Python is one of the most popular programming languages for data analysis, automation, and artificial intelligence. It’s my favorite programing language by far, and I use it for a lot of scripting or client work. Let’s explore some potential approaches and advantages and disadvantages of each. Also, this is an overview of the possibilities, not a tutorial, since most of the possibilities require a few steps and security considerations, but I’ll do my best to guide you through the process, without overwhelming you.
The Native Capabilities
Power Automate cloud flows don’t have a native action to directly execute Python code within the flow itself. Power Automate cloud flows primarily work by connecting various services through pre-built connectors and actions, not by executing custom code directly within the flow.
However, this doesn’t mean you can’t use Python with your Power Automate solutions. It just means you’ll need to host and run your Python code elsewhere and connect to it from your flow.
Most solutions will require either a paid Power Automate account since we’ll require premium connectors, so please take this into consideration.
Solutions
Here are a few solutions to consider. This is not an exhaustive list, so it’s perfectly possible to have other solutions. If you think that there’s a solution that is missing please reach out and I’ll add it.
Azure Functions
The most versatile way to integrate Python with Power Automate cloud flows is using Azure Functions. You can quickly deploy a “Function App” and run the code you already have. This approach makes sense when:
- You don’t want to manage infrastructure
- Your scripts are fast to run
- There aren’t complex dependencies in your code. If your code becomes too complex maybe you should think of another solution to save costs and manage complexity.
I wrote a deep dive on how to use Azure Functions and how to run Python code and get it directly in Power Automate. There are a few steps and some security considerations, so check it out for more details.
Custom API/Web Service
For more complex scenarios, you might build a dedicated Python API:
- Create a web API using frameworks like Flask or FastAPI
- Deploy it to Azure App Service or another cloud provider
- Call it from Power Automate using HTTP actions
This approach makes sense when:
- You need multiple related Python operations
- You want better API management and documentation
- You need to reuse the Python functionality in multiple systems
Azure Automation with Python Runbooks
For scheduled or triggered tasks:
- Create a Python runbook in Azure Automation
- Connect to it from Power Automate using the Azure Automation connector
- Start the runbook and optionally wait for completion
This works well for:
- Background processing tasks
- Scheduled maintenance operations
- Operations that don’t need real-time responses
Common Integration Challenges
When integrating Python with Power Automate cloud flows, keep these potential challenges in mind:
Performance Considerations
- Azure Functions have cold start times that can add latency (unless you pay to have them “always on”)
- Set an appropriate timeout for your HTTP actions in Power Automate
- Consider the consumption plan vs. premium plan tradeoffs for Azure Functions
Security Best Practices
- Use managed identities where possible for authentication between services
- Store sensitive credentials in Azure Key Vault, not in your code
- Implement proper input validation in your Python code
- Use Azure API Management for additional security layers
Debugging and Monitoring
- Add logging in both Power Automate and your Python code
- Set up Azure Application Insights for your functions so that, if something goes wrong, you can understand why and fix it.
- Test your Python code independently before integrating with Power Automate. You can do it locally using VSCode for example before deploying it to Azure.
Cost Considerations
Remember that using Azure services to run your Python code will add costs:
- Azure Functions: You pay for execution time and memory usage
- Azure App Service: You pay for the hosting plan and also have to deal with server maintenance and fix any issues that go wrong. Your time counts as a cost as well.
- Azure Automation: You pay for job runtime
For smaller workloads, the consumption plans may keep costs minimal, but for larger workloads, monitor usage carefully.
As stated before, I recommend running it a few times with a few test cases (use the most complex you can find) to get an average and then extrapolate that information per month. This way you can have an idea if you fit in the “free” allowance and if not, how much would you pay.
Final Thoughts
While Power Automate cloud flows can’t run Python scripts directly, the integration options with Azure services provide powerful capabilities when you need Python’s data processing, machine learning, or automation capabilities alongside Power Automate’s workflow orchestration.
The Azure Functions approach offers the best balance of flexibility, maintainability, and ease of integration for most scenarios. By keeping your workflow orchestration in Power Automate and your complex processing in Python, you can build solutions that leverage the strengths of both technologies.
Remember that good system design often involves separation of concerns—let Power Automate handle the workflow and connectivity aspects it excels at, while delegating complex data processing to Python running in the appropriate cloud service. Finally, if you’re not an infrastructure expert or think that it will require a lot of work, consider the hosted options so that you can save time there and focus on your projects.
Photo by Zach Graves on Unsplash
