November 13, 2025
11 min readThis article provides a detailed, code-level comparison of these two APIs, covering setup, implementation, accuracy, and structured data quality.
By the end, you’ll have a clear understanding of which API works best for your specific needs, whether it’s quick integration or robust enterprise-grade features.
I. Introduction: The Developer’s Dilemma in a Cloud-First World
In today’s cloud-first world, developers no longer need to build complex machine learning models from scratch. Cloud APIs provide instant access to state-of-the-art, pre-trained models.
This approach eliminates the hassle of managing infrastructure or spending months on model training. It allows developers to integrate powerful AI capabilities into their applications quickly.
When it comes to intelligent document processing, two titans dominate the market. Google Cloud and Microsoft Azure are the leading providers of these essential services.
This article presents a head-to-head, code-level comparison of their flagship offerings. Our objective is to help developers choose the right API for their invoice data extraction projects.
II. Setup and Authentication: Getting Your Keys to the Kingdom
Before we can compare the APIs, we need to get access to them. Both platforms require a similar setup process involving creating an account, enabling the service, and obtaining credentials.
Google Cloud (Document AI)
First, you will need a Google Cloud Platform (GCP) project. If you don’t have one, create one in the GCP Console.
Inside your project, you must enable the Document AI API. Next, you need to create a service account, which will allow your Python script to authenticate securely.
When you create the service account, download the JSON key file to your computer. This file contains your private credentials and must be kept secure.
To make these credentials accessible to your code, set an environment variable named GOOGLE_APPLICATION_CREDENTIALS to the file path of your downloaded JSON key.
Finally, you need to install the official Python client library. Open your terminal and run the command pip install google-cloud-documentai.
Microsoft Azure (Document Intelligence)
The setup process for Azure is very similar. You will start by creating a free Azure account if you do not already have one.
Inside the Azure portal, you will create a new Document Intelligence resource. This action will provision the service for your use.
Once the resource is created, you will need to retrieve two key pieces of information from its “Keys and Endpoint” page. These are your unique Endpoint URL and your API Key.
You should set these as environment variables in your terminal for secure access. Name them DOCUMENTINTELLINTELLIGENCE_ENDPOINT and DOCUMENTINTELLIGENCE_API_KEY.
The last step is to install the Azure Python client library. Run the command pip install azure-ai-documentintelligence in your terminal.
III. Code Implementation: Parsing an Invoice in Python
With the setup complete, we can now dive into the code. We will write two separate Python scripts to parse the same invoice, one for each cloud provider.
Google Document AI Python Example
First, we import the necessary libraries and instantiate the DocumentProcessorServiceClient. This client object is our main entry point to the API.
You must then define the full processor name. This is a long string that includes your project ID, the location of the processor (e.g., ‘us’), and the specific processor ID for invoices.
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import os
from google.cloud import documentai
# 1. Instantiate the client
client = documentai.DocumentProcessorServiceClient()
# 2. Define the processor name
# You must create an invoice processor in the GCP console first
processor_name = client.processor_path(‘your-gcp-project-id’, ‘us’, ‘your-processor-id’)
# 3. Read the invoice file
with open(‘sample-invoice.pdf’, ‘rb’) as f:
pdf_content = f.read()
# 4. Call the API
request = documentai.ProcessRequest(
name=processor_name,
raw_document=documentai.RawDocument(content=pdf_content, mime_type=’application/pdf’)
)
result = client.process_document(request=request)
document = result.document
# 5. Extract and print the data
print(“— Google Document AI Results —“)
for entity in document.entities:
print(f”Field: {entity.type_}, Value: {entity.mention_text}, Confidence: {entity.confidence:.2f}”)
The response from Google is a list of entities. We iterate through this list to access the structured fields like invoice_id, total_amount, and their confidence scores.
Azure Document Intelligence Python Example
The Azure code follows a similar pattern but with its own specific client and response structure. We start by importing the libraries and creating the DocumentIntelligenceClient.
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import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.documentintelligence import DocumentIntelligenceClient
# 1. Instantiate the client
endpoint = os.environ[“DOCUMENTINTELLIGENCE_ENDPOINT”]
key = os.environ[“DOCUMENTINTELLIGENCE_API_KEY”]
client = DocumentIntelligenceClient(endpoint=endpoint, credential=AzureKeyCredential(key))
# 2. Call the API
with open(‘sample-invoice.pdf’, ‘rb’) as f:
poller = client.begin_analyze_document(“prebuilt-invoice”, analyze_request=f, content_type=”application/pdf”)
# 3. Get the result
result = poller.result()
# 4. Extract and print the data
print(“\n— Azure Document Intelligence Results —“)
if result.documents:
for doc in result.documents:
for field_name, field_value in doc.fields.items():
if field_value:
print(f”Field: {field_name}, Value: {field_value.content}, Confidence: {field_value.confidence:.2f}”)
The key difference is in the response object. Azure returns an AnalyzeResult that contains a collection of documents, and each document has a fields dictionary where you can access the extracted data by name.
IV. Head-to-Head Benchmark: Accuracy and Structured Data Quality
Now for the most important part: the comparison. We will test both APIs on a set of three different invoices to see how they perform under various conditions.
Our test set will include one clean, digitally-born PDF, one slightly skewed and faded scan of a paper invoice, and one low-quality photo of an invoice taken with a mobile phone.
Accuracy Comparison
For the messy documents, we first look at the quality of the general text extraction. Which API does a better job of performing basic OCR and turning the fuzzy characters into a readable text stream?
Next, we will perform a field-level accuracy check. We will compare the extracted values for key fields like the Invoice ID, Total Amount, and Vendor Name against the true values from the documents for all three invoices.
This will give us a clear picture of how robust each API is when faced with both ideal and challenging input.
Structured Data Comparison
For invoices, extracting line items is a critical and often difficult task. We will evaluate the completeness and accuracy of the table data extracted by each API.
We want to know which platform is better at handling multi-page tables or invoices with complex, non-standard layouts. This is often where one service will clearly outperform the other.
Both APIs also provide a confidence score for each extracted value. We will compare these scores to see if they are a reliable indicator of the actual accuracy of the field.
V. Decision Framework: API Feature & Performance Comparison
To summarize our findings, we can build a detailed comparison table. This provides a clear, at-a-glance reference for developers.
| Criteria | Google Document AI | Azure Document Intelligence |
| Ease of Setup | Relatively straightforward; relies on a JSON key file. | Simple; requires copying an Endpoint and API Key. |
| Python SDK Quality | Well-documented and easy to use. | Mature and robust, with clear documentation. |
| Accuracy (Clean Docs) | Excellent. | Excellent. |
| Accuracy (Messy Docs) | Often superior in general text recognition from poor images. | Very good, but can sometimes struggle more with low quality. |
| Line Item Extraction | Good, but can sometimes merge or miss complex lines. | Excellent and highly structured, a key strength of the platform. |
| Pricing Model | Per page, with different tiers for different processors. | Per page/transaction, with a generous free tier. |
| Additional Features | Excellent support for handwriting, custom models (Workbench). | Strong support for custom models, barcodes, and other document types. |
VI. Conclusion: Which Cloud OCR API is Right for You?
After this deep dive, we can draw some clear conclusions. Google’s offering often excels in its ease of use and its remarkable ability to perform general text detection from a wide variety of image qualities.
Azure, on the other hand, provides an extremely robust and enterprise-focused toolset. Its strength lies in its highly structured data output, especially for complex tables and line items.
So, which one should you choose? The final recommendation is nuanced and depends on your specific project needs.
For developers who need to get up and running quickly and require accurate data extraction from a wide range of document types with minimal setup, Google’s Document AI is a very strong contender. User comments and online forums often suggest that the underlying Google Vision API is superior for many challenging, real-world use cases.
For large enterprises that are already deeply integrated with the Microsoft ecosystem or that require highly detailed control over structured data models for complex workflows, Azure Document Intelligence is a powerful and compelling choice. Its focus on enterprise-grade features and structured output makes it ideal for these environments.
