In the rapidly evolving world of business automation, invoice processing remains one of the most impactful applications of AI and machine learning. Tools like Amazon Textract, Google Document AI, and Azure Document Intelligence aim to eliminate manual data entry, reduce errors, and accelerate operations — but how do they actually perform under real-world conditions?
As demand grows for automated invoice recognition, so do the questions businesses are asking:
To answer these questions, we conducted a detailed benchmark of five leading AI services:
Each system was tested on a diverse dataset of real invoices — spanning multiple layouts, formats, and time periods — and evaluated on key fields such as vendor names, invoice totals, and itemized line entries. We also measured speed and cost to provide a comprehensive view of how each tool performs in practical use.
Whether you're migrating away from AWS Textract, comparing Azure and Google options, or exploring the next generation of document AI, this analysis will give you the technical insights you need to make an informed decision.
We have carefully evaluated dozens of AI models and services for document processing to choose the most optimal selection for the purposes of processing invoices real-world projects:
Given these criteria, we have chosen five AI models able to recognise invoices. We’ve given each one a nickname for ease of understanding:
All five models are specialised in analysing invoices, have API integration capabilities and are very popular in the field of smart document analysis.
We have put together a dataset containing scanned digital invoices in the following formats: JPG, PNG, PDF (without a text layer). All scans are of high quality and contain minimal distortions and visual noise.
Each invoice contains tabular data, and the dataset itself contains at least 3 different types of layouts, which allows us to test the models across a variety of document designs.
Another important aspect is the year of the document: the dataset includes invoices issues from 1971 to 2020, allowing us to see how well modern AI services handle older document formats.
Approach: Pretrained invoice parser via AWS AnalyzeExpense API
Strengths: Fast, scalable, and reliable on basic fields like totals and vendor names
Weaknesses: Moderate field accuracy; struggles with complex layouts and table parsing
Best For: High-volume, low-complexity invoice workflows where speed is more critical than depth
Approach: Prebuilt invoice model within Google Cloud’s Document AI suite
Strengths: Easy GCP integration; quick setup through the Google Console
Weaknesses: Weakest overall performance in both field and line-item accuracy; especially limited on complex or older invoices
Best For: Basic GCP-native workflows involving simple invoice formats
Approach: Layout-aware, pretrained model with strong semantic parsing from Microsoft Azure
Strengths: Excellent at handling non-standard layouts, nested tables, and complex invoice structures; second-best overall field and table accuracy
Weaknesses: Slightly slower than AWS; occasional field gaps in edge cases
Best For: Semi-structured to complex invoices, particularly those with tables or older formats
Approach: Combines external OCR (e.g. Azure Read or Tesseract) with GPT-4o for extraction and reasoning
Strengths: Highest field-level accuracy (98%); excellent with layout variance and handwritten or non-standard formats
Weaknesses: Slower processing (~16s per page); requires additional OCR integration and setup
Best For: High-accuracy workflows involving complex documents and business-critical data extraction
Approach: Direct image input using GPT-4o’s multimodal capabilities (no separate OCR)
Strengths: Simple to deploy; low integration effort; strong field accuracy (90.5%)
Weaknesses: Weak on table/line-item extraction; slowest option (~33s per page)
Best For: Lightweight, internal automations, R&D tasks, and scenarios where table parsing is not essential
When evaluating AI services for invoice processing, two of the most widely known providers are Amazon Textract and Google Document AI (formerly based on Google Vision APIs). Both offer pretrained models for structured document extraction — but how do they compare in practice?
Our updated tests reveal a significant performance gap, especially in terms of accuracy and handling of itemized tables.
We tested each tool on a broad set of real-world invoices, both modern and historical. Amazon Textract, using its AnalyzeExpense API, delivered modest results on standard fields such as invoice totals, dates, and vendor names. It consistently extracted key-value pairs, but showed limitations with nested data and tables.
Google Document AI’s Invoice Parser, on the other hand, underperformed in nearly all areas. It frequently missed line items and showed inconsistency in extracting totals and labels — particularly on multi-column or non-standard layouts, where it struggled more than Textract.
However, benchmark data shows that both AWS and Google trail significantly behind newer and more capable tools:
While Google slightly outperformed Textract in field-level accuracy, its inability to reliably extract line items makes Textract the better option between the two.
Both services are cloud-native and fast, typically processing a page in under 4 seconds.
There’s no meaningful difference in cost-effectiveness, but Google’s lower performance makes Textract a better value overall.
Both services integrate well into their respective cloud platforms, so existing infrastructure and tooling can also influence the decision.
If you're choosing between AWS Textract and Google Document AI for invoice processing, Textract comes out ahead in our evaluation. While neither tool excels at complex document handling, Textract offers better overall reliability, especially for structured data and itemized tables.
That said, both lag behind newer entrants like Azure AI Document Intelligence and GPT-4o API, which showed significantly better performance in recent benchmarks.
When comparing invoice recognition tools, Microsoft’s Azure Document Intelligence and Amazon Textract are two of the strongest traditional cloud options. Both offer pretrained APIs tailored for structured documents and integrate well within their cloud ecosystems. But their performance diverges when handling complex layouts, tables, and extraction consistency.
Our benchmarking showed Azure slightly outperforming AWS in key areas — particularly when dealing with irregular or older invoices.
Azure and Textract both scored similarly in overall field recognition (~78–93% depending on document type), but Azure held a clear lead in parsing complex layouts and extracting line items:
Textract remains strong on templated fields, but Azure’s superior structure awareness makes it more versatile on real-world invoice formats — especially those created before 2000.
Both platforms are production-grade, cloud-native, and offer strong service-level agreements.
Azure’s more detailed output structure supports deeper integration for invoice workflows that require validation or enrichment logic.
If you're working with simple or template-based invoices, AWS Textract remains a reliable and efficient option. But if your invoice formats are variable or require accurate line-item recognition and layout parsing, Azure Document Intelligence is the stronger choice — particularly on historical or non-standard documents.
For teams looking for an alternative to AWS with better document structure awareness, Azure is currently the top performer among traditional cloud providers.
To evaluate the real-world capabilities of AWS Textract and its alternatives, we benchmarked five leading document recognition models across a diverse set of invoice samples. These invoices varied in format, layout complexity, and age — simulating realistic enterprise use cases.
Each tool was evaluated across three critical dimensions:
We assessed each tool's ability to extract structured fields like invoice totals, tax amounts, payment due dates, and vendor names. Accuracy was based on how well each model’s output matched human-verified ground truth values.
Model |
Field Accuracy (%) |
---|---|
GPT-4o + OCR (GPTt) |
98.0% |
Azure Document Intelligence |
93.0% |
GPT-4o (Image Input) |
90.5% |
Google Document AI |
82.0% |
AWS Textract |
78.0% |
Capturing individual products and services from invoice tables is critical but difficult. We measured extraction performance by evaluating table structure completeness, accuracy of line entries, and alignment with ground truth.
Model |
Line-Item Score (%) |
---|---|
Azure Document Intelligence |
87.0% |
AWS Textract |
82.0% |
GPT-4o (Image Input) |
63.0% |
GPT-4o + OCR (GPTt) |
57.0% |
Google Document AI |
40.0% |
To assess efficiency, we benchmarked each tool on a standardized invoice set to calculate average latency and projected API cost per 1,000 pages.
Model |
Processing duration per page, s |
Cost, per 1000 pages |
---|---|---|
AWS Textract |
2.9 ± 0.2 |
$101 |
Google Document AI |
3.8 ± 0.2 |
$10 |
Azure Document Intelligence |
4.3 ± 0.2 |
$10 |
GPT-4o (Image Input) |
16.9 ± 1.9 |
$8.80 |
GPT-4o + OCR (GPTt) |
33.0 ± 2.3 |
$8.80 |
1 — $0.008 per page after one million per month
While Amazon Textract offers a reliable foundation for invoice data extraction, our 2025 benchmark testing shows that several alternatives now outperform it — depending on your accuracy needs, document complexity, and system architecture.
Below are the top-performing alternatives based on our evaluation:
Best Overall Field Accuracy & Flexibility
If you're looking for the most accurate alternative to AWS Textract, GPT-4o paired with a third-party OCR layer (such as Azure Read or Tesseract) delivered the highest field-level accuracy in our tests. It handled complex layouts, inconsistent formats, and edge cases with exceptional precision. While it doesn't lead in table parsing, its raw field recognition was unmatched.
Ideal For: Complex documents, diverse invoice formats, and post-processing pipelines where field accuracy is paramount.
Best Cloud-Native Replacement for Textract
Azure’s pretrained Document Intelligence model clearly outperformed AWS Textract in our tests. It offered stronger accuracy on both standard fields and complex layouts — including multi-column tables and nested structures. Its structured output and robust layout interpretation make it well-suited for production systems.
Ideal For: Teams in the Microsoft ecosystem or those processing invoices with variable or complex layouts.
Best for Quick Prototyping and Lightweight Use Cases
GPT-4o with direct image input (no third-party OCR) is easy to use and more accurate than AWS Textract for field extraction — scoring 90.5% in our tests. While it falls short in table parsing and processing speed, it offers a low-barrier entry point for R&D or internal automation efforts.
Ideal For: Low-risk scenarios, prototypes, and internal tools where ease of use is prioritized over throughput.
Google's Invoice Parser model showed the weakest performance in nearly every category. It consistently missed key fields and failed to handle structured tables, especially in older or irregular invoices. While GCP integration is smooth, the model is not suitable for production invoice workflows without significant manual correction.
Only viable for: Very simple invoices and low-risk use cases within the GCP ecosystem.
When selecting an AWS Textract alternative, the right tool depends on your priorities:
Priority |
Recommended Tool |
---|---|
Maximum field accuracy |
GPT-4o + OCR (GPTt) |
Best table and layout handling |
Azure Document Intelligence |
Low setup complexity |
GPT-4o (Image Input) |
Cloud-native replacement |
Azure Document Intelligence |
Whether you're migrating from Textract or evaluating invoice extraction tools for the first time, these alternatives represent a meaningful step forward in performance and capability — each suited to different needs across speed, structure, and scalability.