Microservices With Python: Architecture, Setup, Containerization, & Deployment

Let’s say you want to update a payment gateway without disrupting the entire user checkout experience. This is difficult in a monolithic application, but microservices architecture fully decouples these components. 

This approach structures an application as a suite of small, independent services. Each of them runs a unique process and communicates through lightweight APIs. Python helps build these services with a developer-friendly syntax and robust async frameworks.

Through this blog, we’ll explore microservices with Python for targeted updates and faster innovation. Let’s begin.

What is Microservices?

Microservices is an architectural style that structures an application as a collection of loosely coupled and independently deployable services. Instead of building a single, monolithic codebase, the application is broken down into a suite of small services. Each of them is aligned with a specific capability (e.g., user authentication, product search and payment processing).

Each service runs its own process and communicates with others through well-defined, lightweight APIs. That is, often over HTTP/REST or messaging queues.

This decentralization grants development teams the autonomy to build, scale, and update their service with its own technology stack and database. It doesn’t impact the entire system.

What is the Core Principle of Microservices Architecture?

Decentralization is the core principle of microservices architecture. A monolithic application is a single, unified unit. But a microservices-based system is decentralized in its management, data, and development.

That manifests in three key ways:

Decentralized Governance

Teams have the autonomy to design, build, and deploy their service independently. This often means choosing the best technology stack (e.g., Python with FastAPI for one service, Node.js for another) for the specific problem.

Decentralized Data Management

Each service owns its private database or data model. Services never access another service’s database directly; they only interact through public APIs. This prevents tight coupling and makes sure that services are independent.

Decentralized Execution

The application logic is distributed across multiple, independently scalable processes. A failure in one service does not bring down the entire application. That leads to greater overall system resilience.

In essence, every design decision in microservices is guided by moving away from a centralized, monolithic model to one of distributed ownership and responsibility.

Key Components of Microservices Architecture

Transitioning to a microservices architecture requires more than just breaking code into smaller pieces. There are some key components involved.

The Services

Small, single-purpose units encapsulating a specific business domain (e.g., User Service, Order Service, etc.). They are the core building blocks.

API Gateway

API Gateway is the single entry point for all client requests. It handles request routing, composition, protocol translation, and often authentication. That shields the internal services from direct external exposure.

Service Discovery & Registry

A mechanism that allows services to find and communicate dynamically with each other in a distributed environment. It’s especially critical in cloud-based systems where service instances may change location (IP/port).

Inter-Service Communication

The defined protocols services use to talk, typically either synchronous (e.g., HTTP/REST, gRPC) or asynchronous (e.g., message brokers like Apache Kafka).

Centralized Configuration Management

This externalized configuration service allows all service instances to retrieve their application settings from a central location without needing redeployment.

Distributed Tracing & Monitoring

Tools that aggregate logs, metrics, and traces from all service instances. This is essential for observing system health, debugging issues, and tracking a request’s path across multiple services.

Containerization & Orchestration

Technologies like Docker (for packaging services and their dependencies into containers) and Kubernetes (for automating deployment and management of that containers). They are the de facto standards for deploying microservices.

How to Build Microservices With Python?

Building a microservice in Python web development involves creating a small, independent, and fully functional application. Here, we’ll use FastAPI for its modern features, performance, and automatic interactive documentation.

Prerequisites

Before you start, ensure you have the following installed and basic knowledge of:

• Python 3.8+ installed on your system.
• Understanding of RESTful API concepts (HTTP methods, status codes).
• A code editor like VS Code.
• pip (Python’s package installer) for managing dependencies.

Step 1: Create the Project Structure

A clear structure is vital for maintainability. Create a new directory for your service.

mkdir user-service
cd user-service

Inside, create the following structure:

user-service/
│
├── app/
│   ├── __init__.py
│   ├── main.py          # The core application file
│   └── models.py        # For defining data models
│
├── requirements.txt     # Project dependencies
└── README.md

This separates your application code (app/) from configuration files.

Step 2: Set Up a FastAPI Server

Here’s how you go about setting up the FastAPI server.

Create a Virtual Environment

This isolates your project’s dependencies.

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install Dependencies

Create a requirements.txt file and add:

fastapi
uvicorn[standard]

Then install them.

pip install -r requirements.txt

Here,

• FastAPI is a web framework.
• Uvicorn is an ASGI server to run your FastAPI app.

Build the Basic Server

In app/main.py, import FastAPI and create an application instance.

from fastapi import FastAPI

app = FastAPI(title="User Service", version="1.0.0")

@app.get("/")
async def root():
    return {"message": "User Service is running successfully!"}

Step 3: Define API Endpoints

This step involves defining data models and the core logic for your API.

Define a Data Model (in app/models.py).

Use Pydantic models to define the structure of your data, which automatically handles validation.

from pydantic import BaseModel

class User(BaseModel):
    id: int
    email: str
    username: str

class UserCreate(BaseModel):
    email: str
    username: str

Create In-Memory Storage and Endpoints (in app/main.py)

Update the file to include new endpoints for creating and retrieving users.

from fastapi import FastAPI, HTTPException
from .models import User, UserCreate

app = FastAPI(title="User Service", version="1.0.0")

# In-memory "database" for demonstration
fake_db = {}

@app.get("/")
async def root():
    return {"message": "User Service is running successfully!"}

@app.get("/users/{user_id}", response_model=User)
async def read_user(user_id: int):
    if user_id not in fake_db:
        raise HTTPException(status_code=404, detail="User not found")
    return fake_db[user_id]

@app.post("/users/", response_model=User)
async def create_user(user: UserCreate):
    # Simulate creating an ID
    user_id = len(fake_db) + 1
    new_user = User(id=user_id, **user.dict())
    fake_db[user_id] = new_user
    return new_user

Step 4: Test the Service

Finally, you test the microservices.

Run the Server

Start the Uvicorn server from your terminal.

uvicorn app.main:app --reload

• app.main:app tells Uvicorn to import the app object from app/main.py.
• The –reload flag enables auto-reload on code changes (for development only).

Interactive API Docs

Navigate to http://127.0.0.1:8000/docs in your browser. FastAPI automatically provides Swagger UI documentation. You can interact with your /users/ endpoints directly from here.

Test with an HTTP Client

Use curl or a tool like Postman or Thunder Client (VS Code extension) to send requests.

Create a User (POST)

curl -X 'POST' 'http://127.0.0.1:8000/users/' \
-H 'Content-Type: application/json' \
-d '{"email": "john@example.com", "username": "john_doe"}'

Retrieve a User (GET)

curl -X 'GET' 'http://127.0.0.1:8000/users/1'

You have now successfully built, run, and tested a basic Python microservice. Next up, we connect to a real database, add authentication, and containerize the service with Docker.

Python Microservices Communication

In a microservices architecture, services are decoupled and must collaborate to fulfill business processes. This interaction is achieved through well-defined communication patterns. Python offers excellent libraries for implementing both synchronous and asynchronous communication.

Synchronous Communication (Request-Response) (HTTP/REST)

The most common method for synchronous communication is using HTTP calls with REST APIs. The requests library is the standard tool in Python for this.

Let’s look at the implementation example of order-service. The order-service needs to validate a user ID with the user-service before creating an order.

# order_service/app/main.py
from fastapi import FastAPI, HTTPException, Depends
import requests  # Make sure to pip install requests

app = FastAPI(title="Order Service")

# Configuration: URL of the User Service
USER_SERVICE_URL = "http://user-service:8001/users/"

@app.post("/orders/")
async def create_order(user_id: int):
    """
    Creates an order after validating the user_id.
    """
    # Step 1: Synchronous HTTP call to User Service
    try:
        response = requests.get(f"{USER_SERVICE_URL}{user_id}")
        # Raise an exception for 4XX/5XX status codes
        response.raise_for_status()

        # If successful, the user exists
        user_info = response.json()
        print(f"Order created for user: {user_info['username']}")

        # ... logic to create the order ...
        return {"message": f"Order created for user_id {user_id}"}

    except requests.exceptions.HTTPError:
        # Handle 404 from the User Service
        raise HTTPException(status_code=404, detail="User not found")
    except requests.exceptions.ConnectionError:
        # Handle network errors (e.g., user-service is down)
        raise HTTPException(status_code=503, detail="User service unavailable")

Asynchronous Communication (Message Brokers)

For better decoupling and resilience, use a message broker like RabbitMQ or Redis. A service publishes an event (e.g., OrderCreated) without knowing which other services will consume it. The pika library is a common choice for RabbitMQ.

The order-service publishes an event when an order is created. The email-service and inventory-service listen for this event to trigger their own processes.

Publisher (in order-service)

# order_service/message_queue/publisher.py
import pika
import json

def publish_order_created_event(order_data):
    """Publishes an 'order.created' event to the message queue."""
    # Connect to RabbitMQ server
    connection = pika.BlockingConnection(pika.ConnectionParameters('rabbitmq'))
    channel = connection.channel()

    # Declare a topic exchange (ensures it exists)
    channel.exchange_declare(exchange='orders', exchange_type='topic')

    # Publish the message to the exchange with a routing key
    channel.basic_publish(
        exchange='orders',
        routing_key='order.created',  # The event topic
        body=json.dumps(order_data)   # The message payload
    )
    print(f" [x] Sent 'order.created' event: {order_data}")
    connection.close()

# In your order creation endpoint, call this function after saving the order
# publish_order_created_event({"order_id": 123, "user_id": 456, "total": 99.99})

Consumer (in email-service)

# email_service/message_queue/consumer.py
import pika
import json

def start_consumer():
    """Starts a consumer to listen for 'order.created' events."""
    connection = pika.BlockingConnection(pika.ConnectionParameters('rabbitmq'))
    channel = connection.channel()

    channel.exchange_declare(exchange='orders', exchange_type='topic')
    # Create an anonymous, exclusive queue and bind it to the exchange
    result = channel.queue_declare(queue='', exclusive=True)
    queue_name = result.method.queue

    # Bind to all events with the 'order.created' routing key
    channel.queue_bind(exchange='orders', queue=queue_name, routing_key='order.created')

    def callback(ch, method, properties, body):
        # This function is called when a message is received
        order_data = json.loads(body)
        print(f" [x] Received {method.routing_key}: {order_data}")
        # ... logic to send a confirmation email ...

    channel.basic_consume(queue=queue_name, on_message_callback=callback, auto_ack=True)
    print(' [*] Email Service waiting for messages. To exit press CTRL+C')
    channel.start_consuming() # This is a blocking call

# Run the consumer when the service starts (e.g., in a separate thread)

Containerizing Microservices With Docker

After the microservices are tested, containerization is the foundational step in their deployment. It packages a service and all its dependencies into a single, standardized portable unit called a container.

Docker is the dominant platform for this. Containerizing your Python microservices ensures they run consistently across any environment, from a developer’s laptop to a production cluster.

Step 1: Create a Dockerfile

A Dockerfile contains the commands you can call on the command line to assemble an image. Create this file in the root directory of your service (e.g., user-service/).

# Use an official Python runtime as a base image
FROM python:3.11-slim-bookworm

# Set the working directory in the container
WORKDIR /app

# Copy the requirements file first to leverage Docker cache
COPY requirements.txt .

# Install any dependencies specified in requirements.txt
RUN pip install --no-cache-dir -r requirements.txt

# Copy the rest of the application code into the container
COPY ./app ./app

# Expose the port the app runs on
EXPOSE 8000

# Define the command to run the application
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

Consider these key instructions:

• FROM: Starts from a minimal base image with Python pre-installed.
• WORKDIR: It sets the working directory in the container.
• COPY requirements.txt .: Copies the dependency list. This is done separately to leverage Docker’s build cache. If requirements.txt doesn’t change, the expensive pip install step is skipped in subsequent builds.
• RUN pip install…: Installs the Python dependencies into the container.
• COPY ./app ./app: Copies the actual application code.
• EXPOSE: It lets Docker know that the container listens on port 8000 at runtime.
• CMD: The command that runs when the container starts. The –host 0.0.0.0 flag is crucial to make the server accessible from outside the container.

Step 2: Build the Docker Image

Go to the directory that contains your Dockerfile and run the docker build command. The -t flag tags your image with a name and version.

docker build -t user-service:1.0.0 .

This command tells Docker to build an image named user-service with the tag 1.0.0 using the current directory (.) as the build context.

Step 3: Run the Container

Once the image is ready, you can run it as an isolated container. The -p flag publishes the container’s internal port 8000 to your machine’s port 8000.

docker run -d -p 8000:8000 --name user-service-container user-service:1.0.0

• -d: Runs the container in detached mode (in the background).
• -p 8000:8000: Maps <host-port>:<container-port>.
• –name: Assigns a custom name to the running container to simplify the management.

Your microservice is now running inside a Docker container and can be accessed at http://localhost:8000.

Step 4: Orchestrate with Docker Compose

While docker run works for a single service, microservices require multiple containers to work together. Docker Compose is a useful tool to define and run multi-container applications.

Create a docker-compose.yml file to define your entire ecosystem:

version: '3.8'

services:
  user-service:
    build: ./user-service  # Path to the service's directory containing the Dockerfile
    ports:
      - "8001:8000"
    environment:
      - ENV=production
    networks:
      - my-microservice-network

  order-service:
    build: ./order-service
    ports:
      - "8002:8000"
    environment:
      - USER_SERVICE_URL=http://user-service:8001
    networks:
      - my-microservice-network
    depends_on:
      - user-service

  # Example of including a message broker
  rabbitmq:
    image: "rabbitmq:3-management"
    ports:
      - "15672:15672"  # Management UI
    networks:
      - my-microservice-network

networks:
  my-microservice-network:
    driver: bridge

Key Features of This Compose File:

• Services: Defines each microservice and its configuration.
• Build Context: The build: key tells Compose where to find the Dockerfile for each service.
• Networking: The networks key places all services on a custom network. This allows them to communicate using their service name as a hostname (e.g., order-service can call http://user-service:8001).
• Dependencies: The depends_on key ensures user-service starts before order-service.

To start all your services with their dependencies, run:

docker compose up -d

Authentication & Authorization in Python Microservices

Authentication (AuthN) verifies a user’s identity, and Authorization (AuthZ) determines what they are allowed to do. Implementing these in microservices requires a centralized, standardized strategy to avoid code duplication and security gaps across services.

The most robust and scalable pattern is to use a centralized Identity Provider (IdP) and API Gateway. JSON Web Tokens (JWTs) are used as the credential.

How Does the JWT Pattern Work?

Here’s how JWT processes the authentication requests.

1. A dedicated Auth Service acts as the Identity Provider. It handles login, user registration, and token issuance.
2. A user logs in with credentials (e.g., username/password) against the Auth Service.
3. The Auth Service responds with a signed JWT (access token) containing claims (e.g., user_id, roles, permissions).
4. The client includes this JWT in the Authorization: Bearer <token> header of every request to any microservice.
5. Each microservice independently verifies the JWT’s signature and validates its claims to grant or deny access.

No central call to the Auth Service is needed for each request, making the system highly scalable.

Step 1: Create the Central Auth Service

Use FastAPI with python-jose to handle JWT creation and verification.

pip install fastapi uvicorn python-jose[cryptography] passlib[bcrypt]
# auth_service/main.py
from fastapi import FastAPI, HTTPException, Depends
from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
from jose import JWTError, jwt
from passlib.context import CryptContext
from datetime import datetime, timedelta
from pydantic import BaseModel

# Configuration (Move to environment variables in production!)
SECRET_KEY = "your-secret-key"  # Use a strong, random key!
ALGORITHM = "HS256"
ACCESS_TOKEN_EXPIRE_MINUTES = 30

app = FastAPI()
pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")

# Fake user database for demonstration
fake_users_db = {
    "johndoe": {
        "username": "johndoe",
        "hashed_password": pwd_context.hash("secret"),
        "role": "admin",
    }
}

class Token(BaseModel):
    access_token: str
    token_type: str

def verify_password(plain_password, hashed_password):
    return pwd_context.verify(plain_password, hashed_password)

def create_access_token(data: dict):
    to_encode = data.copy()
    expire = datetime.utcnow() + timedelta(minutes=ACCESS_TOKEN_EXPIRE_MINUTES)
    to_encode.update({"exp": expire})
    encoded_jwt = jwt.encode(to_encode, SECRET_KEY, algorithm=ALGORITHM)
    return encoded_jwt

@app.post("/token", response_model=Token)
async def login_for_access_token(form_data: OAuth2PasswordRequestForm = Depends()):
    # Check if user exists and password is correct
    user = fake_users_db.get(form_data.username)
    if not user or not verify_password(form_data.password, user["hashed_password"]):
        raise HTTPException(status_code=401, detail="Incorrect username or password")
    
    # Create JWT with user data (subject & role)
    access_token = create_access_token(
        data={"sub": user["username"], "role": user["role"]}
    )
    return {"access_token": access_token, "token_type": "bearer"}

Step 2: Protect Your Microservices

Each downstream microservice (e.g., order-service) needs a dependency to verify incoming JWTs.

pip install python-jose[cryptography] fastapi
# order_service/app/main.py
from fastapi import FastAPI, Depends, HTTPException, status
from fastapi.security import OAuth2PasswordBearer
from jose import JWTError, jwt
from pydantic import BaseModel

# MUST use the same SECRET_KEY and ALGORITHM as the Auth Service
SECRET_KEY = "your-secret-key"
ALGORITHM = "HS256"

oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token") # Points to auth service URL

app = FastAPI()

class User(BaseModel):
    username: str
    role: str

async def get_current_user(token: str = Depends(oauth2_scheme)):
    credentials_exception = HTTPException(
        status_code=status.HTTP_401_UNAUTHORIZED,
        detail="Could not validate credentials",
        headers={"WWW-Authenticate": "Bearer"},
    )
    try:
        # Decode and verify the JWT
        payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
        username: str = payload.get("sub")
        role: str = payload.get("role")
        if username is None:
            raise credentials_exception
        return User(username=username, role=role)
    except JWTError:
        raise credentials_exception

# Authorization Dependency: Check for required role
def require_role(required_role: str):
    def role_checker(current_user: User = Depends(get_current_user)):
        if current_user.role != required_role:
            raise HTTPException(status_code=403, detail="Insufficient permissions")
        return current_user
    return role_checker

@app.get("/admin-dashboard")
async def read_admin_dashboard(user: User = Depends(require_role("admin"))):
    return {"message": f"Welcome to the admin dashboard, {user.username}"}

@app.get("/orders/")
async def read_orders(user: User = Depends(get_current_user)): # AuthN only
    return {"message": f"Here are your orders, {user.username}"}

For the best results, use an API gateway, centralize secret management, use asymmetric cryptography, and add token revocation.

Deploying Python Microservices

Deploying microservices requires a shift from running single containers to orchestrating multiple, interconnected services reliably and at scale. The de facto standard for this is Kubernetes.

You have already prepared the microservices and containerized them. We move further.

Step 1: Define Your Application With Kubernetes Manifests

Kubernetes requires YAML files (manifests) to define your application’s desired state. You typically need four key components for each service:

Deployment (deployment.yaml)

It defines the blueprint for your application pod (a group of containers).

apiVersion: apps/v1
kind: Deployment
metadata:
  name: user-service
spec:
  replicas: 3 # Run 3 identical instances (pods)
  selector:
    matchLabels:
      app: user-service
  template:
    metadata:
      labels:
        app: user-service
    spec:
      containers:
      - name: user-service
        image: your-registry/user-service:1.0.0 # Your container image
        ports:
        - containerPort: 8000
        env:
        - name: DATABASE_URL # Inject config via env vars
          valueFrom:
            secretKeyRef:
              name: app-secrets
              key: database-url
---

Service (service.yaml)

Creates a stable network endpoint to access the pods, enabling internal service discovery.

apiVersion: v1
kind: Service
metadata:
  name: user-service
spec:
  selector:
    app: user-service
  ports:
    - protocol: TCP
      port: 80 # Port the service listens on
      targetPort: 8000 # Port the container listens on
  # type: LoadBalancer # Uncomment for public access (not typical for internal services)
---

Ingress (ingress.yaml)

Acts as a smart router, managing external HTTP/S traffic into your cluster and routing it to the correct internal service based on paths or domains. This is your API Gateway in Kubernetes.

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: main-ingress
  annotations:
    kubernetes.io/ingress.class: "nginx"
spec:
  rules:
  - host: api.myapp.com
    http:
      paths:
      - path: /users
        pathType: Prefix
        backend:
          service:
            name: user-service
            port:
              number: 80
      - path: /orders
        pathType: Prefix
        backend:
          service:
            name: order-service
            port:
              number: 80
---

ConfigMap and Secret

Store non-confidential configuration data in ConfigMaps and sensitive data like passwords and API keys in Secrets. These are then mounted as environment variables or files into your pods.

# Example Secret (data is base64 encoded)
apiVersion: v1
kind: Secret
metadata:
  name: app-secrets
type: Opaque
data:
  database-url: dXJsLWVuY29kZWQtaW4tYmFzZTY0
  secret-key: YW5vdGhlci1iYXNlNjQtdmFsdWU=

Step 2: Choose a Cloud Provider

Select a managed Kubernetes service like Google Kubernetes Engine (GKE), Azure Kubernetes Service (AKS) or Amazon EKS. This eliminates the complexity of managing the control plane.

Step 3: Set Up a Container Registry

Use Google Container Registry (GCR), Amazon ECR, or Docker Hub to store your built Docker images. Your Kubernetes cluster will pull images from here.

Step 3: Build and Push Images

Automate the process of building your Docker images and pushing them to your registry with a unique tag (e.g., a Git commit SHA).

docker build -t gcr.io/my-project/user-service:$(git rev-parse --short HEAD) .
docker push gcr.io/my-project/user-service:$(git rev-parse --short HEAD)

Step 4: Apply Your Manifests

Use kubectl apply -f <directory-with-manifests> to deploy your application to the Kubernetes cluster. The cluster’s controllers will work to match the actual state to the state you defined in your YAML files.

Step 5: Automate with CI/CD

This entire process: testing, building, pushing images, and deploying manifests should be automated using a CI/CD pipeline (e.g., GitHub Actions, GitLab CI, Jenkins).

If you want help with the development and containerization of microservices, hire Python developers with us.

Best Practices for Python Microservices

Let’s discuss the practices critical for building a system that is robust, maintainable, and scalable, rather than a distributed monolith.

Design Around Business Domains

Structure each service around a specific business capability (e.g., “Payment Service,” “User Profile Service”), not technical layers.

This aligns team ownership with business goals and ensures services are loosely coupled and cohesive. Use the principles of DDD (Domain-Driven Design) to define bounded contexts.

Ensure Loose Coupling and High Cohesion

Services must be scalable and independently deployable. They should communicate through well-defined APIs or asynchronous events, not shared databases or tight, synchronous calls. A modification in one service should not require changes in another.

Implement API-First Design

Define the API contract for a service first, before writing any code. Use OpenAPI (Swagger) specifications to create a clear, versioned contract that all teams agree upon.

This way, the frontend and backend teams can work in parallel and simplify the integration.

Centralize Observability

Implement the three pillars of observability from the start: centralized logging (e.g., ELK Stack), aggregated metrics (e.g., Prometheus/Grafana), and distributed tracing (e.g., Jaeger, Zipkin).

This is non-negotiable for debugging distributed systems.

Secure Services Independently

Assume the network is hostile. Every service must independently validate credentials. Use a centralized identity provider to issue JWTs, and have each service verify those tokens. Never trust internal traffic by default.

Automate Everything

Manual deployment and management of dozens of services is impossible. Use Infrastructure as Code (IaC) for provisioning and CI/CD for automated testing, containerization, and deployment. This ensures consistency and enables rapid, reliable releases.

Use Lightweight Frameworks

Choose frameworks designed for microservices. FastAPI is the modern leader for its performance, async support, and automatic API docs. Flask is a simpler alternative.

Avoid monolithic, “batteries-included” frameworks like Django for simple services unless you need their full ORM and admin panel.

Decentralize Data Management

The biggest coupling anti-pattern is a shared database. Each service must own its data and database. Communicate data changes only via public APIs or events. This allows each service to use the database technology best suited for its needs.

Let’s Summarize

A microservices architecture decomposes applications into discrete, purpose-driven services. So the developers have the autonomy to innovate, experiment, and deploy independently.

Python, with frameworks like FastAPI and Flask, provides a powerful and developer-friendly foundation for this journey. It simplifies the creation of robust APIs and efficient service communication. While the path introduces complexity in coordination and monitoring, the payoff in agility and maintainability is significant.

So, want to build the best microservices with Python? Then get our pro Python development services today!

FAQs on Microservices With Python