AWS Textract vs Google Document AI: Which One to Choose in 2026

Last updated: July 2026

Choose AWS Textract if your stack is AWS-native and your documents are forms, tables, expenses, IDs or long PDFs: it wins on measured parsing quality and page limits. Choose Google Document AI for OCR language breadth, zero-shot custom extractors and GCP integration. Both share the same caveats: no workflow layer, thin accuracy measurement, cloud-only deployment, and US jurisdiction regardless of region.

Everything below is sourced from the official AWS Textract pricing page, the Amazon Textract Developer Guide, Google Cloud's Document AI pricing and limits documentation, all checked in July 2026, plus one independent dataset: the anyformat document AI benchmark (2026), which measured both services on 1,000+ real documents.

At a glance

OCR and parsing quality

Textract wins on measured accuracy; Document AI wins on language breadth, and it is not close in either direction.

On the 2026 anyformat parsing benchmark, which scored PDF-to-Markdown conversion across 1,000+ real documents and 30+ document types on text, tables and structure, AWS Textract reached a 65.4% parse score against Google Document AI's 55.0% (anyformat benchmark, 2026). Both services return bounding-box coordinates for every element, and both attach confidence values that the same benchmark classified as uncalibrated, meaning the reported confidence does not reliably track real accuracy.

Language support flips the result. Amazon Textract detects printed text in six languages (English, Spanish, French, German, Italian and Portuguese) and reads handwriting in English only; it does not support vertically aligned text, which rules out standard Japanese and Chinese layouts (AWS documentation, 2026). Google's Enterprise Document OCR supports 200+ languages for printed text and around 50 for handwriting (Google Cloud documentation, 2026). For any corpus beyond Western European languages, Textract is not an option.

Pre-built processors

Document AI has the broader catalog; Textract has the deeper US-lending stack.

Google ships pretrained parsers for invoices and expenses ($0.10 per document of up to 10 pages, roughly $10 per 1,000 pages), bank statements ($0.75 per document), pay slips and W-2s ($0.30 per document), plus US driver's licenses and passports ($0.10 per document) and a limited-access utility parser (Google Cloud pricing, 2026). Note the billing unit: pretrained processors charge per 10-page increment, so a one-page receipt costs the same as a ten-page one.

Textract's specialized APIs are fewer but priced per page: AnalyzeExpense for invoices and receipts at $10 per 1,000 pages, AnalyzeID at $25 per 1,000 pages, signature detection at $3.50 per 1,000 pages, and AnalyzeLending, a mortgage-package classifier-plus-extractor at $70 per 1,000 pages (AWS pricing, US West, 2026). One shared limitation: both vendors' identity processors only cover US documents. Neither reads a European ID card or passport out of the box (AWS and Google Cloud documentation, 2026).

Custom field extraction

Document AI wins this dimension since its foundation-model Custom Extractor went zero-shot.

Google's Custom Extractor now runs on a pretrained foundation model that extracts user-defined fields from a schema alone, with no labeled documents; few-shot prompting accepts up to 5 labeled examples, and fine-tuning, the path to production accuracy on harder document types, requires at least 50 training and 50 test documents (Google Cloud documentation, 2026). It costs $30 per 1,000 pages, and each deployed custom processor version adds hosting at $0.05 per hour, about $438 per year (Google Cloud pricing, 2026).

Textract's equivalent is split in two. Queries answers natural-language questions without training at $15 per 1,000 pages, but only in English and capped at 15 queries per page for synchronous calls and 30 for asynchronous ones (AWS documentation, 2026). Custom Queries improves accuracy through adapters at $25 per 1,000 pages, which require annotated training documents and are capped at 10 adapters per account (AWS documentation, 2026). For non-English documents or evolving schemas, Google's path is materially shorter.

Table extraction

Textract wins, and tables are the main reason teams pick it.

Table extraction is a dedicated, first-class feature in Textract's AnalyzeDocument API at $15 per 1,000 pages, with explicit support for merged cells and table titles in the response model. The 10-point parse-score gap over Document AI in the 2026 anyformat benchmark, on a metric that averages text, table and structure quality, is consistent with Textract's long-standing reputation in independent comparisons of cloud OCR services on line-item detection. Document AI extracts tables through Form Parser at $30 per 1,000 pages, twice Textract's price for a weaker measured result.

Neither is clean on hard cases: tables spanning many pages, nested structures and layouts that change per supplier degrade both, which is precisely where the benchmark documents the field dropping off.

Pricing

Neither wins outright; the cheaper vendor depends entirely on which feature you call.

Plain OCR is identical at the entry point: $1.50 per 1,000 pages on both (AWS pricing and Google Cloud pricing, 2026). The volume discounts differ: Textract drops to $0.60 after 1 million pages per month, while Google's Enterprise Document OCR requires 5 million pages per month to reach the same $0.60. Between those volumes, Textract is the cheaper OCR.

For structured extraction the picture inverts by feature. Tables: Textract $15 vs Google's $30 Form Parser. Forms (key-value pairs): Textract $50 vs Google's $30. Forms and tables together are $65 per 1,000 pages on Textract, and stacking forms, tables and queries costs $70, more than double Google's Custom Extractor at $30, though the Google route adds hosting fees for deployed custom versions. Invoices land near parity at roughly $10 per 1,000 pages on both, with Google's per-document billing penalizing short documents.

Page and size limits

Textract wins for long documents, by two orders of magnitude.

Asynchronous Textract jobs accept PDFs and TIFFs up to 500 MB and 3,000 pages (AWS documentation, 2026). Document AI's online processing caps most processors at 15 pages, extendable to 30 with imageless mode, and 40 MB per request; batch processing accepts files up to 1 GB and 5,000 files per request, but page caps still vary by processor, from 15 pages up to 1,000 (Google Cloud documentation, 2026).

Textract has its own catch: synchronous calls take a single page for PDF and TIFF and 10 MB per file, so any real workload runs through the asynchronous API with S3, SNS and polling logic around it. On both platforms, production means building the async plumbing.

Deployment

This one is a tie, and a short section: both are cloud-only.

Neither Textract nor Document AI can be deployed on-premise, in a customer's VPC as a self-contained container, or in an air-gapped environment. Organizations whose data cannot leave their perimeter, common in defense, healthcare and parts of financial services, cannot run either service at all.

EU data residency

A draw, and not a flattering one: both offer EU processing locations, neither offers European jurisdiction.

Textract runs in five EU regions: Ireland, London, Paris, Frankfurt and Spain (AWS endpoints documentation, 2026), with lower throughput quotas than US regions (5 transactions per second for AnalyzeDocument in Ireland versus 10 in N. Virginia). Document AI offers an eu multi-region for processing and storage plus London and Frankfurt single regions with a reduced processor lineup (Google Cloud documentation, 2026). AWS gives finer country-level control; Google's eu location is simpler to adopt.

The structural point stands for both: region selection changes where data is processed, not which legal framework governs it. Amazon Web Services, Inc. and Google LLC are US companies subject to US law, including the CLOUD Act, regardless of the region a customer selects. For teams where sovereignty is a board-level requirement rather than a checkbox, neither service resolves it.

Ecosystem

Whichever cloud you already run on wins this dimension, and it usually decides the whole evaluation.

Textract is wired into S3 for input and output, Lambda and Step Functions for orchestration, SNS and SQS for job notifications, and Amazon A2I for human review loops. Document AI connects to Cloud Storage, streams results into BigQuery for analytics, and feeds Vertex AI pipelines, with the Workbench UI for labeling and evaluating custom processors. Using either service from the other cloud adds egress costs, cross-cloud IAM and latency; in practice, almost nobody does it.

The verdict

AWS-native stack, structured documents. If you run on AWS and your workload is forms, tables, expenses or US lending packages, Textract is the stronger and better-measured extractor, and the async API handles documents up to 3,000 pages.

GCP-native stack or multilingual corpus. If you run on GCP, feed BigQuery, or process documents beyond six Western European languages, Document AI is the only viable pick of the two; Textract simply does not read those languages.

Custom schemas, minimal setup. If you need non-standard fields extracted quickly, Document AI's zero-shot Custom Extractor gets from schema to structured output without a training cycle, as long as your documents fit the 15-to-30-page online limits.

Long documents and archives. For contracts, mortgage files or archive digitization measured in hundreds of pages per document, Textract's 3,000-page asynchronous limit makes it the default choice.

When neither is the right fit

Both services share the same structural gaps. There is no workflow layer: classification, routing, validation and human review are assembled from Lambda and Step Functions on AWS or Cloud Functions and Workflows on GCP. Measurement is thin, too. Document AI's Workbench reports evaluation metrics for custom processors and Textract has no equivalent, but neither puts a labelled benchmark next to live production accuracy, so noticing that a supplier's new invoice layout quietly degraded extraction is your team's problem to solve. That surrounding pipeline, post-processing, schema validation, retry and routing logic, a review interface, monitoring and periodic re-tuning, is the cost that per-page comparisons leave out, and unlike the API bill it does not stop once it is built. Neither deploys on-premise. And both operate under US jurisdiction, whatever region is selected.

Teams that hit those walls, European data sovereignty, air-gapped deployment, built-in human review, or a way to prove accuracy did not regress, tend to outgrow both. anyformat, the European document intelligence platform that publishes the benchmark cited throughout this article, was built for those cases: schema-based zero-shot extraction, a visual workflow layer with human review, EU data residency and on-premise deployment. It also ships the measurement piece: every Extract and Classify workflow has a Health tab where you build a dataset with verified ground truth, slice it into sub-datasets by provider or document type, and run numbered, immutable evaluations that score any workflow version field by field, with the dataset benchmark displayed next to live production accuracy, confidence and through rate. A self-optimizing workflow, Optimizer, has been announced but is not yet available. Pricing is credit-based and charged per page per operator, with parsing at 25 credits per page, the $25 per 1,000 pages the benchmark quotes. On the same 2026 parsing benchmark it scored 78.1%, against Textract's 65.4% and Document AI's 55.0%. The direct comparisons are here: anyformat vs AWS Textract and anyformat vs Google Document AI.

Frequently asked questions

Pricing and limits verified in July 2026 against the AWS Textract pricing page, the Amazon Textract Developer Guide, and Google Cloud's Document AI pricing and limits documentation. Benchmark data: the anyformat document AI benchmark (2026), measured on 1,000+ real documents.