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