Python Web Framework Comparison: Django, Flask, FastAPI and Rising Options

Django is a strong starting point for database-backed web apps with an integrated admin; Flask fits a small, custom WSGI stack; FastAPI fits typed, API-first services. For Python developers and technical founders, this choice affects integration work, maintenance and how a backend handles concurrent requests. Choosing the wrong fit can mean rebuilding authentication, database and deployment plumbing that another framework already supplies. This comparison covers architecture, practical tradeoffs, dated PyPI download changes and GitHub star snapshots so you can shortlist options before building a prototype.

Django vs Flask vs FastAPI (with Litestar, Sanic, Starlette): the quick verdict for Python web projects

Choosing the right Python web framework in 2026 comes down to what you are building, how you handle concurrency, and how much you want out of the box. Django is the full stack development framework that ships with an ORM, migrations, admin interface, templating engine, and user authentication - all wired together for relational, database-driven web applications. Flask is the lightweight micro framework that gives you routing, request handling, and Jinja templates, then steps aside so you can bolt on only the libraries you need. FastAPI is the async-first option built for building web APIs with standard Python type hints, automatic request validation, and OpenAPI documentation generated from your code.

Beyond the big three, two rising async alternatives deserve evaluation. Litestar is a community-driven ASGI framework offering dependency injection, OpenAPI generation, and support for multiple model types including Pydantic, dataclasses, and msgspec. Sanic is both an async web framework and its own high performance production server with TLS, WebSocket, and lifecycle-hook support. Starlette, meanwhile, is the underlying ASGI toolkit that FastAPI itself is built on - minimal by design, it handles routing, middleware, and background tasks, but most developers encounter it indirectly rather than choosing it as a standalone solution.

This article is written for Python developers and technical founders deciding how to build or modernize a backend in 2026. Later sections include dated PyPI download windows and GitHub star snapshots as partial momentum signals. These are not measures of usage, revenue, or quality - they are proxies for how often packages are pulled into environments and how much developer attention a repository attracts. Tools like RiseFinder can help you cross-check such public signals across many projects before committing.

Framework choice affects the integration work, maintenance and operational complexity your team will carry. Use architectural fit first, then the dated evidence below to decide which options deserve a prototype.

The short answer: Choose Django for CRUD-style, admin-heavy, relational web apps where rapid development matters. Choose Flask for small services, prototypes, or custom stacks where you want maximum flexibility. Choose FastAPI for API-first, async-friendly backends that need typed contracts and auto-generated API documentation. Evaluate Litestar when you want FastAPI-like ergonomics with a different plugin architecture, and Sanic when you want a built-in async server with lifecycle hooks. Use Starlette when you need a lean ASGI foundation - knowing you likely already have it if you use FastAPI.

What counts as a "Python web framework" in this comparison?

For this comparison, "Python web development frameworks" means server-side Python frameworks and toolkits that help build HTTP APIs and web applications. Client-side UI toolkits, front-end JavaScript frameworks, and static site generators are excluded.

The frameworks fall into three categories:

• Full stack frameworks like Django, which bundle ORM, admin, templating, auth, and migrations into one integrated package for full stack web apps.

• Microframeworks like Flask, which provide a minimal core - routing, request/response, template support - and leave ORM, auth, and admin to extensions and custom components.

• Async-first ASGI frameworks and toolkits like FastAPI, Litestar, Sanic, and Starlette, which support asynchronous I/O. Validation, schema generation and other built-ins vary by framework.

These six open-source projects provide different levels of application structure. Before adopting one, check its current release, supported Python versions and compatibility with your selected dependencies.

At a glance: when to reach for Django, Flask, FastAPI, Litestar, Sanic or Starlette

Before diving into details, here is the cheat-sheet overview. The table below summarizes each framework's sweet spot, stack type, interface, and typical learning curve.

The main split: Django is the convention-heavy full stack option. Flask is the minimalist foundation. FastAPI, Litestar, and Sanic are async, API-centric, and increasingly popular for backend development where concurrency matters. Starlette is a lower-level building block that many teams already use indirectly through FastAPI.

Methodology: how we're comparing Python web frameworks (and what we're not claiming)

This comparison combines official framework documentation, dated PyPI download events, a GitHub star snapshot, architectural differences (WSGI vs ASGI, sync vs async), and typical project fit.

Key caveats on the data:

• PyPI downloads are download events from the "without_mirrors" dataset, not unique developers, active users, websites, or deployments. CI pipelines, dependency resolution, and reinstalls all contribute - even after excluding known mirrors.

• GitHub stars express interest and mindshare, not production adoption or business outcomes. Only a single snapshot (25 October 2026, ~04:11 UTC) is provided; no historical baseline is available in this article, so no star growth percentages or "fastest-growing on GitHub" claims are made.

• No speed benchmark or "fastest framework" claim is made. Meaningful performance comparisons require controlled, workload-specific testing that is beyond this article's scope.

Data sources:

• PyPI Stats for downloads: pypistats.org or the /API endpoint for each package.

• Official GitHub repositories for star counts.

• Primary documentation for features and behavior: Django, Flask, FastAPI, Litestar, Sanic, Starlette.

RiseFinder is not the origin of these PyPI or GitHub measurements. Instead, RiseFinder helps readers organize and cross-reference such public signals across many Python projects - useful as a starting point to assemble evidence, not as a sole input for long-term technology bets.

Usage momentum: PyPI download trends for major Python web frameworks

Download events give one - but not the only - signal of how widely frameworks are being pulled into environments in a given period. The table below compares two non-overlapping 7-day UTC windows of "without_mirrors" download events.

Important context for reading this table:

• These are short-window snapshots, not long-term trends. A single week's movement does not establish a direction.

• Starlette is a dependency of FastAPI (and potentially other frameworks), so its download events can include transitive installs rather than independent project adoption. Do not add Starlette and FastAPI counts together or rank the largest number as "best."

• Higher downloads in this window do not necessarily mean "better" or "more widely deployed" in production.

The totals above sum seven daily observations from each package's PyPI Stats overall endpoint, using only without_mirrors. Percentage change is (latest − earlier) ÷ earlier × 100, rounded to two decimals. The full weeks end on 17 and 24 October 2026. PyPI Stats explains that its data derives from PyPI's public records and still includes CI downloads; excluding known mirrors does not turn events into people.

Interest signals: GitHub stars across Django, Flask, FastAPI, Litestar, Sanic and Starlette

GitHub stars are a coarse proxy for developer interest and mindshare, not actual deployments or production usage. Below is a single snapshot; no historical star baseline is available in this comparison, so no star growth percentages or "fastest-growing" claims are made.

FastAPI has more stars than Django in this snapshot. A single total does not establish which project gained stars faster, which has more deployments or which will suit your application.

Full-stack vs microframework vs async framework: architectural fit in 2026

Architecture - full-stack vs micro vs async-first - is typically the first decision driver when evaluating backend frameworks. It determines what you get out of the box, how much you need to assemble yourself, and how your application handles concurrent HTTP requests.

Full-stack frameworks bundle everything needed for the complete application. Django is the canonical example: according to its official overview, it ships with a built-in ORM for defining data models, automatic migrations, a powerful admin interface for database administration, a templating engine, user authentication, forms, and security middleware. Django follows a model view template architecture and encourages rapid development of database-driven web applications. Django was first released as open-source in 2005 (source) and is still active - a testament to its maturity. Its integrated database and account-management features can fit social or e-commerce applications; assess the actual requirements of your product.

Microframeworks keep the core small. Flask, per its documentation, is a lightweight WSGI web application framework designed to scale from small projects to complex applications. It provides routing, request/response handling, and template support via Jinja, but does not ship with a built-in ORM, admin panel, or auth system. Flask was released as an open-source project in 2010 (first public preview on 16 April 2010) and is known for flexibility and customization. Flask supports RESTful request dispatching and unit testing; its small core leaves application architecture choices to your team.

Async-first ASGI frameworks and toolkits are designed around non-blocking I/O and asynchronous programming:

• FastAPI builds on Starlette and Pydantic, using standard Python type hints for request validation, automatic OpenAPI schema generation, and support for both synchronous and asynchronous path-operation functions. FastAPI was first released in 2018 (PyPI history) and focuses on performance and building APIs. Its typed request contracts and async support can fit data-oriented APIs; benchmark the workload rather than assuming a performance advantage.

• Litestar is a community-driven ASGI framework supporting dependency injection, OpenAPI generation, typed models (Pydantic, dataclasses, msgspec), middleware, and plugins.

• Sanic ships its own high performance production-ready server with TLS support, WebSocket handling, lifecycle hooks, signals, and customizable error handling. Official Sanic Extensions add CORS, Jinja templates, dependency injection, OpenAPI docs, validation, and health monitoring.

• Starlette is the minimal ASGI toolkit - routing, middleware, background tasks, WebSocket support - often used as a foundation rather than a standalone framework.

Who wins where:

• Django wins when you want one integrated stack for relational, admin-backed web apps with built in tools for the entire development process.

• Flask wins for maximum component choice, small projects, and very lean services where you assemble only what you need.

• Async ASGI frameworks win when concurrency, typed APIs, and high performance APIs are first-class needs.

WSGI vs ASGI: what it means for Python web performance and concurrency

The web server gateway interface (WSGI) is the traditional synchronous interface that Django and Flask use. A WSGI request follows a straightforward path: request arrives, view processes it, response returns. Django and Flask are typically deployed via WSGI HTTP servers like Gunicorn or uWSGI.

ASGI (Asynchronous Server Gateway Interface) extends that model for non-blocking I/O, WebSockets, streaming, and long-lived connections. FastAPI, Litestar, and Starlette are ASGI-first. Sanic ships its own web server and can also run as an ASGI application with an ASGI server, as its deployment guide explains.

A critical caveat: async improves handling of concurrent I/O-bound workloads - many simultaneous external API calls, database queries, or WebSocket connections. It does not make CPU-bound code faster by itself. If your bottleneck is data processing, image manipulation, or ML inference, you still need worker pools or task offloading regardless of whether you use async code or sync views.

Django supports a native ASGI request stack, with caveats around synchronous middleware and operations. Flask's async views under WSGI still occupy a worker for each request; adding async def does not by itself increase the number of requests that deployment handles concurrently. Check library compatibility before mixing sync and async code.

• Prefer ASGI if you expect high traffic applications with lots of concurrent clients, WebSockets, streaming, or long-lived connections.

• WSGI stacks remain entirely viable for standard CRUD sites, content serving, and internal tools where per-request I/O is limited and the application structure is straightforward.

Decisive factor 1: project type and built-ins (admin, ORM, templating, validation)

What a framework gives you out of the box versus what you must assemble from extensions is usually the most important practical difference in the development process.

Django provides the deepest built-in feature set among these options. Its overview documentation describes an ORM that maps Python classes to database tables with automatic migrations, a ModelAdmin system that generates a complete admin interface for data access and database administration, a template system with inheritance and filters, and built-in auth with session management. Django supports rapid development with built-in tools and follows its pragmatic design philosophy across all these components. Full-stack frameworks provide tools for each layer of web development, and Django is the clearest example.

Flask ships without an ORM, admin, or auth system. According to Flask's documentation, it is designed to be quick to start with and capable of scaling to complex applications. Developers typically bring in SQLAlchemy for data access, Flask-Login for user authentication, Flask-Admin for an admin panel, and WTForms for validation. This makes Flask ideal for projects needing flexibility - you choose every component - but it means more integration work and decisions. A microframework can serve a small application or one component of a larger system, provided the team owns the surrounding integrations.

FastAPI lacks a built-in ORM or admin but excels at request validation and API documentation. Per its official site, it validates and converts request data using standard Python type hints, generates OpenAPI documentation with interactive Swagger UI and ReDoc, and provides editor support and testing features. FastAPI was first released in 2018 for building APIs with typed contracts.

Litestar, per its documentation, provides routing, dependency injection, security features, middleware, OpenAPI generation, and plugin support. It accepts data models defined as Pydantic, dataclasses, TypedDict, or msgspec structs. ORM support comes via plugins (SQLAlchemy, Piccolo).

Sanic, according to sanic.dev, includes its own web server, WebSocket support, lifecycle hooks, signals, and error handling. Official Sanic Extensions add optional Jinja templates, OpenAPI docs, validation, and dependency injection. ORM integration uses external async-compatible libraries.

Starlette is a minimal ASGI toolkit - routing, middleware, background tasks, WebSocket support - with no ORM, admin, or validation layer. It is often pulled in transitively through FastAPI rather than chosen directly.

Winner for "batteries-included database web apps": Django - because of its integrated ORM, admin interface, and templating engine. Micro and async frameworks trade this for flexibility, lighter cores, and different application structure patterns.

Decisive factor 2: API-first and async workloads (typed contracts, OpenAPI, WebSockets)

This factor matters most for teams building web APIs: public or partner APIs, microservices backends, or real-time streaming endpoints. The question is which framework makes building APIs with typed contracts, auto-generated documentation, and async support the smoothest part of the development process.

FastAPI is purpose-built for this. Its documentation shows how type-annotated path, query, and body parameters drive automatic request validation and OpenAPI schema generation, including interactive Swagger UI and ReDoc interfaces. FastAPI supports both def and async def routes; choose according to whether the called libraries block or expose awaitable I/O, as its async guide explains. WebSocket support comes via Starlette underneath. FastAPI focuses on performance and was designed for high-performance APIs from day one. Surveys indicate FastAPI's growing popularity among Python developers, particularly for API-centric work.

Litestar similarly supports typed models, OpenAPI generation, dependency injection, and async routes. Its docs highlight support for function-based and class-based controllers, MessagePack, and multiple serialization backends including msgspec for performance-sensitive workloads.

Sanic offers async routes, a built-in server, WebSocket support, and lifecycle hooks. OpenAPI documentation, validation, and CORS are available through Sanic Extensions. Sanic's async capabilities shine when you want a framework that is also your web server.

Starlette provides routing, background tasks, WebSocket handling, and middleware - the ASGI primitives that other frameworks like FastAPI build upon. Schema generation is not core, but Starlette handles the async plumbing well.

Django can serve APIs through add-ons like Django REST Framework or Django Ninja, which provide serialization, validation, and browsable API docs. Django's newer async views are improving, but the API layer remains an extra dependency on top of the high level framework.

Flask can produce APIs via Flask-RESTful, Flask-Smorest, or similar extensions, but has no integrated typed validation layer or OpenAPI generation by default. Python web frameworks simplify handling HTTP requests and routing URLs, but Flask leaves the API contract layer to your chosen extensions.

Winner for API-first, type-centric async services: FastAPI - with Litestar, Sanic, and Starlette as strong async alternatives when you want different ergonomics, plugin architecture, or lower-level control.

Decisive factor 3: learning curve, maturity and community ecosystem

Maturity and learning curve directly affect hiring, documentation quality, availability of tutorials and books, and the pool of reusable components and extensions. For technical founders, ecosystem depth translates to faster software development and lower onboarding costs.

Django was open-sourced in 2005 and is still active. More than twenty years of development provide substantial documentation and established application patterns. Review the packages your application would actually depend on rather than assuming every extension is maintained. Its integrated architecture offers established conventions for database-backed applications. The learning curve for basic CRUD apps is beginner-friendly thanks to strong conventions and built-in tools that guide the agile development experience.

Flask had its first public preview on 16 April 2010. Its minimalist core is easy to learn, and its large extension ecosystem covers most common needs. Its documentation covers both a quick start and larger application patterns. The core remains small, while you choose the additional components.

FastAPI was first released to PyPI in 2018. It is younger but has grown rapidly; its documentation is modern, well-structured, and covers testing, security, and deployment. FastAPI supports modern Python features like type hints, which also means developers benefit from strong editor support.

Litestar, Sanic and Starlette offer different async architectures and integration choices. Check current releases, dependency compatibility, issue handling and examples for your chosen database and authentication stack before treating any of them as a drop-in substitute.

The PSF/JetBrains 2024 Python Developers Survey reported in its web frameworks table: 38% FastAPI, 35% Django, 34% Flask among survey respondents. This reflects that survey's sample, not the entire Python universe or current 2026 rankings. Those dated survey results provide context for the three frameworks' prominence among respondents. They do not establish a universal popularity ranking or count deployed applications.

Winner for lowest friction onboarding and ecosystem depth in 2026: Django and Flask - with FastAPI rapidly closing the gap for API work. Newer async frameworks trade ecosystem size for more modern patterns and different architectural tradeoffs. Django, FastAPI, and Flask each cater to different architectural styles.

Decisive factor 4: deployment, scaling and operations

Deployment concerns matter early for technical founders: compatibility with popular HTTP servers, ease of containerization, observability, and background tasks all affect how quickly you move from prototype to production.

Django supports WSGI and native ASGI deployments. Flask is a WSGI framework; an ASGI adapter is a separate deployment choice with its own tradeoffs. Both containerize easily; many PaaS providers and cloud platforms support wsgi applications out of the box, including google app engine. Background workers are handled via Celery, RQ, or Huey - mature patterns for scheduling and data processing. Django includes a built-in test framework; Flask has well-documented testing support.

FastAPI, Litestar, and Starlette deploy with ASGI servers such as Uvicorn or Hypercorn. They are straightforward in Docker and Kubernetes environments, supporting horizontal scaling via multiple worker processes. FastAPI and Starlette include built-in background tasks primitives; Litestar provides lifecycle hooks and background task support. Testing support is well-documented across all three.

Sanic ships its own high performance web server with TLS support, auto-reloading, and application inspection tools. It can still be run behind reverse proxies or in containers. Sanic Extensions add health endpoints and monitoring capabilities. The built-in development server also serves as the production server, which simplifies the deployment stack.

Logging, middleware, and observability hooks are increasingly comprehensive across async frameworks. OpenTelemetry integration, structured logging, and metric collection are available for all six, though the sync/async split affects which libraries work seamlessly.

Operational choice: favor the framework whose deployment, dependency and observability requirements your team can support. Test a representative workload and failure path before treating documentation or popularity as evidence of operational fit.

How Django, Flask, FastAPI, Litestar, Sanic and Starlette feel in code

Each framework has a distinct code style. The following minimal excerpts illustrate a route that reads a path parameter and returns JSON. They need the relevant dependencies and project setup; they are not complete application templates or performance benchmarks.

Django (view + URLconf):

# views.py
from django.http import JsonResponse

def hello(request, name):
    return JsonResponse({"message": f"Hello, {name}"})

# urls.py
from django.urls import path
from . import views

urlpatterns = [
    path("hello/<str:name>/", views.hello),
]

Flask:

from flask import Flask, jsonify

app = Flask(__name__)

@app.route("/hello/<name>")
def hello(name):
    return jsonify(message=f"Hello, {name}")

FastAPI:

from fastapi import FastAPI

app = FastAPI()

@app.get("/hello/{name}")
def hello(name: str):
    return {"message": f"Hello, {name}"}

Litestar:

from litestar import Litestar, get

@get("/hello/{name:str}")
async def hello(name: str) -> dict:
    return {"message": f"Hello, {name}"}

app = Litestar([hello])

Sanic:

from sanic import Sanic, json

app = Sanic("MyApp")

@app.get("/hello/<name:str>")
async def hello(request, name: str):
    return json({"message": f"Hello, {name}"})

Starlette:

from starlette.applications import Starlette
from starlette.responses import JSONResponse
from starlette.routing import Route

async def hello(request):
    name = request.path_params["name"]
    return JSONResponse({"message": f"Hello, {name}"})

app = Starlette(routes=[Route("/hello/{name}", hello)])

Key observations:

• Decorator-heavy vs class-based vs explicit routing: Flask, FastAPI, Litestar, and Sanic use decorators. Django separates URL configuration from view logic. Starlette builds routes as explicit objects.

• Type annotations: FastAPI and Litestar rely on them for request validation and schema generation. The other frameworks use them optionally.

• Handler style: the Litestar, Sanic and Starlette excerpts use async handlers. FastAPI supports both sync and async functions, while Django and Flask also support async views subject to deployment and extension constraints.

If your team prefers explicit type contracts and auto-generated documentation, FastAPI or Litestar will feel natural. If you value convention and separation of concerns, Django's model view controller-adjacent structure (model view template, specifically) may suit better. If you want the shortest path from idea to running server side code, Flask's minimal style is hard to beat.

Which Python web framework should you choose?

There is no universal best Python web framework - the right choice depends on your project type, team skills, and operational requirements. Python web frameworks are primarily utilized in backend architectures, and the best Python framework for your situation is the one that aligns with what you are actually building.

• Choose Django if you're building:

  • Database-heavy SaaS, dashboards, CMS-like web apps, or e-commerce platforms.

  • Internal tools that benefit from the built-in admin interface and user authentication.

  • Full stack web apps where rapid development and convention over configuration save time.

• Choose Flask if you:

  • Want a small surface area and will assemble your own stack from preferred libraries.

  • Are deploying straightforward internal APIs, prototypes, or small projects that do not need a full admin.

  • Value the agile development experience of adding only what you need.

• Choose FastAPI if you:

  • Are building API-first backends or microservices.

  • Want async support, typed request/response models, and auto-generated OpenAPI docs.

  • Need high performance APIs for data-intensive or real-time workloads.

• Evaluate Litestar if you:

  • Prefer FastAPI-like typed APIs but want a different plugin architecture, dependency injection system, or msgspec serialization.

• Evaluate Sanic if you:

  • Value an async open source framework with a built-in high performance web server, TLS support, and lifecycle hooks.

• Use Starlette if you:

  • Want to compose your own ASGI toolkit or need a very lean foundation - and understand that many teams already get Starlette transitively through FastAPI.

For most teams in 2026: Django for full web applications with server side rendering and admin needs; FastAPI for API-first backend development; Flask when you explicitly want minimalism. Before committing, prototype a thin slice in one or two contenders - the time invested reveals friction points that no comparison article can fully predict.

How RiseFinder can help you track momentum in Python web frameworks

RiseFinder is a free starting point for researching framework and ecosystem momentum using real, dated signals from public sources.

Here is how you can use it concretely:

• Go to RiseFinder search and look up packages like "fastapi", "django", "litestar", "sanic", or related tooling by name.

• Open a project to see which sources it appears in (e.g., PyPI, GitHub) and historical movements where available. Coverage varies: if a package or repository is absent, inspect its primary source and do not treat the missing entry as zero activity.

Features worth knowing about:

• Daily "riser" lists aggregated from multiple sources surface notable changes across the tracked universe.

• You can save interesting projects locally in the browser without creating an account.

• Browse by categories tied to data sources - these are source-based categories, not Python-specific slices.

• An optional daily email surfaces notable changes across tracked projects.

Limitations to be aware of:

• No custom alerts, no public API, no traffic estimates or predictions.

• Coverage is a curated subset of packages, repositories, companies, and apps - not a complete universe.

• Public proxy metrics remain separate from evidence of users, customers or revenue.

If you are evaluating Python web development frameworks for a long-term bet, shortlist your candidates, then use RiseFinder to see dated evidence of momentum before committing.

Frequently asked questions about Python web frameworks in 2026

This section rapidly answers common selection and migration questions about Python web development.

Can I mix Django, Flask, FastAPI, Litestar, Sanic or Starlette in one architecture?

Yes, and it is common. Many teams run a legacy Django monolith for admin, server side rendering, and user-facing pages while deploying new FastAPI or Litestar microservices for API surfaces or high-concurrency endpoints. Interoperability works through HTTP (reverse proxies like Nginx routing to different backends), shared databases, message queues, or event buses.

That said, operational complexity grows with each framework added. Mixing should be intentional - driven by concrete needs like async capabilities for a specific service - not by a desire to use every new tool. Each additional framework means another set of deployment patterns, dependency trees, and on-call knowledge.

Is it worth rewriting a working Django or Flask app to FastAPI or another async framework?

Generally, no - not purely for hype or perceived performance without measured pain points. If your existing application handles its load and your team is productive, a rewrite introduces risk and cost.

Instead, profile actual bottlenecks: Is the database the constraint? External API latency? CPU-bound data processing? If concurrency on I/O-bound paths is the proven bottleneck, consider an incremental approach - extract the high-concurrency endpoints into a new async service alongside the existing monolith. This lets you adopt async code where it helps without risking a big-bang rewrite. Object-Relational Mapping and application structure patterns may need rethinking in the new service, but the rest of your system stays stable.

Which Python web framework is best for machine learning and data-heavy APIs?

Any of the major frameworks can serve ML models; the best Python framework depends more on your integration needs than on the ML stack itself. Consider:

• FastAPI or Litestar pair well with typed request/response schemas for ML-backed APIs, making it easy to validate input payloads and document expected outputs. FastAPI is ideal for data-intensive applications and real-time responses, and high-performance frameworks are optimized for APIs and asynchronous operations.

• Django is a strong choice when ML is one feature inside a broader product with admin, user management, and content - the full stack handles the complete application while your model serves predictions from a specific endpoint.

For heavy inference workloads, the framework's role is typically thin: receive the request, validate input, call the model (often via a separate process or service), and return the result. Async support helps when orchestrating multiple upstream/downstream calls during inference.

What about other Python web frameworks like Bottle, web2py or TurboGears?

• Bottle is a single-file WSGI micro framework with no dependencies beyond the Python standard library. It is a lightweight choice for minimal tasks; check its deployment and concurrency documentation against your requirements.

• web2py is a full-stack framework that includes a web interface for development and database administration. Check maintenance activity and community health carefully before selecting it for new builds.

• TurboGears composes components including SQLAlchemy and other frameworks into a full-stack solution. This article does not include a measured momentum comparison for TurboGears.

For any of these other frameworks, check official documentation, recent release dates, and community activity before choosing them for new projects. Full-stack frameworks provide end-to-end solutions for web application development, but only if actively maintained.

How does security compare across these Python web frameworks?

Security features in web frameworks protect against vulnerabilities like XSS and SQL injection, but the level of protection out of the box varies significantly.

• Django ships many protections by default: CSRF middleware, XSS prevention via auto-escaping in templates, clickjacking protection, secure session handling, and built-in user authentication. Many safe defaults are active without configuration.

• Flask, FastAPI, Litestar, Sanic, and Starlette offer security primitives and documentation, but often rely on correct configuration and chosen extensions. Flask provides safe templating via Jinja's auto-escaping, and built-in signed-cookie sessions. User authentication, CSRF protection and other application policies need deliberate implementation or suitable extensions. FastAPI and Litestar's type-based request validation helps prevent many input-validation issues, but authentication and authorization are plugged in separately.

Regardless of framework:

• Follow each framework's official security guide.

• Keep dependencies updated and run regular audits.

• Layer operational security - TLS, security headers, WAF, file uploads validation - on top of framework-level protections. Security is a combination of what the framework provides and how disciplined your team is in using it.