Flask: The Mature Micro-Framework

Created in 2010 by Armin Ronacher, Flask follows a “micro” design philosophy that provides the essential components for web development while allowing developers to choose additional extensions as needed. Now 15 years into its development, Flask has matured into a stable, well-documented framework with an extensive ecosystem.

Flask’s minimalist core focuses on simplicity and flexibility, making it ideal for projects that require customization and straightforward implementation. The framework’s design philosophy embraces the concept of “batteries included but removable,” giving developers the freedom to structure their applications according to project requirements.

In 2025, Flask continues to excel in general web development scenarios, especially for projects where a full-featured framework would be overkill but a custom solution would require too much development time.

FastAPI: The Modern Async Powerhouse

FastAPI represents a newer approach to web framework design, built specifically for creating high-performance APIs with native async support. Leveraging Python’s modern features, particularly type hints, FastAPI provides automatic data validation, serialization, and documentation generation.

Since its introduction, FastAPI has positioned itself as a framework designed for speed—both in terms of runtime performance and developer productivity. Its creator, Sebastián Ramírez, built FastAPI to address the limitations of existing frameworks when developing modern, high-performance APIs.

By 2025, FastAPI has evolved beyond its initial focus to include more comprehensive features while maintaining its performance advantages. The framework has expanded its scope to include better support for various databases, authentication systems, and deployment options, making it a more complete solution for web development.

Flask (Synchronous)

@app.route('/data')
def get_external_data():
    # This blocks the entire worker while waiting
    response = requests.get("https://api.example.com/data")
    data = response.json()
    # Additional processing
    processed_data = process_data(data)
    return jsonify(processed_data)
        

In this Flask example, the worker thread is blocked during the entire HTTP request, preventing it from handling other incoming requests until the current one completes. This synchronous behavior can become problematic when dealing with slow external services or multiple concurrent users.

FastAPI (Asynchronous)

@app.get('/data')
async def get_external_data():
    async with httpx.AsyncClient() as client:
        # This doesn't block - other requests can be processed
        response = await client.get("https://api.example.com/data")
        data = response.json()
        # Additional processing (ideally also async)
        processed_data = await process_data_async(data)
        return processed_data
        

FastAPI’s asynchronous design allows the server to handle multiple requests concurrently. When an API call is awaiting a response, the server can process other requests instead of sitting idle. This non-blocking approach significantly improves throughput and resource utilization, especially in I/O-bound applications.

Flask REST Implementation

from flask import Flask, jsonify, request

app = Flask(__name__)

# Sample data
items = [
    {"id": 1, "name": "Item 1"},
    {"id": 2, "name": "Item 2"}
]

@app.route('/items', methods=['GET'])
def get_items():
    return jsonify(items)

@app.route('/items', methods=['POST'])
def add_item():
    new_item = request.get_json()
    items.append(new_item)
    return '', 204

@app.route('/items/', methods=['GET'])
def get_item(item_id):
    item = next((item for item in items if item["id"] == item_id), None)
    if item:
        return jsonify(item)
    return jsonify({"error": "Item not found"}), 404
        

Flask’s approach requires manual handling of request parsing, validation, and response formatting. While this provides flexibility, it also places more responsibility on the developer to ensure proper implementation.

FastAPI REST Implementation

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

app = FastAPI()

# Data model with validation
class Item(BaseModel):
    id: int
    name: str

# Sample data
items = [
    Item(id=1, name="Item 1"),
    Item(id=2, name="Item 2")
]

@app.get("/items")
async def get_items():
    return items

@app.post("/items", status_code=204)
async def add_item(item: Item):
    items.append(item)
    return None

@app.get("/items/{item_id}")
async def get_item(item_id: int):
    item = next((item for item in items if item.id == item_id), None)
    if not item:
        raise HTTPException(status_code=404, detail="Item not found")
    return item
        

FastAPI’s implementation leverages Python type hints and Pydantic models for automatic request validation, serialization, and documentation. This approach reduces boilerplate code and potential bugs while improving code readability and maintainability.

FastAPI Documentation

• Automatically generates OpenAPI documentation
• Provides interactive Swagger UI at /docs endpoint
• Alternative ReDoc interface at /redoc endpoint
• Documentation derived from Python type hints and function docstrings
• Automatic schema generation from Pydantic models
• Support for OAuth2 and other authentication in docs
• No additional configuration required for basic documentation

Key Benefit: Zero-effort API documentation that stays in sync with your code, reducing the documentation maintenance burden and ensuring that documentation accurately reflects implementation.

Flask Documentation

• Requires additional libraries like Flasgger or APIFlask to generate OpenAPI documentation
• Documentation must be manually defined in docstrings or separate YAML files
• More setup and configuration required
• Documentation can become outdated if not manually updated with code changes
• Third-party extensions have improved but still require more effort than FastAPI’s built-in solution

Example with Flasgger:

from flask import Flask
from flasgger import Swagger

app = Flask(__name__)
swagger = Swagger(app)

@app.route('/items/')
def get_item(id):
    """
    Get a specific item
    ---
    parameters:
      - name: id
        in: path
        type: integer
        required: true
    responses:
      200:
        description: Item found
      404:
        description: Item not found
    """
    # Implementation
    return {"id": id, "name": "Example Item"}
        

FastAPI Enterprise Adoption

• Netflix: Uses FastAPI for its high-performance streaming services, leveraging the asynchronous architecture for real-time updates and personalized content delivery. The framework’s ability to handle thousands of concurrent connections has been crucial for their recommendation systems.
• Uber: Employs FastAPI to streamline web development processes and maximize backend reliability, taking advantage of its native support for asynchronous programming for high concurrency and real-time interactions in their ride-sharing platform.
• Microsoft: Integrates FastAPI with Azure Functions, allowing developers to leverage the framework within Microsoft’s cloud ecosystem for serverless applications and AI services.
• Fintech Sector: Multiple financial technology companies have adopted FastAPI for trading platforms and payment processing systems, where milliseconds of latency can have significant business impact.

Flask Enterprise Adoption

Flask continues to be widely used for internal tools, admin interfaces, and content management systems at many large organizations. Its simplicity and flexibility make it well-suited for projects where API performance is not the primary concern.

Educational institutions and research organizations particularly favor Flask for its gentle learning curve and extensive documentation, making it ideal for teaching web development concepts and building research-oriented applications.

Flask’s maturity and stability also make it a common choice for organizations with established codebases that prioritize reliability and maintainability over cutting-edge performance.

When to Choose Flask in 2025

• General-purpose web applications with traditional rendering
• Content management systems and admin interfaces
• Educational projects and teaching environments
• Applications requiring extensive customization
• Projects where developer familiarity with Flask exceeds FastAPI
• Legacy system maintenance and gradual modernization
• Simple web applications where performance is not critical
• Projects that prioritize simplicity and readability

When to Choose FastAPI in 2025

• API-first and microservices development
• High-performance applications with concurrent processing needs
• Real-time data streaming and processing
• Machine learning model serving and AI applications
• Projects requiring comprehensive API documentation
• Modern cloud-native applications leveraging containerization
• Systems processing many simultaneous connections
• Applications where type safety and validation are priorities
• Projects starting with modern Python versions (3.7+)

Flask Learning Resources

• Official Documentation: Comprehensive and continually updated with clear examples and best practices
• Flask Mega-Tutorial 2025 Edition: Miguel Grinberg’s definitive guide, updated for modern Flask usage
• Books: “Flask Web Development, 3rd Edition” and “Flask: Building Python Web Services” offer deep dives into the framework
• Online Courses: Multiple high-quality courses on platforms like Udemy, Coursera, and Pluralsight covering Flask with modern tooling
• Community Resources: Extensive collection of blog posts, Stack Overflow answers, and GitHub examples

FastAPI Learning Resources

• Official Documentation: Detailed and interactive with extensive examples and explanations
• FastAPI Best Practices: Community-maintained repository of patterns and anti-patterns
• Books: “Building Data Science Applications with FastAPI” and “FastAPI: Modern Python Web Development”
• Udemy’s “FastAPI – The Complete Course 2025”: Comprehensive course covering beginner to advanced topics
• YouTube Series: Multiple tutorial series from Python influencers covering real-world applications
• GitHub Examples: Growing collection of sample applications and implementation patterns

Which framework is better for beginners in 2025?

Flask has traditionally been considered more beginner-friendly due to its simplicity and extensive documentation. However, FastAPI’s improved documentation and growing educational resources have narrowed this gap. Flask remains slightly easier for complete beginners due to its simpler concepts and extensive tutorials. However, developers with some Python experience might find FastAPI’s structured approach and automatic validation features more intuitive for building robust applications from the start.

How have database integrations evolved for each framework?

Flask still relies primarily on extensions like Flask-SQLAlchemy for database integration. FastAPI has matured in this area with better support for SQLAlchemy 2.0, asyncio-compatible ORMs like SQLModel, and established patterns for common database operations. FastAPI’s async capabilities give it an edge for high-load database operations, while Flask’s approach may be more familiar to developers with traditional database experience.

How do the frameworks handle AI integration in 2025?

FastAPI has gained a significant advantage for AI model serving due to its performance characteristics and built-in validation. Many machine learning libraries now provide FastAPI integration examples, and its async capabilities work well for handling concurrent model inference. Flask can certainly work with AI models but generally requires more custom code to achieve the same functionality and performance, particularly for real-time prediction services.

What about cloud deployment options in 2025?

Both frameworks deploy well to modern cloud environments, but FastAPI has gained more native support from cloud providers. Major platforms like AWS, Google Cloud, and Azure now offer optimized FastAPI deployment options, while Flask continues to work well with traditional deployment patterns. FastAPI’s lower resource utilization also translates to cost savings in pay-per-use cloud environments, making it increasingly attractive for cloud-native applications.