The Drive AI vs AWS Textract vs Google Document AI — Document Extraction Compared (2026)

Document extraction APIs turn unstructured files into structured data. Invoices become line items. Contracts become key-value pairs. Resumes become candidate profiles. The question is which API does it best for your use case.

This comparison covers four platforms: The Drive AI, AWS Textract, Google Document AI, and Adobe PDF Extract API. We tested each on the same extraction tasks and compared setup complexity, output quality, pricing, and developer experience.

Why Document Extraction APIs Matter in 2026

Manual data entry from documents costs businesses an estimated $12.9 billion annually. Even companies that digitized their paperwork years ago still struggle with the extraction step: pulling specific fields from PDFs, images, and scanned documents into databases, CRMs, and spreadsheets.

The latest generation of extraction APIs goes beyond basic OCR. They understand document structure, identify key-value pairs, and return confidence scores. Some, like The Drive AI, let you define exactly what fields you want and return structured JSON matching your schema.

The right choice depends on what you extract, how many file types you handle, and how much infrastructure you want to manage.

Platform Overviews

The Drive AI Extract API

The Drive AI takes a schema-first approach. You define the fields you want extracted — with types, descriptions, and required flags — and the API returns structured JSON matching your schema. Every field includes a confidence score (high, medium, or low) and citations pointing back to the source text.

Key characteristics:

• Schema-based extraction with typed fields (string, number, boolean, array, enum)
• 107+ supported file types including PDF, DOCX, XLSX, images, and websites
• Website extraction that renders JavaScript, parses the DOM, and follows links
• OCR with vision model proofreading for scanned documents
• Confidence scores and source citations on every extracted field
• Simple API key authentication (X-API-Key header)
• SDKs for Node.js (@thedriveai/sdk) and Python (thedriveai)

AWS Textract

Amazon Textract is part of the AWS ecosystem. It provides OCR, form extraction (key-value pairs), and table extraction as separate API operations. Textract works well for structured forms where fields are visually labeled on the page.

Key characteristics:

• Three operations: DetectDocumentText, AnalyzeDocument (forms/tables), and AnalyzeExpense (invoices/receipts)
• Supports PDF and image formats (JPEG, PNG, TIFF)
• Asynchronous processing for multi-page documents via S3
• IAM-based authentication with AWS credentials
• Strong integration with other AWS services (S3, Lambda, Step Functions)
• Mature enterprise support and compliance certifications

Google Document AI

Google Document AI offers a processor-based model. You create processors — either general-purpose (form parser, OCR) or specialized (invoice parser, receipt parser, W-2 parser) — and send documents through them. Custom processors can be trained on your own document types.

Key characteristics:

• Pre-trained processors for invoices, receipts, identity documents, bank statements, and more
• Custom Document Extractor for training on your own document types
• Form Parser for generic key-value pair and table extraction
• GCP project and service account required
• Human-in-the-loop review workflows
• Enterprise-grade compliance and data residency controls

Adobe PDF Extract API

Adobe focuses exclusively on PDFs. The Extract API returns structured JSON with text, tables, and positional data. It preserves reading order and document structure well, making it a strong choice for PDF-only workflows.

Key characteristics:

• PDF-only (no image or other format support)
• Structured JSON output preserving document hierarchy
• Table extraction with cell-level positioning
• 500 free document transactions per month
• SDKs for Node.js, Python, .NET, and Java
• Part of the broader Adobe Document Services platform

Detailed Comparison

Code Examples: Extracting Invoice Data

To make this comparison concrete, here is the same task — extracting vendor name, invoice number, date, line items, and total from an invoice PDF — implemented with each API.

The Drive AI

import TheDriveAI from "@thedriveai/sdk";

const client = new TheDriveAI({ apiKey: "tda_live_..." });

const result = await client.extract({
  file: "https://example.com/invoice.pdf",
  schema: [
    {
      name: "vendor_name",
      type: "string",
      description: "Company or person who issued the invoice",
      required: true,
    },
    {
      name: "invoice_number",
      type: "string",
      description: "The invoice or reference number",
      required: true,
    },
    {
      name: "invoice_date",
      type: "string",
      description: "Date the invoice was issued (YYYY-MM-DD)",
      required: true,
    },
    {
      name: "line_items",
      type: "array",
      description: "Each line item with description, quantity, unit_price, and amount",
      required: true,
    },
    {
      name: "total_amount",
      type: "number",
      description: "Total amount due on the invoice",
      required: true,
    },
  ],
});

console.log(result.data);
// {
//   vendor_name: "Acme Corp",
//   invoice_number: "INV-2026-0042",
//   invoice_date: "2026-05-15",
//   line_items: [
//     { description: "Consulting hours", quantity: 40, unit_price: 150, amount: 6000 },
//     { description: "Travel expenses", quantity: 1, unit_price: 450, amount: 450 }
//   ],
//   total_amount: 6450
// }

console.log(result.confidence);
// { vendor_name: "high", invoice_number: "high", ... }

console.log(result.citations);
// { vendor_name: "Acme Corp, 123 Main St...", ... }

Setup time: under 5 minutes. Get an API key from dev.thedrive.ai, install the SDK, and call the endpoint.

AWS Textract

import boto3

textract = boto3.client(
    "textract",
    region_name="us-east-1",
    aws_access_key_id="AKIA...",
    aws_secret_access_key="...",
)

# Upload file to S3 first
s3 = boto3.client("s3")
s3.upload_file("invoice.pdf", "my-bucket", "invoice.pdf")

# Start async analysis
response = textract.start_expense_analysis(
    DocumentLocation={"S3Object": {"Bucket": "my-bucket", "Name": "invoice.pdf"}}
)
job_id = response["JobId"]

# Poll for completion
import time
while True:
    result = textract.get_expense_analysis(JobId=job_id)
    if result["JobStatus"] == "SUCCEEDED":
        break
    time.sleep(2)

# Parse results — Textract returns all detected fields
for doc in result["ExpenseDocuments"]:
    for field in doc["SummaryFields"]:
        label = field.get("LabelDetection", {}).get("Text", "")
        value = field.get("ValueDetection", {}).get("Text", "")
        confidence = field.get("ValueDetection", {}).get("Confidence", 0)
        print(f"{label}: {value} (confidence: {confidence:.1f}%)")

    for table in doc.get("LineItemGroups", []):
        for item in table["LineItems"]:
            for expense_field in item["LineItemExpenseFields"]:
                print(expense_field["Type"]["Text"], expense_field["ValueDetection"]["Text"])

Setup time: 30-60 minutes. Create an AWS account, configure IAM credentials, create an S3 bucket, set bucket policies, and install boto3. For multi-page PDFs, you must use the asynchronous API and poll for results.

Note that Textract returns all detected fields rather than fields you specify. You write post-processing code to map Textract's output labels to your data model.

Google Document AI

from google.cloud import documentai_v1 as documentai

project_id = "my-gcp-project"
location = "us"
processor_id = "abc123def456"  # Invoice parser processor ID

client = documentai.DocumentProcessorServiceClient()
resource_name = client.processor_path(project_id, location, processor_id)

with open("invoice.pdf", "rb") as f:
    raw_document = documentai.RawDocument(
        content=f.read(),
        mime_type="application/pdf",
    )

request = documentai.ProcessRequest(
    name=resource_name,
    raw_document=raw_document,
)

result = client.process_document(request=request)
document = result.document

# Extract entities from the invoice parser
for entity in document.entities:
    print(f"{entity.type_}: {entity.mention_text} "
          f"(confidence: {entity.confidence:.2f})")
    for prop in entity.properties:
        print(f"  {prop.type_}: {prop.mention_text}")

Setup time: 45-90 minutes. Create a GCP project, enable the Document AI API, create a service account, download credentials, create an invoice parser processor in the console, and install the google-cloud-documentai package. If the pre-trained invoice parser does not cover your fields, you train a custom processor which takes additional time.

Adobe PDF Extract API

const { ServicePrincipalCredentials, PDFServicesInstance,
        ExtractPDFParams, ExtractPDFOperation,
        ExtractElementType } = require("@adobe/pdfservices-node-sdk");

const credentials = new ServicePrincipalCredentials({
  clientId: "...",
  clientSecret: "...",
});

const pdfServices = new PDFServicesInstance({ credentials });

const input = await pdfServices.upload({
  readStream: fs.createReadStream("invoice.pdf"),
  mimeType: "application/pdf",
});

const params = new ExtractPDFParams({
  elementsToExtract: [ExtractElementType.TEXT, ExtractElementType.TABLES],
});

const job = new ExtractPDFOperation({ input, params });
const result = await pdfServices.submit(job);
const content = await result.getContent();

// Returns structured JSON with text blocks and tables
// You map these to your data model manually

Setup time: 20-30 minutes. Create Adobe Developer credentials, configure OAuth, and install the SDK. Adobe returns structured text and table elements, but mapping them to specific invoice fields requires custom code.

When to Use Which

Choose The Drive AI when:

• You need specific fields, not everything. Schema-based extraction means you define exactly what you want and get structured JSON back. No post-processing to filter or map generic OCR output.
• You process diverse file types. With 107+ supported formats including DOCX, XLSX, and websites, you avoid maintaining separate pipelines for different inputs.
• You extract data from websites. The Drive AI is the only option here that renders JavaScript, parses the DOM, and returns structured data from web pages.
• You want fast integration. API key authentication, two SDKs, and a single endpoint. No cloud provider accounts, IAM roles, or processor provisioning.
• You need citations. Every extracted field links back to its source text, which is valuable for audit trails and verification.

Choose AWS Textract when:

• You are already on AWS. Textract integrates natively with S3, Lambda, and Step Functions. If your infrastructure runs on AWS, the integration is smoother despite the initial IAM setup.
• You process high volumes of standardized forms. Textract's form and table detection is mature and handles structured documents (tax forms, government applications, insurance claims) reliably.
• You need AnalyzeExpense or AnalyzeLending. These purpose-built APIs are optimized for invoices/receipts and lending documents respectively, with field-level normalization.
• Enterprise compliance is non-negotiable. AWS offers FedRAMP, HIPAA, SOC, and other compliance certifications that some regulated industries require.

Choose Google Document AI when:

• You need pre-trained processors for specific document types. Google offers 20+ processors for invoices, receipts, bank statements, pay stubs, W-2s, driver licenses, passports, and more. If your document type has a dedicated processor, accuracy will be high out of the box.
• You want to train custom extractors. Google's Custom Document Extractor lets you label documents and train a model for your specific layout. This is valuable for proprietary forms that no general-purpose API handles well.
• You need human-in-the-loop review. Google provides a built-in review interface for low-confidence extractions, which is useful in workflows that require human verification.
• Your infrastructure runs on GCP. Like Textract on AWS, Document AI integrates natively with Google Cloud Storage, BigQuery, and other GCP services.

Choose Adobe PDF Extract when:

• You only process PDFs. Adobe understands PDF structure deeply — reading order, headers, footers, tables, and figures. If your input is exclusively PDF and you need structural fidelity, Adobe is a strong choice.
• You need positional data. Adobe returns bounding box coordinates for every element, which is useful for document reconstruction or overlay applications.
• You want generous free usage. 500 free transactions per month covers many small-to-medium workloads without cost.

Pricing Summary

The Drive AI's pricing is straightforward: one credit per page, $0.01 per credit on Pro. No separate charges for different extraction types. AWS and Google charge differently depending on which features you use, and Google's per-page cost varies significantly by processor type.

The Bottom Line

AWS Textract and Google Document AI have larger ecosystems, deeper enterprise compliance, and years of production use at scale. If you are deeply embedded in AWS or GCP and process standardized document types, staying within your cloud provider's ecosystem is a reasonable choice.

The Drive AI stands out on developer experience and extraction flexibility. Schema-based extraction eliminates the post-processing step that Textract and Document AI require. Website extraction is unique to The Drive AI. The 107+ file format support means a single API replaces multiple tools. And the combination of confidence scores with source citations provides verification that most alternatives lack.

For teams building new extraction pipelines — especially those handling diverse file types or needing structured output matching a specific schema — The Drive AI reduces integration time from hours to minutes.

Have questions? Reach out at contact@thedrive.ai.