When I build an AI application, choosing the backend framework is an important decision.

There are several good options, but I usually look at 𝐅𝐚𝐬𝐭𝐀𝐏𝐈, 𝐃𝐣𝐚𝐧𝐠𝐨, and 𝐅𝐥𝐚𝐬𝐤 first.

The choice really depends on what I'm building.

✔️ 𝐅𝐚𝐬𝐭𝐀𝐏𝐈 makes a lot of sense when the application is mainly an AI/API backend. Since most AI tools I use are already in Python, I can keep the whole stack in one ecosystem, from LLMs and embeddings to document processing, RAG, databases, and the API itself.

✔️ 𝐃𝐣𝐚𝐧𝐠𝐨 is a strong choice when the AI functionality is part of a larger web application. Its built-in ORM, authentication, admin panel, and other features can save a lot of development time.

✔️ 𝐅𝐥𝐚𝐬𝐤 is still a great option when I want something simple, lightweight, and flexible, especially for smaller services or prototypes.

For an AI application, I also need to think beyond the framework:

✔️ Authentication

✔️ Database and data persistence

✔️ Document processing

✔️ Embeddings and vector search

✔️ RAG

✔️ Background tasks

✔️ Testing

✔️ Docker

✔️ Monitoring

✔️ Deployment

There isn't one framework that is "best" for every AI application. For the type of production AI backends I'm building, 𝐅𝐚𝐬𝐭𝐀𝐏𝐈 is often a practical choice because it provides a clean API layer while keeping everything close to the Python AI ecosystem.

The framework is only one piece of the puzzle.

Good architecture matters more than the framework you choose.


What do you normally use for AI applications: 𝐅𝐚𝐬𝐭𝐀𝐏𝐈, 𝐃𝐣𝐚𝐧𝐠𝐨, 𝐅𝐥𝐚𝐬𝐤, or something else?

Here is the roadmap to build an AI application with FastAPI: https://www.youtube.com/watch?v=0SLLG2Z_Htw


#FastAPI #Python #AIEngineering #GenerativeAI #RAG #BackendDevelopment #MachineLearning #Django #Flask #SoftwareArchitecture

The hardest part of building AI applications isn't writing the prompt or calling the model. In the last two weeks, I learned that keeping the backend from turning into spaghetti code once you move past the tutorial phase.

When you're wiring up an AI document pipeline in FastAPI, a few things quickly become non-negotiable:



• Payload Guardrails: If your Pydantic schemas aren't catching malformed JSON, missing nested fields, or bad Enums at the door, your AI service will fail unpredictably downstream.



• Route Isolation: Mixing your raw API endpoints with validation logic and business rules makes refactoring a nightmare by week three.



• The Persistence Gap: Transitioning from mock in-memory data structures to a real relational database and a vector store for RAG is where most clean prototypes start to break down.



If you're building production backends for AI and ML features, where do you usually draw the line between keeping things simple and over-engineering your architecture?



You can learn about FastAPI: https://www.youtube.com/playlist?list=PLQNCas8_eikM



#FastAPI #Python #BackendEngineering #SoftwareArchitecture #APIs #Pydantic #ArtificialIntelligence #MachineLearning #RAG

While moving from other web frameworks to 𝐅𝐚𝐬𝐭𝐀𝐏𝐈, one question came to my mind:



𝐖𝐡𝐲 𝐝𝐨 𝐈 𝐧𝐞𝐞𝐝 𝐀𝐏𝐈𝐑𝐨𝐮𝐭𝐞𝐫 𝐰𝐡𝐞𝐧 𝐅𝐚𝐬𝐭𝐀𝐏𝐈 𝐚𝐥𝐫𝐞𝐚𝐝𝐲 𝐥𝐞𝐭𝐬 𝐦𝐞 𝐝𝐞𝐟𝐢𝐧𝐞 𝐫𝐨𝐮𝐭𝐞𝐬 𝐝𝐢𝐫𝐞𝐜𝐭𝐥𝐲?



For a small project, this is enough:



@𝒂𝒑𝒑.𝒈𝒆𝒕("/𝒅𝒐𝒄𝒖𝒎𝒆𝒏𝒕𝒔")

𝒂𝒔𝒚𝒏𝒄 𝒅𝒆𝒇 𝒈𝒆𝒕_𝒅𝒐𝒄𝒖𝒎𝒆𝒏𝒕𝒔():

...

So why introduce APIRouter?



The answer became clearer when I started thinking about a real application.



As the project grows, I may have authentication, users, documents, search, AI processing, and many more endpoints.



Keeping all of these routes in one place can quickly become difficult to maintain.

That’s where APIRouter becomes useful.



It allows me to organize routes by feature and keep the application structure clean.



So my takeaway is simple:



𝐅𝐚𝐬𝐭𝐀𝐏𝐈 𝐥𝐞𝐭𝐬 𝐦𝐞 𝐝𝐞𝐟𝐢𝐧𝐞 𝐭𝐡𝐞 𝐫𝐨𝐮𝐭𝐞𝐬.

𝐀𝐏𝐈𝐑𝐨𝐮𝐭𝐞𝐫 𝐡𝐞𝐥𝐩𝐬 𝐦𝐞 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐞 𝐭𝐡𝐞𝐦.



It’s not required to make FastAPI work. It becomes useful when I want the application to stay organized and maintainable as it grows.



This was one of those small FastAPI concepts that made much more sense once I started looking at the bigger picture.



𝐇𝐚𝐯𝐞 𝐲𝐨𝐮 𝐡𝐚𝐝 𝐭𝐡𝐞 𝐬𝐚𝐦𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐰𝐡𝐞𝐧 𝐦𝐨𝐯𝐢𝐧𝐠 𝐭𝐨 𝐅𝐚𝐬𝐭𝐀𝐏𝐈?



🔗 𝐆𝐢𝐭𝐇𝐮𝐛: https://github.com/epythonlab2/ai-document-api



🎥 𝐅𝐚𝐬𝐭𝐀𝐏𝐈 𝐜𝐨𝐮𝐫𝐬𝐞: https://www.youtube.com/watch?v=0SLLG2Z_Htw&list=PLQNCas8_eikM



#FastAPI #Python #APIRouter #BackendDevelopment #SoftwareArchitecture #APIDevelopment #PythonDeveloper