Function App manual deployment - Overview

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Last updated: 2025-10-01

Note

Here are a few examples that demonstrate the deployment process and the creation of E2E solution. Please feel free to use them as references if needed.

Demo: Azure Implementation - PDF Layout Extraction with Azure AI Document Intelligence Supporting Multiple Document Versions with Visual Selection Cues (full-code approach)
Demo: Automated PDF Invoice Processing with Open Framework (full-code approach)
Demo: PDF Layout Extraction with Doc Intelligence (full-code approach)
Demo: Automated PDF Invoice Processing with Doc Intelligence (full-code approach)

Function App Hosting Options

In the context of Azure Function Apps, a hosting option refers to the plan you choose to run your function app. This choice affects how your function app is scaled, the resources available to each function app instance, and the support for advanced functionalities like virtual network connectivity and container support.

Tip

Scale to Zero: Indicates whether the service can automatically scale down to zero instances when idle.
- IDLE stands for:
  - I – Inactive
  - D – During
  - L – Low
  - E – Engagement
- In other words, when the application is not actively handling requests or events (it's in a low-activity or paused state).
Scale Behavior: Describes how the service scales (e.g., event-driven, dedicated, or containerized).
Virtual Networking: Whether the service supports integration with virtual networks for secure communication.
Dedicated Compute & Reserved Cold Start: Availability of always-on compute to avoid cold starts and ensure low latency.
Max Scale Out (Instances): Maximum number of instances the service can scale out to.
Example AI Use Cases: Real-world scenarios where each plan excels.

Flex Consumption

Feature	Description
Scale to Zero	`Yes`
Scale Behavior	`Fast event-driven`
Virtual Networking	`Optional`
Dedicated Compute & Reserved Cold Start	`Optional (Always Ready)`
Max Scale Out (Instances)	`1000`
Example AI Use Cases	`Real-time data processing` for AI models, `high-traffic AI-powered APIs`, `event-driven AI microservices`. Ideal for fraud detection, real-time recommendations, NLP, and computer vision services.

Consumption

Feature	Description
Scale to Zero	`Yes`
Scale Behavior	`Event-driven`
Virtual Networking	`Optional`
Dedicated Compute & Reserved Cold Start	`No`
Max Scale Out (Instances)	`200`
Example AI Use Cases	`Lightweight AI APIs`, `scheduled AI tasks`, `low-traffic AI event processing`. Great for sentiment analysis, simple image recognition, and batch ML tasks.

Functions Premium

Feature	Description
Scale to Zero	`No`
Scale Behavior	`Event-driven with premium options`
Virtual Networking	`Yes`
Dedicated Compute & Reserved Cold Start	`Yes`
Max Scale Out (Instances)	`100`
Example AI Use Cases	`Enterprise AI applications`, `low-latency AI APIs`, `VNet integration`. Ideal for secure, high-performance AI services like customer support and analytics.

App Service

Feature	Description
Scale to Zero	`No`
Scale Behavior	`Dedicated VMs`
Virtual Networking	`Yes`
Dedicated Compute & Reserved Cold Start	`Yes`
Max Scale Out (Instances)	`Varies`
Example AI Use Cases	`AI-powered web applications`, `dedicated resources`. Great for chatbots, personalized content, and intensive AI inference.

Container Apps Env.

Feature	Description
Scale to Zero	`No`
Scale Behavior	`Containerized microservices environment`
Virtual Networking	`Yes`
Dedicated Compute & Reserved Cold Start	`Yes`
Max Scale Out (Instances)	`Varies`
Example AI Use Cases	`AI microservices architecture`, `containerized AI workloads`, `complex AI workflows`. Ideal for orchestrating AI services like image processing, text analysis, and real-time analytics.

Function App: Configure/Validate the Environment variables

Note

This example is using system-assigned managed identity to assign RBACs (Role-based Access Control).

Under Settings, go to Environment variables. And + Add the following variables:
- COSMOS_DB_ENDPOINT: Your Cosmos DB account endpoint 🡢 Review the existence of this, if not create it
- COSMOS_DB_KEY: Your Cosmos DB account key 🡢 Review the existence of this, if not create it
- COSMOS_DB_CONNECTION_STRING: Your Cosmos DB connection string 🡢 Review the existence of this, if not create it
- invoicecontosostorage_STORAGE: Your Storage Account connection string 🡢 Review the existence of this, if not create it
- FORM_RECOGNIZER_ENDPOINT: For example: https://<your-form-recognizer-endpoint>.cognitiveservices.azure.com/ 🡢 Review the existence of this, if not create it
- FORM_RECOGNIZER_KEY: Your Documment Intelligence Key (Form Recognizer). 🡢
- FUNCTIONS_EXTENSION_VERSION: ~4 🡢 Review the existence of this, if not create it
- WEBSITE_RUN_FROM_PACKAGE: 1 🡢 Review the existence of this, if not create it
- FUNCTIONS_WORKER_RUNTIME: python 🡢 Review the existence of this, if not create it
- FUNCTIONS_NODE_BLOCK_ON_ENTRY_POINT_ERROR: true (This setting ensures that all entry point errors are visible in your application insights logs). 🡢 Review the existence of this, if not create it
- Click on Apply to save your configuration.

Function App: Develop the logic

You need to install VSCode
Install python from Microsoft store:
Open VSCode, and install some extensions: python, and Azure Tools.
Click on the Azure icon, and sign in into your account. Allow the extension Azure Resources to sign in using Microsoft, it will open a browser window. After doing so, you will be able to see your subscription and resources.
Under Workspace, click on Create Function Project, and choose a path in your local computer to develop your function.
Choose the language, in this case is python:
Select the model version, for this example let's use v2:
For the python interpreter, let's use the one installed via Microsoft Store:
Choose a template (e.g., Blob trigger) and configure it to trigger on new PDF uploads in your Blob container.
Provide a function name, like BlobTriggerContosoPDFInvoicesDocIntelligence:
Next, it will prompt you for the path of the blob container where you expect the function to be triggered after a file is uploaded. In this case is pdfinvoices as was previously created.
Click on Create new local app settings, and then choose your subscription.
Choose Azure Storage Account for remote storage, and select one. I'll be using the invoicecontosostorage.
Then click on Open in the current window. You will see something like this:
Now we need to update the function code to extract data from PDFs and store it in Cosmos DB, use this an example:
1. PDF Upload: A PDF file is uploaded to the Azure Blob Storage container (pdfinvoices).
2. Trigger Azure Function: The upload triggers the Azure Function BlobTriggerContosoPDFLayoutsDocIntelligence.
3. Initialize Clients: Sets up connections to Azure Document Intelligence and Cosmos DB.
  - Initializes the DocumentAnalysisClient using the FORM_RECOGNIZER_ENDPOINT and FORM_RECOGNIZER_KEY environment variables.
  - Initializes the CosmosClient using Azure Active Directory (AAD) via DefaultAzureCredential.
4. Read PDF from Blob Storage: Reads the PDF content from the blob into a byte stream.
5. Analyze PDF: Uses Azure Document Intelligence to analyze the layout of the PDF.
  - Calls begin_analyze_document with the prebuilt-layout model.
  - Waits for the analysis to complete and retrieves the layout result.
6. Extract Layout Data: Parses and structures the layout data from the analysis result.
  - Extracts lines, tables, and selection marks from each page.
  - Logs styles (e.g., handwritten content) and organizes data into a structured dictionary.
7. Save Data to Cosmos DB: Saves the structured layout data to Cosmos DB.
  - Ensures the database (ContosoDBDocIntellig) and container (Layouts) exist or creates them.
  - Inserts or updates the layout data using upsert_item.
8. Logging (Process and Errors): Logs each step of the process, including success messages and detailed error handling for debugging and monitoring.
- Update the function_app.py, for example see the code used in each demo:
  Template Blob Trigger Function Code updated
- Now, let's update the requirements.txt, see the code used in each demo:
  Template requirements.txt Updated requirements.txt
- Since this function has already been tested, you can deploy your code to the function app in your subscription. If you want to test, you can use run your function locally for testing.
  - Click on the Azure icon.
  - Under workspace, click on the Function App icon.
  - Click on Deploy to Azure.
  - Select your subscription, your function app, and accept the prompt to overwrite:
  - After completing, you see the status in your terminal: