Fullstack Flask: Building and Deploying APIs on Cloud with Docker

 In the modern web development landscape, Flask remains a lightweight and flexible choice for building APIs using Python. When combined with Docker and cloud platforms like AWS, Azure, or Google Cloud, developers can build scalable and portable APIs that can be deployed seamlessly across environments.

This blog walks you through how to build a fullstack Flask API and deploy it on the cloud using Docker, a tool that has become the backbone of modern DevOps workflows.


Why Flask and Docker for API Development?

Flask is a micro web framework that's perfect for creating RESTful APIs quickly. It allows for rapid development without too much overhead. Docker, on the other hand, packages your application and its dependencies into isolated containers, ensuring it runs the same way in development, testing, and production environments.

Together, Flask and Docker offer:

Reproducibility

Environment independence

Easy cloud deployment

Fast scaling with container orchestration (e.g., Kubernetes)


Step 1: Create a Simple Flask API

Start with a simple Flask application:

python


# app.py

from flask import Flask, jsonify


app = Flask(__name__)


@app.route("/api/hello")

def hello():

    return jsonify({"message": "Hello, World!"})


if __name__ == "__main__":

    app.run(host="0.0.0.0", port=5000)

Also, add a requirements.txt:


ini

Copy

Edit

Flask==2.2.2

Step 2: Dockerize the Flask App

Create a Dockerfile to containerize your Flask app:


dockerfile

Copy

Edit

# Dockerfile

FROM python:3.10-slim


WORKDIR /app


COPY requirements.txt .

RUN pip install -r requirements.txt


COPY . .


CMD ["python", "app.py"]

Then, build and run the Docker container:


bash

Copy

Edit

docker build -t flask-api .

docker run -p 5000:5000 flask-api

Visit http://localhost:5000/api/hello to see your API response.


Step 3: Push Docker Image to Docker Hub

To deploy to the cloud, push your image to a registry like Docker Hub:


bash

Copy

Edit

docker tag flask-api yourusername/flask-api

docker push yourusername/flask-api

You can also use AWS ECR, Google Artifact Registry, or Azure Container Registry.


Step 4: Deploy to the Cloud

Now that your image is hosted online, deploy it using your preferred cloud platform.

Example: Deploy on AWS (Elastic Beanstalk with Docker)

Create a .ebextensions and Dockerrun.aws.json file.

Use the Elastic Beanstalk CLI to initialize and deploy.

AWS pulls your Docker image and runs it on an EC2 instance with a load balancer.


Or Use Google Cloud Run:

Enable Cloud Run in your GCP console.

Deploy directly from the command line:


bash

Copy

Edit

gcloud run deploy --image gcr.io/your-project/flask-api --platform managed


Benefits of This Workflow

✅ Portability – Run your app anywhere Docker is supported

✅ Isolation – Avoid dependency issues

✅ Scalability – Easily deploy multiple instances

✅ Speed – Go from local development to live API in minutes


Conclusion

Building and deploying Flask APIs on the cloud with Docker brings speed, flexibility, and confidence to your development process. This approach simplifies deployment and ensures your APIs are production-ready and scalable from day one.

With just a few tools—Flask, Docker, and a cloud provider—you can build robust backend systems that power modern applications and microservices. It’s a must-learn workflow for any aspiring fullstack Python developer.

Learn FullStack Python Training

Read More : Fullstack Flask Deployment: Setting Up Continuous Delivery on AWS with CodePipeline

Read More : Deploying Fullstack Python Apps on AWS Lambda for Serverless Architecture

Read More : Fullstack Python: Automating Cloud Deployments with Terraform

Visit Our IHUB Talent Training Institute in Hyderabad

Comments

Post a Comment

Popular posts from this blog

How to Use Tosca's Test Configuration Parameters

Tosca by Tricentis is a powerful model-based test automation tool widely used in enterprise environments for end-to-end testing. One of its most useful features is Test Configuration Parameters (TCPs), which provide flexibility and control in managing test execution. TCPs enable testers to create reusable test cases by passing dynamic values without hardcoding them. This significantly enhances test maintenance, scalability, and efficiency. In this blog, we’ll explore what Test Configuration Parameters are, how to create and use them, and the best practices to follow when working with TCPs in Tosca. What Are Test Configuration Parameters? Test Configuration Parameters are custom-defined key-value pairs that can be associated with test cases, test case folders, or execution lists in Tosca. They help manage configuration data such as environment URLs, browser types, credentials, or any other variable data that may differ across test runs. By using TCPs, you can avoid duplicating test case...
Read more

Using Hibernate ORM for Fullstack Java Data Management

In the world of fullstack Java development, managing data efficiently and seamlessly across the application is critical. One of the most powerful tools in the Java ecosystem for handling database operations is Hibernate ORM (Object-Relational Mapping). Hibernate simplifies the interaction between Java applications and relational databases, reducing boilerplate code and improving maintainability. This blog explores how Hibernate ORM works, why it’s widely used, and how it fits into the data management strategy of a fullstack Java application. What is Hibernate ORM? Hibernate ORM is an open-source Java framework that provides a powerful and flexible mechanism for mapping Java objects to database tables. It abstracts the low-level JDBC code, allowing developers to work with high-level object-oriented concepts. Instead of writing raw SQL queries, Hibernate allows developers to define database interactions using simple Java classes (also known as POJOs) and annotations or XML configurations...
Read more

Creating a Test Execution Report with Charts in Playwright

Modern test automation isn't just about running tests—it's also about presenting clear, visual feedback on test results. When working with Playwright, one of the most powerful end-to-end testing tools for modern web apps, generating detailed execution reports can help your team track progress, identify issues quickly, and make data-driven decisions. A visual report with charts brings your testing efforts to life. In this blog, we’ll walk through how to create a test execution report with charts using Playwright’s built-in capabilities along with third-party tools for enhanced visualization. Why Visual Reports Matter Plain text logs and console outputs are hard to parse at scale. A good report should answer questions like: How many tests passed or failed? What’s the overall success rate? Which tests are flaky or slow? How have test results changed over time? Visualizing this data with charts—like pie charts, bar graphs, or timelines—helps QA teams and stakeholders better underst...
Read more