AWS Textract vs Google Document AI: OCR Comparison
Published on March 2, 2026
Picking an OCR engine by reading the vendor’s marketing page will waste at least 3 weeks of engineering time rebuilding something that doesn’t work.
We have deployed document AI pipelines for clients processing anywhere from 40,000 to 2.3 million pages per month. The “right” answer between AWS Textract and Google Document AI is almost never what the blog roundups tell you.
What actually matters: accuracy on your document types, cost at your volume, and how many AWS services you are already paying for.
The Accuracy Gap Nobody Talks About
In an independent benchmark of 100 documents tested head-to-head, Google Document AI hit 95.8% average accuracy while AWS Textract landed at 94.2%. That 1.6-point gap sounds small until you’re processing 500,000 invoices a month — that’s roughly 8,000 misread pages that either need human review or quietly corrupt your downstream data.
But here’s what that number hides.
Low-Quality Scans Below 150 DPI
The gap widens to 4.9 percentage points: Document AI scores 81.2% versus Textract’s 76.3%. If your documents come from a mix of fax machines, mobile phone photos, and decade-old photocopiers — and most enterprise document pipelines do — Document AI wins that specific battle clearly.
Complex Tables With Merged Cells and Multi-Level Headers
Textract’s table extraction is genuinely industry-leading — cell-level relationship mapping and merged cell detection that Document AI simply doesn’t handle as cleanly. We ran a client’s purchase order pipeline through both tools — 12,000 POs with 6-column tables — and Textract got line-item detection at 82% accuracy while Document AI’s table parser collapsed to 40%.
That is not a minor difference. That is your ERP import failing twice every five records.
What You Are Actually Paying Per Page
Stop comparing the headline $1.50/1,000 pages rate and thinking these tools cost the same. They do not.
AWS Textract Pricing (US West Oregon)
DetectDocumentText
Basic text detection: $1.50 per 1,000 pages
Tables API
Table extraction: $15 per 1,000 pages
Forms (Key-Value Pairs)
Form extraction: $50 per 1,000 pages
Analyze Lending
Specialized financial docs: $70 per 1,000 pages (first 1M pages/month)
Google Document AI Pricing
Enterprise Document OCR
$1.50 per 1,000 pages (up to 5M/month), drops to $0.60 per 1,000 after that
Form Parser
$30 per 1,000 pages (up to 1M pages/month)
Layout Parser
$10 per 1,000 pages
Custom Extractor
$30 per 1,000 pages
The Dirty Math
A client processing 200,000 forms per month would pay $10,000/month on Textract Forms API versus $6,000/month on Google Form Parser. That $4,000 monthly difference is $48,000 per year — enough to hire a part-time ML engineer.
But if you’re already running your workload on AWS S3, Lambda, and SageMaker, adding Textract costs you $0 in egress and about 45 minutes to wire up. Moving the same documents to Google Cloud Document AI means data transfer costs plus GCP billing overhead on a second cloud account. We have seen companies “save” $2,000/month on OCR pricing while adding $3,700/month in cross-cloud data transfer fees. (Yes, the math works against you.)
Where Each Tool Actually Wins
This is the honest breakdown. Not “both are great for different use cases” — the actual scenarios where one beats the other by a margin that matters.
Pick AWS Textract If
Your data lives in S3. Period.
Your documents have structured tables — purchase orders, financial statements, multi-column layouts.
You need Queries. Textract’s built-in natural language Queries feature lets you ask questions directly about document content. Document AI has no equivalent.
You process US lending documents, W2s, or mortgage packets — Textract has a specialized Lending Analysis API built exactly for this.
You need human-in-the-loop review wired up with Amazon A2I without writing custom connectors.
Pick Google Document AI If
Your documents come from poor scan quality or non-standard DPI — Document AI’s OCR engine is measurably better at recovering text from degraded sources.
You process multi-language documents. Document AI supports 200+ languages with higher confidence than Textract.
You need out-of-the-box specialized parsers for bank statements, pay slips, identity documents, or procurement contracts — Google ships pre-trained processors for these, priced individually.
Your pipeline is already on GCP or you use BigQuery, Vertex AI, or Google Workflows.
You process more than 5 million pages per month. Document AI’s volume pricing drops to $0.60 per 1,000 pages for OCR versus Textract’s flat rate.
The AWS Ecosystem Lock-In Nobody Warns You About
The biggest underrated factor in this decision is ecosystem lock-in — and it cuts both ways.
AWS Textract’s deep integration with S3, Lambda, SNS, SQS, and Comprehend means you can build an end-to-end document processing pipeline with about 14 hours of engineering versus 35–40 hours when you mix AWS infrastructure with Google Document AI. We built an invoice processing system for a logistics client in the UAE — 100% on AWS — and connected Textract directly to Lambda triggers, pushed results to DynamoDB, and wired up Comprehend for entity extraction. Total build time: 11.5 hours. Had we routed documents to Document AI from the same AWS infrastructure, we would have added authentication layers, cross-cloud network calls, and a completely separate GCP billing setup.
The Controversial Take
If your stack is AWS-first, choosing Document AI for its 1.6% accuracy advantage is almost never worth it unless you are processing legally sensitive documents where that gap costs you real money in compliance failures.
Architecture fit compounds over time in ways that marginal accuracy gains do not.
The Handwriting Problem
Both tools handle handwriting — and both tools struggle with it. Textract scored 71.2% accuracy on documents with handwritten notes, while Document AI reached 74.8%. Neither of these numbers is good enough for any workflow where handwritten fields carry financial or legal weight without human review.
If Handwriting Accuracy Is Your Primary Constraint
Neither tool solves your problem. You need a custom OCR model fine-tuned on your specific handwriting corpus, or you need to build a hybrid pipeline that routes handwritten pages to a separate model.
That is a service we build at Braincuber — and it typically recovers accuracy to 91–94% on client-specific handwriting patterns after fine-tuning.
Real Implementation Cost: What the Pricing Page Ignores
AWS Textract free tier covers 1,000 pages/month for the first 3 months. Google Document AI similarly offers free tiers for new users. Neither of these numbers means anything if you’re building a production pipeline — you’ll blow through the free tier in 2 days of testing.
The Real Cost Is Engineering Time
AWS Textract Setup
2–4 hours for developers already familiar with AWS SDKs
Google Document AI Setup
2–4 hours for developers already familiar with GCP
Cross-Cloud Setup
AWS infrastructure to Document AI: 12–18 hours including authentication, network policy, and error handling
At a $95/hour blended engineering rate, that cross-cloud tax alone is $1,140–$1,710 in one-time setup cost. Not catastrophic — but why pay it when Textract’s accuracy difference is under 2 points for most clean-document workflows?
Side-by-Side: The Numbers That Matter
| Metric | AWS Textract | Google Document AI |
|---|---|---|
| Average OCR Accuracy | 94.2% | 95.8% |
| Clean Invoice Accuracy | 97.2% | 98.1% |
| Low-Quality Scan Accuracy | 76.3% | 81.2% |
| Handwritten Text Accuracy | 71.2% | 74.8% |
| Table/Line-Item Detection | 82% | 40% |
| Basic OCR Price (per 1,000 pages) | $1.50 | $1.50 |
| Forms/Key-Value Price (per 1,000 pages) | $50 | $30 |
| High-Volume OCR Discount | Flat rates by tier | $0.60/1,000 pages at 5M+ |
| AWS Ecosystem Integration | Native | Requires cross-cloud setup |
| Pre-trained Specialized Parsers | Lending, ID, Expense | Invoice, Bank Statement, Pay Slip, W2, Procurement, ID |
Our Actual Recommendation
Stop treating this as an “OCR tool” comparison. These are document intelligence platforms, and the decision should come down to three questions:
Where does your data live? If it’s in S3, use Textract. Period.
What is your document quality? Mixed-quality, multi-language, or non-standard formats tilt toward Document AI.
What is your volume? Under 5M pages/month on either platform, Textract is competitively priced. Above 5M pages/month processing OCR, Document AI’s $0.60 rate pulls ahead by roughly $4,500 per million pages.
Most AWS-native businesses we work with end up on Textract — not because it’s the best OCR engine in a vacuum, but because it’s the best OCR engine inside an AWS architecture. And architecture fit compounds over time in ways that marginal accuracy gains do not.
Stop Guessing Which OCR Tool Fits Your Pipeline
Braincuber builds production-grade document AI pipelines on AWS — Textract integrations, custom OCR fine-tuning, cross-cloud orchestration. 500+ projects across cloud and AI. We will identify exactly where your current extraction process is losing accuracy and costing you money before you even finish the call.
Frequently Asked Questions
Is AWS Textract better than Google Document AI for table extraction?
Yes, for most enterprise use cases. AWS Textract’s table parser includes cell-level relationship mapping, merged cell detection, and header identification. In independent testing, Textract achieved 82% line-item detection accuracy on structured tables versus Google Document AI’s 40% — a gap large enough to break most ERP import workflows.
Which OCR tool is cheaper: AWS Textract or Google Document AI?
At low to mid volumes, both charge $1.50 per 1,000 pages for basic OCR. Document AI’s Form Parser costs $30 per 1,000 pages versus Textract’s $50 per 1,000 pages for form extraction. At 5M+ pages per month, Document AI drops OCR to $0.60 per 1,000 pages — giving it a clear cost edge at high volume.
Can I use Google Document AI if my infrastructure is on AWS?
Yes, but it adds 12–18 hours of engineering for cross-cloud authentication, network policies, and error handling. At a $95/hour blended rate, that’s roughly $1,140–$1,710 in one-time setup cost. For pipelines where S3 and Lambda are already in play, native Textract integration eliminates that overhead entirely.
Which tool handles handwriting better?
Google Document AI scored 74.8% accuracy on handwritten content versus AWS Textract’s 71.2% in head-to-head testing. Both are insufficient for production workflows with legally or financially significant handwritten fields without a custom fine-tuned model or human-in-the-loop review layer added on top.
Does Google Document AI have a free tier?
Yes. Google Document AI offers 300 free pages per month for most processors on the free tier. AWS Textract provides 1,000 pages per month free for the first three months of new account activation. Neither free tier is sufficient for realistic production testing — plan for at least 5,000–10,000 pages in evaluation.