Google Document AI is a set of cloud processors you enable in a GCP project and call from code. If the job in front of you is turning bank statement PDFs into a clean spreadsheet, upload one here and download the Excel file instead.
Upload your bank statement
Drop file here or click to upload
PDF, JPG, PNG, BMP, HEIC, TIFF, MT940
Uploading...
Document AI is capable infrastructure with a strong OCR engine behind it. People shop elsewhere when they discover that getting one statement into Excel means standing up a cloud project first, and that no processor in the catalog is shaped like a bank statement.
You create a GCP project, enable the API, choose a region, create a processor, issue a service account key, and write a client call. That is real work before the first transaction appears, and it does not get shorter for the second user on your team.
Form Parser finds key-value pairs and tables. Layout Parser segments a document into chunks for retrieval. Neither one knows that a statement runs across pages, carries a running balance, or has to foot against a closing figure printed on the last page.
Form Parser and Custom Extractor are both $30 per 1,000 pages up to a million pages a month, Layout Parser is $10, and Enterprise Document OCR is $1.50. A single monthly business statement is often eight to twelve pages, so page counts climb quickly.
The pricing page does not publish a standing free allowance for Document AI processors. You are not billed for failed requests, but there is no ongoing zero-cost band to evaluate against a real year of statements.
If the generic processors do not give you the fields you want, the answer is a Custom Extractor, which means collecting sample statements, labeling fields, training, and evaluating. That is a machine learning project, not a purchase.
Document AI hands back a structured document object. Converting that into a columned XLSX with signed amounts, parsed dates, and an intact running balance is code you write and then maintain as bank layouts change.
BankXLSX skips the platform layer entirely and delivers the thing most Document AI projects are trying to reach: a correct, reconciliation-ready spreadsheet.
Date, description, amount, and running balance land in columns in transaction order, ready to reconcile.
Nothing to enable, configure, or authenticate. Open the page, upload the statement, download the file.
Transactions that continue across a page break stay in order instead of arriving as separate page-level tables.
Scanned statements and phone photos convert directly, in PDF, JPG, PNG, BMP, HEIC, and TIFF.
Built around the layouts, date formats, and balance columns US institutions actually print.
Export QBO, OFX, QFX, and QIF as well as Excel and CSV, so the file imports into QuickBooks, Xero, or Quicken.
No console, no service account, no credit card.
Drop in a PDF or scanned statement. Multi-page and multi-month files work as one document.
Tip: Password-protected PDFs are supported.
Transactions are extracted into structured columns with the balance preserved.
Tip: Scans are handled by built-in OCR.
Take the Excel or CSV, or export an accounting format for your ledger.
Tip: No parsing code to write or maintain.
An honest side-by-side, including where Document AI is clearly the better fit.
Convert client statement PDFs without waiting on a cloud build.
Assemble workpapers from multi-month statements in minutes.
Get statement data into Excel for reconciliation and cash reporting.
Spread borrower history without training a custom model first.
Document AI is a platform you build on and BankXLSX is a tool you use. Google sells the ability to read and structure documents at cloud scale, with processors you compose into a pipeline. That is genuinely powerful when documents arrive continuously and land in BigQuery or an internal system. It is the wrong shape when one person has forty statement PDFs and a deadline.
| What you are comparing | BankXLSX | Google Document AI |
|---|---|---|
| Product type | Finished browser tool for statements | Cloud document processing platform |
| Who uses it | Bookkeepers, accountants, business owners | Developers and data teams on Google Cloud |
| Setup required | None, upload and download | GCP project, enabled API, processor, service account |
| Output | XLSX and CSV, plus QBO, OFX, QFX, QIF | Structured JSON document object |
| Published price | Self-serve plans, free to try | Form Parser and Custom Extractor $30, Layout Parser $10, Enterprise OCR $1.50 per 1,000 pages |
| Free allowance | Free to try, no credit card | None published for processors |
| Statement-specific model | Yes, that is the whole product | No, general processors or a trained Custom Extractor |
| Running balance | Preserved as its own column | Whatever your code reconstructs |
| Training required | None | Labeled samples if you need a Custom Extractor |
| Best for | People converting statements as a task | Teams building document pipelines at scale |
Google prices taken from the Document AI pricing page in July 2026 for the first one million pages a month. Vendors change plans, so confirm current figures before you buy.
The OCR underneath it is among the best available, particularly on poor scans, faxed pages, and handwriting. Layout Parser is a smart piece of engineering for anyone chunking documents for retrieval or a language model, and Custom Extractor gives you a real path to fields no generic model knows about. If your documents already live in Cloud Storage and your analytics already live in BigQuery, keeping extraction in the same account removes a lot of glue.
It is also priced sensibly for what it is. Enterprise Document OCR at $1.50 per 1,000 pages is inexpensive for raw text at volume. The cost that surprises people is never the API line, it is the engineering around it.
Invoice and receipt processors work because those documents have a fixed set of fields on a small number of pages. A statement is a table-reconstruction problem instead. Every row matters, the order matters, the sign of each amount matters, and the running balance has to tie to the closing figure. A generic table parser will happily return eleven separate tables for an eleven page statement, in the wrong order, with the header row repeated, and with the balance column split because a bank printed it in a slightly different position on the continuation pages.
Fixing that is not model work, it is domain work: knowing that this bank prints debits in a separate column, that this one repeats the account summary on every page, that this one wraps long descriptions onto a second line. There are dozens of these rules and they are the actual product. That is what you buy when you buy a converter and what you build when you buy a platform.
Add four things to the per-page rate. The build, which is the ingestion, the calling code, the retry logic, and the assembly of JSON into a spreadsheet. The tuning, which is every bank layout that breaks your assumptions. The validation, because somebody has to confirm the balance foots before the numbers go into a return or a loan file. And the maintenance, because banks redesign statements and processors get versioned. Teams that price all four honestly usually conclude that a platform is right above a certain volume and clearly wrong below it.
Use Document AI when extraction runs unattended inside your own systems, volume is continuous, and the output feeds software rather than a person. Use a converter when a human is doing the work anyway and the deliverable is a file. If you want automation without building the statement layer, the bank statement converter API returns statement-shaped output. To test on your own worst PDF right now, the bank statement converter is free to try, and afterwards you can categorize the transactions or compare the wider field in the bank statement to Excel software roundup.
It can read them and detect tables using Form Parser or Layout Parser, and you can train a Custom Extractor for specific fields. There is no dedicated bank statement processor, so reconstructing an ordered ledger with an intact running balance is still work you do in code.
On the published rates for the first million pages a month, Form Parser and Custom Extractor are $30 per 1,000 pages, Layout Parser is $10, and Enterprise Document OCR is $1.50. Above a million pages, the first two drop to $20. Prices checked in July 2026.
The pricing page does not publish a standing free allowance for Document AI processors. Failed requests returning 4xx or 5xx are not billed, but there is no ongoing zero-cost band, so evaluation is a paid exercise once any trial credit is used.
No. It returns a structured JSON document object containing text, entities, tables, and layout information. Producing an XLSX or CSV means writing an application that maps that response into columns and formats dates and amounts correctly.
Document AI is a platform that gives software the ability to read documents. A converter is a finished application that already knows what a bank statement is, stitches it across pages, keeps the running balance, and returns a spreadsheet a person can use immediately.
On a general platform, often yes, which means gathering labeled sample statements and running a training and evaluation cycle. A purpose-built converter needs no training because the bank layouts are already handled by the product.
They are close enough that price and existing cloud footprint usually decide it. Google has the stronger OCR on difficult scans and Layout Parser, while AWS offers Analyze Lending, which is aimed at loan packages. Neither returns a finished spreadsheet.
Yes. Upload the PDF to a browser converter and download the XLSX. There is no project to create, no API to enable, and no service account key to manage, which is the practical difference between a tool and a platform.
The AWS OCR API, compared side by side.
The desktop OCR suite, compared honestly.
A document AI platform sold by quote, compared.
A developer OCR API with published pricing.
Convert PDF statements straight to Excel.
From the same family of tools
Get started converting bank statements to spreadsheets.
USD
per month
billed as
$288 yearly
Choose speed vs accuracy when extracting
| Base AI Faster | 2,500 pages |
| Pro AI Best accuracy | 500 pages |
Scale statement conversion across your team with automation.
USD
per month
billed as
$888 yearly
Choose speed vs accuracy when extracting
| Base AI Faster | 10,000 pages |
| Pro AI Best accuracy | 2,000 pages |
Enterprise-grade bank statement conversion and controls.
USD
per month
billed as
$ yearly
Choose speed vs accuracy when extracting
| Base AI Faster | pages |
| Pro AI Best accuracy | pages |
