Google Document AI vs Apryse 2026
Google Document AI is a cloud API that turns documents into text, layout, and structured fields. Apryse is an embeddable document SDK whose Smart Data Extraction, OCR, and handwriting (ICR) modules do similar work inside your own infrastructure and whose viewing, editing, conversion, and compliance capabilities go well beyond it. This page compares the two where they overlap and is explicit about where they don't. This page helps you decide.
"The rendering performance, combined with how responsive the Apryse engineering team has been, has made a meaningful difference in our product quality."
Amanda Lansman, Sr Product Manager, Dropbox
Feature Chart: See How Apryse Compares
This chart covers only the capabilities both products offer. Capabilities unique to Apryse are listed in the next section.
OCR engine and languages
Default OCR Module (SDK 12.0 and later): deep-learning engine with models for English and multilingual models covering 80+ languages across Latin, Cyrillic, CJK, Devanagari, and Arabic scripts. Optional IRIS iDRS module (10 languages including Chinese, Japanese, Korean) for complex layouts; a leaner 6-language Alternative module for constrained hardware.
Enterprise Document OCR (stable v2.1): 200+ printed languages, deskew and rotation correction, native-PDF text extraction, image-quality scoring. Premium add-ons ($6 per 1,000 pages) for math/LaTeX, checkbox detection, and font-style detection. Broader language coverage than Apryse.
Handwriting (ICR)
Handwriting ICR Module (released April 2026): recognizes handwritten text in PDFs and images on-premises, outputs a searchable text layer and JSON with page numbers and word coordinates. English only.
Handwriting is recognized within the OCR processor for roughly 50 languages per Google's language table; the font-style add-on flags individual words as handwritten. Materially broader handwriting coverage than Apryse.
OCR output
Searchable PDF, or JSON/XML nested as pages, paragraphs, lines, and words with coordinates, font size, and orientation. No per-word confidence score in OCR output. External OCR JSON can be applied back onto a PDF.
A single Document JSON: full text plus blocks, paragraphs, lines, and tokens, each with text anchors, normalized bounding polygons, per-element confidence, detected languages, and reading order. Zero-valued coordinates are omitted from responses.
Layout and document structure
Document Structure engine returns paragraphs, headings (H1–H6), lists, tables of contents, tables, and images as JSON with a bounding rectangle and page number per element; distinguishes section columns from table columns. Structured Output additionally converts PDF to reflowed DOCX, XLSX, PPTX, and HTML.
Layout Parser returns a DocumentLayout tree (titles, headings, paragraphs, tables, lists, headers and footers, figures) plus context-aware chunks with ancestor headings for RAG. Accepts PDF, DOCX, PPTX, XLSX, and HTML. Stable v1.0 is non-generative; Gemini 2.5 and Gemini 3 versions are in preview. Bounding boxes on chunks are limited to the v1.0 model; tables spanning pages may split.
Table extraction
Tabular Data engine handles merged cells and multi-row headers, outputs table, row, and cell JSON with row and column spans, bounding rectangles, page numbers, and a group ID that links a table continued across pages, or exports directly to XLSX. Deep Learning Assist improves table recognition in the Document Structure engine. No per-cell confidence score.
Form Parser extracts simple tables as header and body rows; Layout Parser returns table structure, and since May 2026 Gemini versions can verbalize table and figure content. Document AI Toolbox (open-source Python) converts tables to Pandas, CSV, HTML, or Markdown.
Forms and key-value pairs
Form Field Detection finds text fields and checkboxes in scanned or static PDFs (radio buttons announced as coming) and can write fillable fields back into the PDF. Form Field Key-Value adds label-to-value matching. Generic Key-Value Extraction is template-free across document types, returning key, value, word-level rectangles, and a 0.0–1.0 confidence per pair. Also extracts CAD title-block metadata from engineering drawings.
Form Parser extracts key-value pairs, checkboxes, simple tables, and 11 generic entity types in 200+ languages. Not trainable. Google documents that it cannot reliably parse radio buttons, has lower accuracy on non-Latin scripts, and struggles with blank forms.
Custom or schema-defined extraction
Not available as self-serve training. No custom schemas, user-defined fields, or model fine-tuning in the SDK. The documented pattern is to pass Smart Data Extraction JSON to the LLM of your choice for schema mapping. A separate .NET Template Extraction product supports user-defined template zones.
Custom Extractor (GA on Gemini 2.5 Flash and Pro; Gemini 3 and 3.5 versions in preview): define a schema, then extract zero-shot, with 5–10 labelled examples, or by fine-tuning. Supports nested entities, derived entities, signature detection, and validation rules. English is the officially supported language. A clear Google advantage.
Pretrained document parsers
None by document type. Generic key-value extraction and classification work across invoices, receipts, IDs, forms, resumes, and other categories without a per-type model.
Invoice, Expense, Bank Statement, Pay Slip, W2, Identity Document Proofing, and US Driver License parsers. A large set of legacy parsers (US passport, 1099 and W9 tax forms, lending, procurement, utility) was discontinued June 30, 2026.
Classification and splitting
Page-level classification into 24 predefined categories: invoices, receipts, IDs, passports, forms, letters, legal documents, financial reports, technical drawings, and more; each with a 0.0–1.0 confidence and a configurable threshold. No user-defined labels. Splitting is done with the SDK's page-manipulation APIs rather than a model.
Custom Classifier and Custom Splitter (GA on Gemini 2.5 Flash) with user-defined labels, zero-shot or trained on about 10 documents per label, $5 per 1,000 pages. Splitter predicts page boundaries; the physical split is done by your code.
Coordinates, page references, confidence
Typed JSON for every engine. Every element carries a rectangle and a 1-based page number. Confidence scores on form fields, key-value pairs, and classification results; table and structure elements carry coordinates but no confidence.
Normalized bounding polygons and page references on tokens, blocks, and entities; confidence on tokens and extracted entities. Parity on coordinates and confidence.
Input formats
PDF, born-digital or scanned, including password-protected files. DOCX, images, and 30+ other formats are first converted to PDF by the SDK's built-in conversion. OCR and ICR modules accept images directly.
PDF, TIFF, GIF, JPEG, PNG, BMP, WebP for OCR and most processors; DOCX, PPTX, XLSX, and HTML for Layout Parser only. Google recommends 200–300 dpi scans. Custom Splitter accepts PDF, TIFF, and GIF only.
Page and size limits
No documented per-document cap in licensed use; long documents are processed whole. Trial keys are limited to 100 pages per extraction operation with watermark pages.
15 pages per synchronous request for OCR, Layout Parser, Form Parser, Custom Extractor, Classifier, and Splitter (30 with imageless mode, contiguous from page 1; 10 for Custom Extractor documents with checkboxes). 40 MB online; 1 GB and 5,000 files per batch with caps of 500 pages (OCR, Layout), 200 (Custom Extractor, Classifier), 100 (Form Parser), 1,000 (Splitter). Longer documents must be split, batched through Cloud Storage, and reassembled.
Deployment
In-process library on your servers: on-premises, private cloud, hybrid, or air-gapped. Docker, AWS Lambda, Azure Functions. Smart Data Extraction on Windows and Linux x64; OCR and ICR also on macOS.
Google Cloud regions only. Full capability in the US and EU multi-regions; Mumbai, Singapore, Sydney, London, Frankfurt, and Montreal carry a subset of processors. No on-premises or embeddable deployment of Document AI processors.
Pricing
Package license: Smart Data Extraction is an add-on to the Server SDK.
Enterprise Document OCR $1.50 per 1,000 pages (first 1,000 free; $0.60 above 5M). Layout Parser $10. Form Parser and Custom Extractor $30 ($20 above 1M). Classifier and Splitter $5 ($3 above 1M). OCR add-ons $6. Pretrained parsers $0.10 per 10 pages up to $0.75 per bank statement. Plus $0.05 per hour for each deployed custom processor version (about $438 per year) and reserved-capacity fees. No charge for failed requests.
Support and SLA
Maintenance and support Monday–Friday (Pacific) with a two-business-day response guarantee; enterprise tiers negotiated. No uptime SLA applies because the software runs in your environment.
99.9% monthly uptime SLA for online and batch processing on multi-region endpoints only; single-region deployments, preview versions, and the best-effort tier are outside the SLA.
Beyond the Overlap: The Rest of the Apryse SDK
Google Document AI is an extraction API. Apryse is a document SDK in which extraction is one module. The capabilities below are part of the same Apryse license and codebase, and are not part of Document AI at all. Google addresses some of them with other products (Gemini, Workspace, Cloud Vision), but not within Document AI.
Viewing and rendering
WebViewer: client-side WebAssembly rendering of 30+ formats (PDF, Office, CAD, images, email) with a source-available React UI; document content never leaves the browser. Native iOS and Android viewers.
Not part of Document AI.
Annotation and collaboration
35+ annotation types, real-time multi-user collaboration, measurement tools, document comparison, XFDF round-trip.
Not part of Document AI.
Forms, signatures, redaction
AcroForm and XFA forms, form creation and filling, electronic and digital signatures with timestamps and long-term validation, true redaction with pattern search, PDF sanitization.
Not part of Document AI. (Custom Extractor can detect the presence of a signature; it does not sign.)
Conversion
Office to PDF without Microsoft Office, PDF to DOCX/XLSX/PPTX/HTML (Structured Output), HTML to PDF, CAD to PDF (DWG, DXF, DGN, DWF; RVT in beta on Windows), email (EML, MSG) to PDF, DICOM and HEIC imaging.
Not part of Document AI. Layout Parser reads Office files for extraction but does not convert or render them.
Compliance formats
PDF/A conversion and validation for all parts and levels, PDF/UA with automated tagging, encryption and permissions, WCAG 2.2 AA viewer.
Not part of Document AI.
Generation, scanning, eSignature
Fluent template-based document generation, Scanbot SDK for mobile document and barcode scanning, Xodo Sign eSignature, and iText (an Apryse company) for Java and .NET PDF programming.
Not part of Document AI.
The practical consequence:
A team building a document product on Document AI still needs a viewer, an editor, a converter, and a review interface from somewhere else. A team building on Apryse gets extraction and the surrounding document stack from one vendor, one engine, and one license.
What Our Customers Say
"The speed at which they integrate is superior to their competition and their product roadmap has been good -- they’ve invested in the right things."
Marcus O'Brien
Global Head of Product Management, AutoCAD
"Document integrity is critical. Apryse absolutely solved the problem for us."
Uma Natarajan
CTO, Juume AI
"The rendering performance, combined with how responsive the Apryse engineering team has been, has made a meaningful difference in our product quality."
Amanda Lansman
Sr Product Manager, Dropbox
When to Choose Google Document AI
Your Documents Already Live in Google Cloud
If the rest of the pipeline is BigQuery, Cloud Storage, and Gemini Enterprise, Document AI is one IAM grant and one API call away. Layout Parser output flows directly into Google's RAG Engine and BigQuery document parsing. Data gravity is real, and fighting it rarely pays.
Schema-Defined Extraction with Few Examples
Custom Extractor lets you declare the fields you want and get them back zero-shot or after labelling 5–10 documents, with nested entities and validation rules. Apryse has no equivalent; you would pair its structured output with your own LLM to map to a schema.
Broad multilingual and handwriting coverage
200+ printed languages and roughly 50 handwritten languages in one processor. If your documents are handwritten in languages other than English, or printed in scripts outside the 80+ Apryse's OCR module covers, Document AI is the more capable recognizer.
Pretrained financial and identity parsers
Invoice, expense, bank statement, pay slip, W2, and identity-document parsers return typed fields without any setup. Apryse extracts these as generic key-value pairs and classifications rather than per-type schemas.
Variable or low volume
At $1.50 per 1,000 OCR pages with the first 1,000 free, a prototype or a low-volume workflow costs almost nothing to start. Pay-per-page is the right economics when volume is small or unpredictable and a license would sit idle.
When to Choose Apryse
Documents can't leave your environment
Regulated, sovereign, or disconnected environments where a cloud API is a non-starter. Smart Data Extraction, OCR, and ICR run in-process on your servers, including fully air-gapped, with no data sent to Apryse. Document AI has no on-premises or embeddable deployment.
You're shipping a product, not running a pipeline
When extraction is a feature inside software you sell, you need a component you can embed, version, and license with your product. Document AI is a service your product would call; Apryse is a library your product contains.
Predictable cost at high volume
A package license doesn't grow with page count. Document AI meters every page ($30 per 1,000 for Form Parser or Custom Extractor) plus hosting and reserved-capacity fees, so cost rises in step with volume. Model your annual volume and processor mix; above a threshold, a license is cheaper and easier to budget.
Reproducible, auditable output
When the same document must produce the same fields on every run such as for audit trails, regression testing, or downstream systems that can't tolerate drift, a deterministic parse matters. Document AI's Gemini-based processor versions are generative models.
Long documents without splitting
A 300-page contract, drawing set, or filing is processed whole. No 15-page synchronous cap, no batch orchestration through Cloud Storage, no reassembling page numbers afterward.
You need the rest of the document stack
Viewer, annotations, forms, redaction, signatures, editing, conversion, compliance, and a review UI that can overlay extracted fields on the source page, from the same SDK as the extraction. Document AI provides none of these.
Apryse is the top choice for document processing technology
Backed by a Team of Experts
FAQ
Document AI is a cloud API: you send pages to Google's processors and pay per page. Apryse is an SDK: Smart Data Extraction, OCR, and ICR run inside your own application and infrastructure under a package license, and the same SDK provides viewing, editing, conversion, and compliance capabilities that Document AI does not.
No. Document AI processors run only in Google Cloud regions. The closest Google offering for disconnected environments is basic OCR on Google Distributed Cloud, which does not include Layout Parser, Form Parser, or custom extractors. Apryse runs on-premises, in private cloud, or fully air-gapped.
No. Smart Data Extraction is template-free and works out of the box with generic key-value extraction, table extraction, form-field detection, and classification into 24 predefined categories. There is no self-serve training or custom schema definition; teams that need a specific schema pass the structured JSON to an LLM of their choice.
Document AI caps synchronous requests at 15 pages (30 with imageless mode) and batch requests at 100–1,000 pages depending on the processor; longer documents are split, sent through Cloud Storage, and reassembled. Apryse has no documented per-document cap in licensed use. Processing is local and bound by your hardware.
Yes. Document AI returns normalized bounding polygons and confidence on tokens and entities. Apryse returns a rectangle and page number on every extracted element, with confidence on form fields, key-value pairs, and classification results. The difference is that Apryse recovers structure deterministically, so the same document yields the same output on every run, while Document AI's Gemini-based processor versions are generative.
At low or variable volume, yes. OCR is $1.50 per 1,000 pages with the first 1,000 free, and there is no license to buy. At high volume the picture changes: Form Parser and Custom Extractor cost $30 per 1,000 pages, deployed custom processors cost $0.05 per hour each, and reserved capacity is $300 per page-per-minute per month. Apryse is a package license whose cost does not scale with page count. Model your annual page volume before deciding.
Neither offers a summarization API today: Google's preview-only Summarizer was discontinued June 30, 2026, and summarization is now directed to the Gemini API; Apryse's structured output is designed to be summarized by the model of your choice. Google's Human-in-the-Loop review service was retired in January 2025 with partners suggested as replacements; Apryse ships WebViewer in the same SDK, so a review interface can be built into the product.
Yes. That is the larger part of the SDK. Viewing and annotation of 30+ formats, forms, redaction, digital signatures, PDF and in-browser DOCX editing, conversion between PDF, Office, HTML, CAD, and email, PDF/A and PDF/UA compliance, document generation with Fluent, and mobile scanning with Scanbot SDK all ship under the same Apryse license. Document AI is limited to OCR, layout, extraction, classification, and splitting.
