Modern programming language frameworks, like FastAPI, make it really simple to build high-quality software products quickly. Moreover, using these frameworks, the development process becomes more fun and less tedious. 

That said, FastAPI is a new Python web framework that is both strong and delightful. FastAPI is a sophisticated, fast (high-performance) web framework for developing Python 3.6+ APIs with standard Python-type hints. 

When combined with React on the front end and MongoDB as the datastore, it creates a complete web application stack named FARM (FastAPI, React, and MongoDB).  It is easy to learn and outperforms the MERN stack (MongoDB, Express, React, Node.js) on benchmarks. 

Do you want to use FastAPI with MongoDB for your web application? If the answer is “Yes,” you landed on the correct page. In this blog post, you will learn about FastAPI MongoDB integration. Moreover, a brief introduction to FastAPI and MongoDB is also presented.

What is FastAPI?

FastAPI Logo
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FastAPI is a lighter, fast Python framework for creating APIs. As the name suggests, “FastAPI” is a high-speed and easy-to-develop API that automatically makes well-swagger documentation for your API. FastAPI was created by Sebastián Ramrez and was released in 2018.

FastAPI’s core use case is creating API endpoints, delivering Python dictionary data as JSON, or utilizing the OpenAPI standard, which includes an interactive Swagger UI. FastAPI is still in its early stages but is already used at firms like Uber, Netflix, and Microsoft.

For more information about FastAPI, see the official website.

What is MongoDB?

MongoDB Logo
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It is a high-performance document-oriented database with a NoSQL framework. It utilizes collections (tables) with various documents (records) and allows the user to store data in a non-relational fashion.

MongoDB saves its data as objects frequently referred to as documents. These documents are held in collections, similar to how tables function in relational databases. MongoDB is well-known for its scalability, ease of use, dependability, and lack of a requirement to utilize a single schema across all stored documents, allowing them to contain varied fields (columns).

For more information about MongoDB, see the official website.

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Why is FastAPI MongoDB Connection Useful?

All web frameworks must bridge the gap between functionality and developer flexibility. FastAPI is closer to Flask in terms of functionality, yet it strikes a better balance. With the FastAPI MongoDB integration, you can leverage all advantages of FastAPI and MongoDB together as a developer.

The following are the crucial advantages of FastAPI MongoDB integration :

  • Fast: Application development with FastAPI is extremely fast, comparable to NodeJS and Go. It is known as one of the quickest Python frameworks on the market.
  • Fast coding: Speed up the feature development pace by 200 to 300 percent.
  • Intuitive: Excellent editing assistance. Debugging takes less time.
  • Simple: Designed to be simple to use and understand. Spend less time reading documents.
  • Strong: Developer can obtain production-ready code. With interactive documentation generated automatically.
  • Standards-based: Based on (and completely compatible with) the open API standards OpenAPI (formerly Swagger) and JSON Schema.

We’ve seen how FastAPI is the most capable web framework, and MongoDB is the most robust and flexible NoSQL database. As a developer, you can leverage the versatility of MongoDB to work with almost any type of data and the unbeatable features of FastAPI to develop fast and secure web applications.

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Steps for FastAPI MongoDB Integration

To demonstrate how FastAPI MongoDB integration works, we will build a Cleaning CRUD API with FastAPI and MongoDB.


  • Python 3.9.0
  • A MongoDB Atlas cluster.
  • Basic knowledge of FastAPI
  • Knowledge about  Pydantic library
  • Visual Studio Code Editor

If you have not downloaded and installed MongoDB yet, click here to download.

Step 1: Initial Setup and Creating Virtual Environment

Now you are ready to start your project. First, let’s create a new folder to store the project named “fastapi-mongo-demo.” Use the following command to create a new folder.

$ mkdir fastapi-mongo-demo

Let’s open this folder in Visual Studio Code Editor.

Create a virtual environment in your project using the following command.

python -m venv env;

Activate the virtual environment by running the command given below.


Step 2: Install Dependencies

  • Install the FastAPI package using the following pip command.
pip install fastapi
  • Install the PyMongo package to connect with MongoDB.
pip install pymongo
  • As FastAPI uses the Starlette framework for web requests, you must install an ASGI server. Install the ASGI server for FAstAPI using the following command.
pip install uvicorn

Note: Unicorn is just as quick as the server implementation, which aids in the interaction between your application and the backend.

Step 3: Create Root End Point for FastAPI

Now you have successfully installed the required dependencies, let’s develop our first API program for our root endpoint (homepage). First, create the file in VS Code.

Enter the following code into

from fastapi import FastAPI

app = FastAPI()

def index():
    return {"message": "Welcome To FastAPI World"}

In the next step let’s run and test our API:

To run the code write the following command in the terminal;

uvicorn main:app --reload

As a result of the above command, your API should be running on localhost at

In your browser, go to http://localhost:8000. You should get the following output.

"message": "Welcome To FastAPI World"

Kudos you have successfully built your API 🎉🎉

The interactive API documentation is also available at http://localhost:8000/docs. You will get the automatically created documentation of your API at the above link. You must get the following similar image as in the output. 

FastAPI Automatically Created Documentation

Step 4: Configuring MongoDB Atlas

Now you have successfully created your first FastAPI program. In the next step, let’s set up our MongoDB Atlas to store our cleaning service data.

  • Sign up for your free MongoDB cloud storage here.
  • Create a project and name it ‘cleaningstore’ after signing up.
  • Click on the ‘Build a Cluster‘ button. After filling required details your clusters will be created within 1-4minutes. 
Create Cluster in MongoDB Atlas
  • After creating your cluster, connect it with Cloud storage. 
  • While choosing the connection method, select “Connect your application.”
  • To use your cluster connection, copy the connection string that appears after selecting Python as the driver.
  • Save your username and password; it will be used in the next step.

Congratulations, you have successfully prepared and configured our MongoDB Atlas.

Step 5: Create Add User End Point

After creating and configuring cloud storage for your application. In the next step, you will be creating an endpoint. A MongoDB connection URI is required to connect the FastAPI application with the MongoDB cluster. So let’s store the required URI in a file.

Create a file and put the following code in it:

# MongoDB attributes
mongodb_uri = 'mongodb+srv://<usersdata>?retryWrites=true&w=majority'
port = 8000  

Ensure you add your correct username and password to the connection string mentioned above.

Now create a file and write the following code in it. This is a connection file that establishes a connection with MongoClient. Along with the connection, a database name “customerdata” will also be created.

from pymongo import MongoClient
import settings

client = MongoClient(settings.mongodb_uri, settings.port)
db = client[customerdata]

Remember, you created the root endpoint in Step 3. Now you are going to create another endpoint named “addUser.” 

from fastapi import FastAPI
import connection
from bson import ObjectId
from schematics.models import Model

class Customer(Model):
    cust_id= ObjectId()
    cust_email = EmailType(required=True)
    cust_name = StringType(required=True)

# An instance of class User
newuser = Customer()

# funtion to create and assign values to the instanse of class Customer created
def create_user(email, username):
    newuser.cust_id = ObjectId()
    newuser.cust_email  = email
    newuser.cust_name = username
    return dict(newuser)

app = FastAPI()

# Our root endpoint
def index():
    return {"message": "Welcome to FastAPI World"}

# Signup endpoint with the POST method"/signup/{email}/{username}")
def addUser(email, username: str):
    user_exists = False
    data = create_user(email, username)

    # Covert data to dict so it can be easily inserted to MongoDB

    # Checks if an email exists from the collection of users
    if connection.db.users.find(
        {'email': data['email']}
        ).count() > 0:
        user_exists = True
        print("Customer Exists")
        return {"message":"Customer Exists"}
    # If the email doesn't exist, create the user
    elif user_exists == False:
        return {"message":"User Created","email": data['email'], "name": data['name']}

In the above code, you created the Signup endpoint. This endpoint simply creates a new user in the MongoDB store and returns information about the newly generated user or if the user already exists.

Run your code to check API is working. Type the following command in the terminal.

uvicorn main:app --reload


In this blog, you have learned about FastAPI MongoDB integration. FastAPI is an excellent, fast framework for creating an API. If you are unfamiliar with it, it’s the right time to start learning and exploring it. FastAPI seamlessly connects with MongoDB, simplifying development.

Apart from MongoDB, you would use several applications and databases across your business for Marketing, Accounting, Sales, Customer Relationship Management, etc. It is essential to consolidate data from all these sources to get a complete overview of your business performance. To achieve this, you need to assign a portion of your Engineering Bandwidth to Integrate Data from all sources, Clean & Transform it, and finally, Load it to a Cloud Data Warehouse or a destination of your choice for further Business Analytics. All of these challenges can be comfortably solved by a Cloud-Based ETL tool such as Hevo Data.

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Hevo Data, a No-code Data Pipeline, can seamlessly transfer data from a vast sea of 100+ sources such as MongoDB & MongoDB Atlas to a Data Warehouse or a Destination of your choice to be visualized in a BI Tool. It is a reliable, completely automated, and secure service that doesn’t require you to write any code!  

If you are using MongoDB as your NoSQL Database Management System and searching for a no-fuss alternative to Manual Data Integration, then Hevo can effortlessly automate this for you. Hevo, with its strong integration with 100+ sources & BI tools(Including 40+ Free Sources), allows you to not only export & load data but also transform & enrich your data & make it analysis-ready in a jiffy.

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Tell us about your experience of learning about the FastAPI MongoDB Integration! Share your thoughts with us in the comments section below.

Former Marketing Analyst, Hevo Data

Kamya is a data science enthusiast who loves writing content to help data practitioners solve challenges associated with data integration. He has a flair for writing in-depth articles on data science.

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