Amazon Textract vs Azure Document Intelligence: The Real Cost of Invoice Extraction
Textract and Azure's invoice model both charge $10 per 1,000 pages in the compared US regions. If the price is tied, what decides which one costs your accounting team less?
- Textract AnalyzeExpense in US West Oregon and Azure S0 prebuilt invoice processing in East US both price at $10 per 1,000 pages in the compared pay-as-you-go tiers.
- Plain OCR is a different purchase. Reading text is not the same as returning supplier fields, totals and line items in an invoice schema.
- At these rates, exception handling can dominate the API bill. Choose the service that works with your documents and existing cloud, then measure the human work left.
The invoice API price is tied in the selected US regions
Do not switch clouds merely to save money on the invoice-processing rate in this comparison. For the stated prebuilt invoice features and US regions, both platforms reach $100 at 10,000 pages. A different exception rate or integration cost can easily matter more than that API line.
The cheaper-looking alternative is often not the same product. An OCR endpoint can return text and locations without delivering the structured invoice fields your accounting workflow needs. If a second system must identify suppliers, taxes and line items, its model and validation work belong in the budget.
A developer considering Azure extraction for invoices and other documents was weighing model options, not merely asking whether text could be read. Define the required output before selecting the lowest page rate.
Costs at 1,000, 10,000 and 100,000 pages
These are API charges for the named invoice features, before free-tier allowances, volume commitments, storage, orchestration and human review. AWS uses US West Oregon's first-million-page rate; Azure uses the East US S0 prebuilt meter.
| Pages processed per month | Textract AnalyzeExpense | Azure prebuilt invoice | What is being bought |
|---|---|---|---|
| 1,000 | $10 | $10 | Prebuilt invoice field and line-item extraction |
| 10,000 | $100 | $100 | The same feature at ten times the page count |
| 100,000 | $1,000 | $1,000 | The same feature before separate operating costs |
AWS's AnalyzeExpense pricing example lists $0.01 per page in Oregon for the relevant volume. Microsoft's official Retail Prices API lists $10 per 1,000 pages for S0 Pre-built Pages in East US. The Azure pricing categories separate Read, prebuilt, custom and add-on processing.
Count pages, not just files. Ten thousand three-page invoices produce 30,000 processed pages under an all-pages processing assumption, or $300 at the stated rate. Reprocessing the same pages creates another workload; the original file count does not remove it.
Invoice extraction must return the fields your accounting system uses
Textract's invoice and receipt documentation describes summary fields, line items, confidence and geometry. Azure's prebuilt invoice model returns its own structured invoice schema. These are the outputs to compare, not two screenshots of recognized paragraphs.
List mandatory fields with their downstream formats: supplier identity, invoice number, invoice date, currency, subtotal, tax, total and each required line-item column. Specify what happens when a field is absent or conflicting.
For example, “extract the vendor” is incomplete if the accounting system needs a unique supplier ID. A recognized name still needs matching to the approved supplier record. Similarly, a line-item description is not a valid purchase-order match unless quantities and identifiers meet your reconciliation rules.
A $100 API bill can sit beside $1,000 of review work
Use a simple operating scenario: 10,000 one-page invoices, 10% routed for manual review, two minutes spent on each reviewed invoice and a loaded hourly review cost of $30.
10,000 invoices × 10% reviewed × 2 minutes ÷ 60 × $30
= $1,000 of monthly review work
$100 extraction API + $1,000 review work
= $1,100 before the remaining operating costsThe review rate and labor inputs are planning assumptions. The calculation shows why a lower exception rate can be more valuable than a small API saving. Reducing that scenario's review rate from 10% to 5% removes $500 of modeled review work without changing the page count.
A production user discussing roughly 50,000 documents per month asked about accuracy and human review together. Those belong in the same purchase test. Do not treat one person's reported accuracy as the rate your own supplier documents will achieve.
Test field correctness, not whether the document looked readable
Build a representative set from the suppliers you actually pay. Include clear digital PDFs, scans, rotated images, multipage invoices, different currencies and formats with discounts or unusual tax layouts. Keep known correct values beside each test document.
Score mandatory fields separately. An accurately recognized address does not compensate for a wrong total or invoice number. Check arithmetic relationships and duplicates as well as text matches. Decide which failures can be corrected automatically and which must enter the exception queue.
Use confidence as a routing input, not as proof that a value is correct. A high-confidence value can still be the wrong field for your business purpose. Track false acceptance as well as rejected documents: the system that accepts more invoices is not necessarily the system that makes fewer expensive mistakes.
Pages, API modes and add-ons need separate treatment
Check the request mode and input constraints against the workload. Textract's limits documentation distinguishes synchronous and asynchronous processing. A batch archive and a single interactive upload need not use the same integration path.
Azure's model overview separates prebuilt models from custom extraction and additional capabilities. Do not add a query-field or high-resolution requirement and leave the original prebuilt-only cost label unchanged.
Record pages sent to each paid API or feature. If a failed workflow reruns extraction from the beginning, the model can spend again before the accounting integration fails in the same place. Store completed extraction results and use a document identifier so a retry can resume the unfinished step.
Your existing cloud is a useful tiebreaker
Choose Textract first when your document intake, storage, identity and processing workflow already run on AWS. Choose Azure Document Intelligence first when those components already run on Azure. Reusing approved access patterns and existing monitoring can be more valuable than migrating a tied page rate.
The complete quote should add object storage, queues, compute, connectors and exception handling. Include the work of adapting the extraction schema to your accounting system. Inspect the relevant AWS security controls or your approved Azure deployment configuration rather than assuming an OCR purchase establishes the whole document-security arrangement.
Choose the workflow with fewer costly exceptions
At the compared rates, neither platform wins on the invoice API price. Existing cloud fit is the practical first filter; results on your own mandatory fields decide the purchase. Use the same document set, count processed pages and record human correction time.
The successful system is the one that sends correct, deduplicated, approved records into accounting with the least remaining work. A cheap API that leaves every invoice waiting for someone to fix it has not automated invoice processing.
Is plain OCR the same as invoice extraction?
No. Text detection and invoice-field extraction are separate feature choices.
Is one PDF one billed page?
Not when the PDF contains several processed pages. Budget the page workload.
Does the API price include human review and accounting integration?
No. Those are separate operating costs in the workflow.