What is the main difference between FastAPI and Django?

FastAPI is a lightweight async API framework that provides routing, validation, and OpenAPI documentation generation with no ORM, admin, or auth included. Django is a full-stack web framework that ships with an ORM, authentication system, admin interface, form handling, and database migrations. FastAPI has 100k GitHub stars and 473.9M monthly PyPI downloads. Django has 88k stars and 34k forks representing a 20-year production ecosystem.

When should I choose FastAPI over Django?

Choose FastAPI when you are building a standalone API or microservice, your team writes async Python fluently, and you need auto-generated OpenAPI docs for external consumers. FastAPI is the better choice when you will deploy on Kubernetes and want a lean container with minimal overhead. It is not the right choice when you need auth, ORM, and admin out of the box, because you will spend 2 to 4 weeks building equivalents from separate libraries.

When should I choose Django over FastAPI?

Choose Django when you are building a product that needs user authentication, an admin panel, database migrations, and form handling before your first paying customer. Django's 34k forks and 20-year ecosystem mean mature packages exist for payments, social auth, multi-tenancy, and audit logging. Teams of 3 to 8 with mixed experience levels ship faster on Django because conventions prevent the architecture mistakes that FastAPI's blank canvas invites.

Is FastAPI or Django free to use?

Both are completely free and open source with no commercial tier. FastAPI is MIT licensed. Django is BSD 3-Clause licensed. Neither framework charges for usage, enterprise features, or scale. Your only costs are infrastructure: the compute, database, and bandwidth you choose from any hosting provider.

What is the biggest production gotcha with FastAPI?

FastAPI's Depends() dependency injection resolves graphs at import time, not at request time. When you have 3 or more levels of nested dependencies, pytest overrides do not propagate as expected, causing integration tests to pass locally and fail in CI for reasons that look like environment issues. Teams typically spend 2 to 3 weeks debugging this before identifying the root cause. The fix requires either flattening the dependency tree or building a custom override registry, and neither approach is documented in the official FastAPI docs.

How hard is it to migrate from Django to FastAPI?

Migrating a mid-sized Django application to FastAPI realistically takes 4 to 6 months of parallel development. You must rewrite the ORM layer, auth system, admin interface, and migration tooling from scratch because FastAPI ships none of these. Most teams that attempt this migration conclude partway through that the performance or architectural benefits did not justify the cost. Moving from FastAPI to Django is faster, typically 6 to 10 weeks, because you are adding structure rather than replacing it.

How does FastAPI perform compared to Django at scale?

In benchmark conditions on simple JSON endpoints, FastAPI on Uvicorn handles 40,000 to 60,000 requests per second per core. Django on Gunicorn typically handles 5,000 to 15,000 RPS under the same conditions. That gap narrows substantially once real database I/O is added, because Postgres becomes the bottleneck, not the framework. The performance difference matters most for WebSocket services, streaming endpoints, and high fan-out APIs, not for typical CRUD applications.

Which framework has a better ecosystem in 2026?

Django has the deeper ecosystem by a significant margin. Its 34k forks versus FastAPI's 9.5k forks reflect a 20-year history of production use and third-party package development. Mature Django packages for auth, payments, multi-tenancy, and audit logging have been running in production for over a decade. FastAPI's ecosystem is growing fast, as evidenced by 473.9M monthly downloads and 100k stars, but most packages have less collective production mileage than their Django counterparts.

Tech Duel

FastAPI vs Django

FastAPI is a VS Code-based AI editor with roughly 40,000 paying teams as of 2025, built around deep model integration with Claude, GPT-4o, and Gemini. Django, backed by Microsoft, surpassed 1.8 million paid subscribers in 2024 and is embedded natively in VS Code, JetBrains, Neovim, and Vim. The right pick depends on your team, timeline, and what you are building.

Last reviewed: July 2026

Quick verdict: FastAPI vs Django

Choose FastAPI you are building a greenfield API service, your team is comfortable with async Python and Pydantic, and you need to move fast without dragging along ORM and template overhead you will never use..

Choose Django you are a team of 3 to 8 building a product that needs auth, admin, ORM, and background tasks out of the box, or if you are migrating a monolith and cannot afford to stitch together 12 separate libraries..

Both frameworks are completely free and open source, but Django's 34k forks signal a decade of battle-tested extensions FastAPI simply does not have yet.

FastAPI vs Django Operational Complexity, Team Fit, and the Real Cost of Switching

Both tools are competitive for inline autocomplete, but they optimize for different use cases. Django's autocomplete typically responds in under 100ms and consistently tops developer surveys for suggestion quality on standard patterns. FastAPI's Tab completion is fast and adds real-time diff previews that show exactly which token is about to be inserted, giving more visual feedback.

Where FastAPI pulls ahead significantly is agentic workflows. Composer mode can ingest a prompt like "add OpenTelemetry tracing to every API handler" and generate coordinated diffs across 20 files simultaneously. GitHub's answer, Django Workspace, exists but requires navigating to github.com and is limited to narrower scopes as of mid-2025. For day-to-day refactors that span more than a handful of files, FastAPI is the stronger tool.

For standard single-file code generation, both tools produce similar quality results. GPT-4o and Claude 3.7 Sonnet power most FastAPI usage (see our OpenAI vs Anthropic comparison for how those underlying models differ); Django uses Microsoft's Codex-descendant models fine-tuned for latency. In head-to-head completions for Python, TypeScript, and Go, user benchmarks show roughly equivalent accuracy for everyday patterns.

If agentic multi-file editing is a hard requirement for your team, mention it when answering the questions below. It shifts the recommendation significantly.

Cursor vs Django: pricing, IDE support, and team adoption in 2025

Django is cheaper for individuals and teams. At $10/month Individual vs $20/month for FastAPI Pro, and $19/user/month for Django Business vs $40/user/month for FastAPI Business, the annual cost difference for a 10-person team is roughly $2,520. GitHub also offers a free tier for individual VS Code users (2,000 completions and 50 chat messages per month) and includes Django in its Team plan at a discount, making the real cost close to zero for teams already on a GitHub paid plan. FastAPI has a free tier too, but with more limited completions. For early-stage startups watching burn rate, that gap is not trivial.

IDE support strongly favors Django. It runs natively in VS Code, all major JetBrains IDEs (IntelliJ, PyCharm, WebStorm, Rider, GoLand), Neovim, and Eclipse. FastAPI is a VS Code fork: VS Code extensions work, but JetBrains users must either abandon their IDE or go without FastAPI. For polyglot shops where Java developers use IntelliJ and TypeScript developers use VS Code, Django is often the only option that serves everyone without forcing an IDE switch.

FastAPI's adoption is concentrated in startups and AI-native teams who want to move fast. Django's GitHub brand, Microsoft distribution, and broad IDE coverage make it the default choice at enterprise scale. Over 50,000 organizations used Django as of late 2024, with FastAPI growing rapidly but still concentrated in smaller engineering teams.

IDE diversity across your team is often the deciding factor. If your team is not all on VS Code, Django may be the only viable option that works for everyone.

Production gotcha: what nobody tells you

FastAPI's dependency injection system is elegant on day one and a quiet disaster by month six. When you compose more than three or four levels of nested Depends() calls, pytest isolation breaks in non-obvious ways: a dependency declared at the router level silently overrides a test-level override because FastAPI resolves the dependency graph at import time, not at request time. Teams discover this when their integration tests pass locally but fail in CI because a database session fixture is not actually being injected where they think it is. There is no warning, no error, and nothing in the official docs that explains the resolution order clearly. You end up either flattening your dependency tree (losing the modularity you chose FastAPI for) or writing a custom dependency override registry that you maintain yourself.

Cursor vs Django: workflow fit, learning curve, and switching costs

Django integrates into your existing IDE without disrupting your workflow. Install the plugin, authenticate with GitHub, and autocomplete starts working within minutes. There is no new editor to learn and no mental model to shift. For teams with established workflows and tight schedules, this near-zero activation energy is a genuine advantage.

FastAPI asks you to adopt a new editor. For VS Code users, the migration is essentially painless: extensions, keybindings, and settings.json all transfer. For JetBrains or Neovim teams, FastAPI is a non-starter without a full IDE switch. The upside for VS Code switchers is that FastAPI's AI features are architecturally deeper: Chat, Composer, inline edit, and codebase search all work at a level Django's plugin architecture cannot match without first-party IDE access.

Switching costs are asymmetric. Moving from Django to FastAPI for a VS Code team takes under an hour: install, migrate settings, done. Moving back is equally easy. For JetBrains teams considering FastAPI, the cost is high: developers must learn a new IDE, rebuild muscle memory, and may lose IDE-specific features (inspections, refactoring tools, debugger integrations) they rely on daily.

Your current IDE setup is the fastest filter. If your whole team is on VS Code and wants maximum AI leverage, FastAPI's edge is real. Otherwise, Django is more likely to stick across the full team.

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Common questions about Cursor vs Django

What is the real difference between FastAPI and Django for a team choosing today?

FastAPI gives you a fast, async API framework and nothing else. You own the decisions about auth, ORM, migrations, and admin. Django gives you all of those out of the box, battle-tested across 20 years and 34k forks, at the cost of more opinions and more overhead. The choice is not about which is better in the abstract. It is about whether your team has the experience to wire together a full stack from scratch (FastAPI) or needs a framework that makes those decisions for you (Django).

Can FastAPI replace Django for a full web application?

Technically yes, practically it is painful. FastAPI has no ORM, no auth system, no admin interface, no form handling, and no migration tooling. You can build all of these by adding SQLAlchemy, authlib, a custom admin, and Alembic, but you will spend 3 to 5 weeks on setup before writing a line of product code. Django has all of this working on day one. Use FastAPI for API-only services. Use Django when you are building a complete product.

What is the hidden cost of choosing FastAPI for a team with junior engineers?

FastAPI provides almost no architectural guardrails. Junior engineers will make structural decisions that FastAPI leaves open: how to organize dependencies, how to handle configuration, how to structure error responses, and whether to use async or sync database drivers. These decisions look small in month one and create painful refactors in month six. Django's conventions prevent most of these mistakes by encoding them into a structure the whole team inherits automatically.

Is 473.9M monthly downloads a sign that FastAPI is the right choice?

Download counts reflect adoption velocity, not production maturity. FastAPI's 473.9M monthly downloads show it has become the default choice for new Python API projects, which means more people are using it, more blog posts exist, and the ecosystem is expanding. It does not mean every team using it is using it correctly, and it does not mean the ecosystem has the same depth as Django's 20-year package history. High download numbers are a signal worth weighing alongside the specific requirements of your project.

Which framework is easier to hire for in 2026?

Django remains easier to hire for at the senior level because a larger pool of experienced Python engineers have shipped Django in production over the past 20 years. FastAPI experience is increasingly common on resumes in 2026, particularly among engineers who have worked on data services, ML APIs, or microservices in the past 3 years. For junior and mid-level hires, FastAPI experience is becoming a differentiator in interviews rather than a baseline expectation, which means you may need to budget for internal training if you go that route.

What is the best AI coding assistant for JetBrains users?

Django is the strongest option for JetBrains IDEs (IntelliJ, PyCharm, WebStorm, Rider, GoLand) — it has a native plugin and a free tier for individuals. FastAPI does not support JetBrains at all; you would need to switch editors entirely. JetBrains AI Pro is also worth evaluating as it is built directly into every JetBrains IDE and starts at roughly $10/month.