Four platforms lead the document extraction market for engineering teams in 2026. Three of them run inside a single cloud, and the fourth runs across clouds with output built for AI agents to consume directly.
This page compares all four on the dimensions that decide the choice: how each one approaches the problem, what its output looks like, how it traces data back to the source, where it deploys, and how it prices.
Use it to match a platform to the stack you already run and the workload you actually process.
The short version
- AWS Textract fits teams already built on AWS who process general OCR, forms, invoices, and identity documents.
- Azure AI Document Intelligence fits Microsoft and Azure teams who need a broad catalogue of prebuilt document models.
- Google Document AI fits Google Cloud teams building multi-step pipelines with custom and specialized processors.
- LandingAI ADE fits teams whose output feeds an AI agent or a RAG system, whose documents carry complex layouts, or whose workflow demands source-level auditability across clouds.
What each platform is
Before the comparison, here is a plain definition of each service so every term later on is clear.
AWS Textract
AWS Textract is Amazon's managed document service. It runs optical character recognition and prebuilt analysis on documents and returns the result as structured JSON inside the AWS ecosystem.
Azure AI Document Intelligence
Azure AI Document Intelligence is Microsoft's document extraction service, known until recently as Form Recognizer. It offers a large catalogue of prebuilt models for common document types and lets teams train custom models of their own.
Google Document AI
Google Document AI is Google Cloud's document platform. It works through composable processors, where you chain steps such as OCR, form parsing, and specialized extraction into a pipeline.
LandingAI ADE
LandingAI ADE, short for Agentic Document Extraction, is an API-first platform that converts documents into structured, source-cited data for AI agents and RAG pipelines.
Its current generation, ADE Gen 2, runs on DPT-3 Pro, LandingAI's own document parsing model and the default model in the platform. DPT stands for Document Pre-Trained Transformer, the family of models LandingAI builds to parse document layout and content.
Quick comparison
The table below summarizes the four platforms across nine decision dimensions.
| Dimension | AWS Textract | Azure AI Document Intelligence | Google Document AI | LandingAI ADE |
|---|---|---|---|---|
| Core approach | Managed OCR plus prebuilt analysis APIs | Prebuilt and custom models (formerly Form Recognizer) | Composable processors chained into a pipeline | Agentic Parse and Extract APIs on DPT-3 Pro, LandingAI's own parsing model |
| Primary output | Block-based JSON | Structured JSON with key-value pairs and tables | Per-processor JSON entities | Nested JSON hierarchy of pages, blocks, and table cells, plus a Markdown representation |
| Source traceability | Bounding boxes per block; your code reconstructs relationships | Bounding regions with cell-level confidence scores | Bounding boxes, varying by processor | Grounding on every block: page, character range, and bounding box, plus line-level boxes for text |
| Extraction method | AnalyzeDocument, AnalyzeExpense, AnalyzeID, and custom queries | Large prebuilt catalogue plus trained custom models | Specialized and custom processors on Vertex AI | Schema-based Extract, where you define the fields you want |
| Deployment | AWS cloud regions | Azure cloud, with a container option for some models | Google Cloud | Hosted SaaS in US and EU, Snowflake Native App, and VPC or on-premises |
| Compliance posture | Covered under the AWS compliance program | Covered under the Azure compliance program | Covered under the Google Cloud compliance program | SOC 2 Type II, HIPAA with a signed BAA, GDPR, and a Zero Data Retention option |
| Ecosystem fit | Deep AWS native (S3, Lambda, IAM) | Microsoft and Azure stack | Google Cloud and Vertex AI | API-first Python and TypeScript, built for RAG and agent pipelines |
| Pricing model | Pay per page, priced per API feature | Per page, across prebuilt and custom tiers | Per page, processor-dependent, with hosting for custom models | Credit-based, where one US dollar buys 100 credits |
| Best-fit workload | AWS-native stacks, general OCR and forms | Microsoft shops, broad prebuilt coverage | Google Cloud teams, multi-step entity pipelines | Agent and RAG pipelines, complex layouts, audit-grade traceability |
A note on the table: the competitor rows describe each vendor's documented approach, and the LandingAI figures come from LandingAI's own documentation. Confirm current pricing on each vendor's site, since rates change often.
How each platform approaches document extraction
AWS Textract
AWS Textract treats document extraction as a managed OCR service inside AWS. You send it a document, and it returns a block-based JSON structure of lines, words, key-value pairs, and tables.
Your application then parses that structure and reassembles it into the semantic shape your system needs. The prebuilt AnalyzeExpense and AnalyzeID paths handle common families such as invoices, receipts, and identity documents.
The whole service wires naturally into S3, Lambda, and IAM. For a team whose infrastructure already lives on AWS and whose documents follow predictable patterns, that gravity is the main reason to choose it.
Azure AI Document Intelligence
Azure AI Document Intelligence leads with the breadth of its prebuilt model catalogue. It covers a wide range of standard business documents out of the box.
When the prebuilt models fall short, you can train custom models of your own. On table extraction, it returns cell-level confidence scores that give reviewers a triage signal.
Teams already standardized on Microsoft and Azure tend to land here, because identity, storage, and billing integration is already solved.
Google Document AI
Google Document AI takes a composable-processor approach, where you chain stages into a pipeline. You connect OCR, form parsing, and specialized extractors in the sequence your workflow needs.
That architecture rewards teams who want fine control over each stage and who already run on Google Cloud with Vertex AI alongside.
The flexibility is real, and so is the engineering investment required to design, connect, and maintain the processor chain.
LandingAI ADE
LandingAI ADE approaches the same problem from the downstream end, starting from what an AI agent needs rather than from raw OCR. It runs on DPT-3 Pro and returns a nested hierarchy of pages, blocks on each page, and cells within tables.
Every block carries a semantic id and its own grounding back to an exact page, a Markdown character range, and a bounding box. The output is a Markdown representation plus that hierarchical JSON, which drops straight into a RAG index or an agent workflow.
That design removes the reconstruction step the three hyperscaler services leave to your engineers. LandingAI reports 99.16% accuracy on the DocVQA benchmark, a public dataset for document question answering; treat that as a vendor-reported figure.
Also read: How LandingAI ADE makes every extraction defensible through visual grounding
The dimensions that decide your choice
Output readiness
Output readiness is the clearest dividing line between the two camps. The three hyperscaler services return OCR-grade structures that your engineers reorganize into application-ready objects.
That means the real cost of each one includes the reconstruction, matching, and validation code your team writes and maintains around it.
ADE returns a hierarchy that an agent can traverse on arrival, so the integration surface shrinks to reading a tree rather than rebuilding one.
Source traceability and audit
Traceability is where the architectures diverge the most. Azure exposes confidence scores that report how sure the model is about a value, which works as a triage signal for routing uncertain fields to a human reviewer.
ADE grounds every block to its source, so each extracted value maps to an exact character span and an exact box on an exact page. A reviewer opens the value and verifies it against the original document directly.
For regulated workflows in finance, healthcare, and legal, a verifiable source location turns an extracted value into an audit trail that a reviewer can defend.
Also read: Confidence scores vs visual grounding, and what each architecture actually tells you
Deployment and data control
Deployment flexibility matters most once compliance enters the room. The three cloud services run inside their own clouds, which suits teams already committed to that provider.
ADE runs as hosted SaaS in both US and EU regions, as a Snowflake Native App, and as a containerized deployment inside your own VPC or on-premises environment on the Enterprise plan.
With its Zero Data Retention option enabled, LandingAI retains your data only long enough to process the request, and your documents and all intermediate processing data are deleted when the operation finishes. Teams in regulated sectors often require this. ZDR is available on the Team and Enterprise plans.
Also read: LandingAI ADE security and compliance: SOC 2 Type II, GDPR, HIPAA, ZDR, and BAA
Pricing model
Pricing splits along the same two-camp line. The three hyperscalers bill per page, with the rate set by the specific API or processor you call, and Google adds hosting cost for deployed custom models.
ADE uses a credit model where one US dollar buys 100 credits. Its consumption scales with document complexity rather than a flat per-page rate, so a dense page of tables costs more than a sparse one.
Enterprise terms vary across all four platforms, so confirm the current numbers against each vendor's pricing page before you model your spend.
When to use each platform
Choose AWS Textract if
- Your stack already runs on AWS.
- Your documents fall into well-understood families such as invoices, receipts, and IDs.
- General OCR plus the prebuilt expense and identity paths cover your job.
Choose Azure AI Document Intelligence if
- You run on Microsoft and Azure.
- The prebuilt model catalogue maps closely to the documents you process.
- You want cell-level confidence signals on table extraction.
Choose Google Document AI if
- You are a Google Cloud team.
- You build multi-step pipelines that need entity extraction and classification.
- You can invest the engineering to design and maintain the processor chain.
Choose LandingAI ADE if
- Your documents carry complex, multi-column, or mixed-media layouts.
- Your output feeds an AI agent or a RAG system directly.
- Your workflow demands source-level auditability across clouds rather than inside one.
- You want to avoid owning the reconstruction and review engineering that the hyperscaler APIs push downstream.
How to run your own test
The most reliable way to decide is to run the same representative set of your own documents through each platform.
Measure three things on the layouts you actually process: extraction accuracy, how ready the output is for your downstream system, and how much review effort each result demands.
Performance on your documents tells you more than any architectural summary.
Also read: Best document parsing APIs in 2026
Frequently asked questions
Which platform is best for complex tables and multi-column layouts?
Complex layouts favor platforms that preserve document structure rather than flatten it into text. ADE returns tables as HTML, which keeps merged cells and nested structure intact, and it grounds each element to its source location.
Textract and Azure both handle standard tables competently. Google Document AI depends on the specific processor you chain for the job.
Can I use the output directly in a RAG or LLM pipeline?
ADE returns structured Markdown and hierarchical JSON designed for direct ingestion into vector databases and agent workflows. The three hyperscaler services return OCR-grade JSON that your application reorganizes into an LLM-ready shape first.
Which platforms support HIPAA and on-premises deployment?
All four address compliance, though the shape differs. The hyperscaler services inherit their cloud provider's compliance program and run inside that cloud.
ADE offers SOC 2 Type II, a signed BAA for HIPAA on Team and Enterprise plans with Zero Data Retention enabled, GDPR coverage, and a containerized deployment inside your own VPC or on-premises environment.
How does pricing compare across the four?
The hyperscalers bill per page, with the rate varying by the specific API or processor you call, and Google adds hosting cost for deployed custom models.
ADE uses a credit model where one US dollar buys 100 credits, and consumption scales with document complexity. Exact enterprise terms vary across all four, so confirm against each vendor's current pricing.
Do I need to train a custom model to get started?
ADE Extract works from a JSON schema you define, so you describe the fields you want and run, without training a model for each document type.
Textract offers prebuilt paths plus custom queries. Azure and Google both support custom model training when their prebuilt options fall short, which adds labelled data and training time to your setup.
Is there a one-to-one comparison for each platform?
For two of them, yes: LandingAI ADE vs AWS Textract and LandingAI ADE vs Azure Document Intelligence. For Google, read LandingAI ADE vs Gemini document processing, which compares ADE with Google's Gemini models rather than Document AI.