Google Document AI
by Google Cloud · Invoice & Document OCR
Cloud document-processing platform that extracts structured data from documents via API.
- Works with
- Any ERP (SAP, Oracle, NetSuite, Dynamics 365, etc.), BigQuery, Google Cloud Storage
- Deployment
- Cloud
- Company size
- Mid-market, Enterprise
- Pricing
- Usage-based (pay-as-you-go, priced per page or per document with volume discounts)
Overview
Google Document AI is a cloud-based document-processing platform that converts unstructured content in scanned files, PDFs, and images into structured data. It is delivered as an API on Google Cloud and is built on Vertex AI, allowing developers to create processors that digitize, classify, split, and extract data from documents without building machine-learning models from scratch. Rather than shipping a packaged finance application, it provides the OCR and data-extraction layer that teams embed into their own accounts payable, lending, or records-management workflows, then route the extracted fields into downstream systems such as an ERP.
The product is organized around "processors" that fall into three families: digitize (Enterprise Document OCR and image-quality checks), extract (Form Parser, Layout Parser, Custom Extractor, and pretrained parsers for invoices, expenses, pay slips, bank statements, and government IDs), and classify (custom classifiers and splitters that route multi-document files). Document AI Workbench provides a console for labeling sample documents, training or uptraining custom extractors, and evaluating processor accuracy before deployment. The Custom Extractor is powered by generative AI, so it can extract fields from free-form or complex-layout documents with little or no training data.
As an API-first product, Document AI is consumed through REST/gRPC endpoints and client libraries rather than a turnkey UI for end users. Output is a structured Document object containing text, layout, entities, and confidence scores that an integration layer maps to ERP records. Because it is part of Google Cloud, it inherits Google's billing, IAM, encryption, and compliance posture, and integrates natively with Cloud Storage for document input and BigQuery for analytics on extracted data. Buyers should plan for developer effort to connect Document AI to an ERP, since there is no prebuilt vendor connector for SAP, Oracle, NetSuite, or Dynamics.
Features & capabilities
OCR and digitization
Convert scanned files and images into machine-readable text and layout.
- Enterprise Document OCR with text and layout extraction
- Text recognition across 200+ languages
- Handwriting recognition
- Image-quality detection for readability scoring
- Automatic deskewing of skewed scans
- OCR add-ons for enhanced extraction
Structure and entity extraction
Pull key-value pairs, tables, and named entities from documents.
- Form Parser for key-value pairs, checkboxes, and tables
- Detection of generic entities such as dates and addresses
- Layout Parser that extracts text, tables, and lists and creates contextual chunks for AI applications
- Custom Extractor for fields specific to a document type
- Generative-AI extraction for free-form text and complex layouts
- Structured Document output with per-field confidence scores
Pretrained specialized parsers
Domain-specific extractors for common business documents.
- Invoice Parser
- Expense Parser
- Utility Parser
- Bank Statement Parser
- Pay Slip Parser
- W2 Form Parser
- US Driver License and US Passport parsers
- Identity Document Proofing with fraud-signal detection
Classification and splitting
Route and separate multi-document files before extraction.
- Custom Classifier to categorize documents by type
- Custom Splitter to separate multi-document files
- Procurement and lending document splitter/classifier processors
- Summarizer for abstractive and bullet-point summaries
Workbench and lifecycle tooling
Build, train, and evaluate processors before production.
- Document AI Workbench console for processor creation
- Auto-labeling and schema management for training datasets
- Uptraining of pretrained processors on customer samples
- Processor evaluation and accuracy metrics
- Document and prediction review for human-in-the-loop QA
- Online (synchronous) and batch (asynchronous) processing modes
Common use cases
- Automating accounts-payable invoice data capture for entry into an ERP
- Extracting line items and totals from expense receipts
- Digitizing archival scanned documents into searchable text
- Classifying and splitting mixed batches of incoming documents
- Parsing bank statements and pay slips for lending and finance workflows
- Identity verification and document proofing for onboarding
- Chunking documents to ground retrieval-augmented generation (RAG) applications
Strengths & considerations
Strengths
- OCR across 200+ languages, broader than many competing OCR engines
- Generative-AI Custom Extractor that works with little or no labeled training data
- Native integration with the wider Google Cloud stack (Cloud Storage, BigQuery, Vertex AI)
- Pay-as-you-go, per-page pricing with no seat licenses and volume discounts at scale
- Workbench tooling for training and evaluating custom processors without ML expertise
- Contractual commitment that customer documents are not used to train Google's models
Considerations
- API-first product with no turnkey UI for business end users; requires developer integration
- No prebuilt connectors for major ERPs (SAP, Oracle, NetSuite, Dynamics) integration is custom-built
- Reviewers cite pricing complexity and the difficulty of forecasting total cost
- Reviewers report documentation gaps and inconsistent extraction accuracy on some document types
- Online processing is capped (e.g., 15 pages online, 500 pages batch for OCR), requiring batch mode for large files
ERP integrations
Document AI returns a structured Document object via API; mapping extracted fields into ERP records requires a custom integration layer. No vendor-supplied ERP connector is offered.
Extracted data and metadata can be stored and analyzed in BigQuery.
Used as the input source and batch-output destination for documents.
Pricing
Published list pricing (US): Enterprise Document OCR $1.50 per 1,000 pages (1-5M/mo), $0.60 per 1,000 above 5M; OCR add-ons $6 per 1,000 pages. Custom Extractor and Form Parser $30 per 1,000 pages ($20 above 1M); Layout Parser $10 per 1,000 pages. Custom Classifier and Splitter $5 per 1,000 pages; Summarizer $25 per 1,000 pages. Pretrained Invoice/Expense/Utility parsers $0.10 per 10 pages; W2 and Pay Slip $0.30, Bank Statement $0.75, ID parsers $0.10 per document. Custom processor hosting $0.05 per deployed version-hour. Failed (4xx/5xx) requests are not billed. Get an independent shortlist with pricing guidance below.
Technical & security
- Hosting
- Google Cloud (SaaS API)
- Data residency
- US, EU, Asia-Pacific (selected regions)
- Compliance
- HIPAA, FedRAMP High, ISO 27001, ISO 27017, ISO 27018, SOC 2, SOC 3, PCI DSS
- Languages
- 200+ languages for OCR / text recognition
About the vendor
- Ownership
- Subsidiary of Alphabet Inc. (Google Cloud)
Alternatives to Google Document AI in Invoice & Document OCR
Google Document AI — frequently asked questions
Is Google Document AI a finished application or a developer API?
It is an API-first platform. You call processors over REST/gRPC and receive structured data back; embedding it into a business workflow or ERP requires development work, and there is no packaged end-user UI for operations like invoice approval.
How does Document AI connect to an ERP like SAP or NetSuite?
Document AI does not ship prebuilt ERP connectors. Extracted fields are returned via API and must be mapped into ERP records through a custom integration, an iPaaS, or Google Application Integration.
How is Document AI priced?
Pricing is usage-based, charged per page or per document depending on the processor, with volume discounts. For example, Enterprise Document OCR is $1.50 per 1,000 pages at low volume and pretrained parsers such as the Invoice Parser are $0.10 per 10 pages.
Does Google use my documents to train its models?
No. Google's documentation states it does not use customer content for any purpose other than providing the service and never uses customer data to train Document AI models.
What compliance certifications does Document AI hold?
Document AI is HIPAA compliant and FedRAMP High, and is covered by Google Cloud's ISO 27001/27017/27018, SOC 2, SOC 3, and PCI DSS audits. It supports CMEK and VPC Service Controls.
Evaluating Invoice & Document OCR?
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