Deploy Serverless Functions with Python in 2025

You are all aware that today is a cloud-based world, and developers want to build fast, low-cost, and scalable apps. Serverless deployment is a popular method to create these types of apps.

This article will cover the following topics:

What does serverless deployment mean
How it works with Python
Which platforms support it
And how to build a real example

So, read this article carefully and learn how Serverless will make our project better and easier to manage.

What is Serverless Deployment?

Serverless doesn’t mean there are no servers, but it means you don’t manage the servers manually. The cloud providers like AWS, Google Cloud, and Azure dynamically manage your resources and data according to your premium plans.

Your work is to write the Python code and then upload the code to a platform like AWS Lambda, and it will run automatically when needed. We don’t need to manage our servers for traffic control, loading problems, etc.

I liked the main things about this platform are that we don’t need to pay extra money; we pay only for what we use for our project.

Why Use Python for Serverless?

We recommended Python for serverless deployment because it runs fast and does not take much memory, and its powerful libraries, like boto3, requests, and pandas, work smoothly in serverless setups.

Python fully supports writing small programs, such as automation scripts, APIs, or event-based tasks.

What Platforms Support Python Serverless Deployment?

Now, we will see which platform is best for running the Python code in a serverless way.

Platform	Service Name	Python Support
AWS	Lambda	Fully supports Python
Google Cloud	Cloud Functions	Fully supports Python
Microsoft Azure	Azure Functions	Fully supports Python
Vercel / Netlify	Edge Functions	Limited Python support
IBM Cloud	Cloud Functions	Supports Python via OpenWhisk

This table shows us which platforms we should select for our Python project.

How to Set Up Serverless Deployment in Python?

We will teach you how to deploy AWS Lambda for our project in Python. because it is a widely used platform in the market.

Step-by-Step Explanation:

Step 1: Install AWS CLI and Configure using this command

First, install the AWS command line tool:

pip install awscli

Then set up your AWS credentials using this command:

aws configure

In this setup, you will have to enter your access key ID, secret access key, AWS region (us-east-1), and output format (enter json).

Step 2: Write a Simple Python Function (lambda_function.py)

Create a lambda_function.py file and paste this code:

def lambda_handler(event, context):
    return {
        'statusCode': 200,
        'body': 'Hello from Python Lambda!'
    }

This function will return a “Hello” message when it’s triggered.

Step 3: Create Deployment Package

If you’re using any Python libraries (like requests), you need to bundle them:

mkdir mylambda && cd mylambda
pip install requests -t .
cp ../lambda_function.py .
zip -r mylambda.zip .

Run this command in step-by-step explanations below:

1. Create a folder for your Lambda package and move into it:

mkdir mylambda && cd mylambda

2. Install external libraries (like requests) into this folder:

pip install requests -t.

// -t means: install the library into the current directory.

3. Copy your Lambda function into this folder:

cp ../lambda_function.py .

This copies lambda_function.py from the parent directory.

4. Zip File:

zip -r mylambda.zip .

This creates a zip file with your code and dependencies.

Step 4: Deploy to AWS Lambda

Use this command to deploy your function to AWS:

aws lambda create-function \
  --function-name python-serverless-demo \
  --runtime python3.11 \
  --role arn:aws:iam::YOUR_ACCOUNT_ID:role/execution_role \
  --handler lambda_function.lambda_handler \
  --zip-file fileb://mylambda.zip

Your function is now live and triggered using an API Gateway or other AWS services.

Cons of Serverless with Python

Sometimes you will have to wait when the function runs for the first time. This is called a “cold start”. It can be a delay of a few seconds.
A function can run for a maximum of 15 minutes in AWS Lambda. It means you can’t use serverless if your task needs more time.
Serverless is not suitable when your app runs for a long time (like video streaming or heavy processing). It’s good for only short tasks.
We have to set IAM (Identity and Access Management) for our function. Your function might fail if we set the wrong information.

How To Deploy Serverless Functions with Python in 2025 (Step-by-Step Guide)