers OCR, Form Parser, Layout Parser, Invoice Parser, Expense Parser, bank statements, W2s, and pay slips. No purchase order processor appears on it. The only mention of a PO is a purchase_order field inside the Invoice Parser schema, meaning the PO number printed on an invoice.
To get real PO fields you build a Custom Extractor: you author the schema yourself, and for best accuracy Google recommends a minimum of 50 training and 50 test documents. Custom Extractor lists at $30 per 1,000 pages, against $10 per 1,000 for the prebuilt Invoice Parser.
Custom processor hosting is listed at $0.05 per hour per deployed processor version. Left running, that is roughly $36 a month before you process a single page. It is a small number that surprises people who budgeted purely per page.
Document AI returns a Document JSON object, not a spreadsheet. The only official CSV path is the Python Document AI Toolbox library, so somebody writes code. And synchronous processing caps at 15 pages, so a longer PO batch means batch jobs, a Cloud Storage bucket, and polling a long-running operation.
PurchaseOrders is tuned for one document. The purchase order fields are already defined, so there is no schema to author, no training set to label, no processor to host, and no JSON to flatten before you see a spreadsheet.
PO number, supplier, ship-to and bill-to, order and delivery dates, payment terms, and the full line-item table come back named and typed. You do not author a schema or label 50 training documents to get there.
Download Excel or CSV with consistent headers straight from the browser. JSON is there through the API if you want it, but you are not required to write a Python script just to turn a nested entities response into rows.
A multi-page purchase order whose line table crosses page breaks is read in one step. There is no 15 page synchronous ceiling, no Cloud Storage bucket, and no long-running operation to poll and reassemble.
No GCP project, no billing account, no IAM roles, no service account keys, and no hourly hosting fee for a deployed processor version sitting idle. Sign up, upload, and read the result.
If the destination is a spreadsheet, the purchase order PDF to Excel converter gets you there in one step, and teams who want a CSV for an importer use the purchase order PDF to CSV converter. Developers who do want JSON read the purchase order API docs. The row-level accuracy question that decides most of these builds is covered in purchase order line item extraction. Comparing the other cloud OCR APIs? See the Amazon Textract alternative and Azure Document Intelligence alternative breakdowns, the packaged tools in the Nanonets alternative, and the whole field in the best purchase order OCR software.
An honest side by side for teams extracting purchase orders. Google figures are as listed on Google Cloud's public documentation and pricing pages, verified July 2026.
| PurchaseOrders | Google Document AI | |
|---|---|---|
| Focus | Purchase orders only, pre-tuned | General document AI platform |
| Purchase order processor | Yes, that is the product | None on the published processor list |
| PO route | Upload and go | Build a Custom Extractor, author the schema |
| Training documents needed | Zero | Zero-shot possible, 50 train plus 50 test to fine-tune |
| Listed page rate | Per document, free tier | $30 per 1,000 pages (Custom Extractor) |
| Idle cost | None | $0.05 per hour per deployed processor version |
| Output | Excel, CSV, JSON, Sheets, API | Document JSON, CSV via Python toolbox |
| Sync page limit | None you manage | 15 pages, then async batch and GCS |
| Interface to use it | Browser upload, on-screen review | Console test view, otherwise you build it |
| Best for | Teams that just need PO data | Engineering teams already on GCP |
Google pricing and processor details reflect Google Cloud's public pricing and documentation pages as of July 2026 and may change; confirm current rates at cloud.google.com/document-ai/pricing. Google shipped a purchase order processor in public preview in December 2022; it does not appear on the current published processor list or pricing page, and we make no claim about whether it can still be instantiated. Document AI genuinely wins on scale economics, breadth of prebuilt processors, Gemini-backed custom training, and native BigQuery and Vertex AI integration. PurchaseOrders captures purchase order data and returns Excel, CSV, JSON, or an API response. It does not create POs, route approvals, perform three-way matching, or post to an ERP. PurchaseOrders is not affiliated with, endorsed by, or sponsored by Google LLC. Google Cloud, Document AI, BigQuery, and Vertex AI are trademarks of Google LLC.
No GCP project, no schema to author, no training set to label. Upload a real purchase order and see the extracted data in seconds.
Drag in a PDF, scan, or photo of a PO from any supplier. There is no cloud project to create, no processor to deploy, and no code to write first.
Tip: Try a PO longer than 15 pages to see multi-page handling with no async batch job.
The AI reads PO number, supplier, ship-to and bill-to, order and delivery dates, line items, SKUs, quantities, units of measure, unit prices, terms, and totals automatically.
Export clean Excel, CSV, JSON, or Google Sheets, or send the data through the API into your ERP or accounting system.
Not on its current published processor list. Document AI ships processors for OCR, forms, layout, invoices, expenses, bank statements, W2s, and pay slips, but no purchase order processor. The only PO reference is a purchase_order field inside the Invoice Parser schema, which captures the PO number printed on an invoice.
Yes, but you have to build it. The supported route is the Custom Extractor: you author the field schema yourself, optionally label training documents, deploy a processor version, and parse the JSON that comes back. It works well once built. It is a project, not a setting you switch on.
As listed in July 2026, Enterprise Document OCR is $1.50 per 1,000 pages, the Invoice Parser is about $10 per 1,000 pages, and both Form Parser and Custom Extractor are $30 per 1,000 pages. Custom processor hosting adds $0.05 per hour per deployed processor version, regardless of volume.
Document AI itself returns a Document JSON object with no spreadsheet export. Google publishes a Python library, the Document AI Toolbox, that can convert tables to a Pandas DataFrame or CSV and push entities into BigQuery. That means the CSV path exists but runs through code you write and maintain.
Google's generative Custom Extractor can run zero-shot from just a schema, and few-shot with roughly five documents. For a full fine-tune, Google's documentation calls for a minimum of 50 training and 50 test documents. Labeling that set is the real cost, not the per-page rate.
The Document AI pricing page lists no free tier and no free monthly page allowance. New Google Cloud customers may have a general platform trial credit, but that is a Google Cloud offer rather than a Document AI free plan. Budget from the per-page rate and the hourly processor hosting fee.
Neither ships a purchase order model, so both mean building. Textract AnalyzeExpense lists lower per page and is simpler to call. Document AI offers deeper custom training and better volume economics at very large scale. If you have no engineers to spare, a purpose-built PO extractor skips the comparison entirely.
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