Streamlit and Flask are two of the most popular ways to deploy models, but they take completely different approaches to solving the problem.

Here is how they compare, and how to know which one is right for your project.

1. Flask: The “Do-It-Yourself” Engine

As we touched on earlier, Flask is a micro-framework for building web servers and APIs. It is a general-purpose web tool, meaning it wasn’t built just for Machine Learning, but it is perfect for it.

• How it works: Flask handles the “backend” (the invisible logic). It takes in data, feeds it to your Pickled model, and spits out a prediction.
• The Catch: Flask does not build a pretty user interface for you. If you want buttons and text boxes, you have to write separate HTML, CSS, and JavaScript files to create the “frontend.”
• Real-life analogy: Flask is like building a restaurant kitchen from scratch. You have total control over where every appliance goes and how the food is made, but if you want customers to sit down and order, you still have to build the dining room and design the menus yourself.

2. Streamlit: The “Magic Wand” for Data Scientists

Streamlit is a tool designed specifically for Machine Learning and Data Science. Its entire goal is to let you build interactive web apps using only Python—no HTML or CSS required.

• How it works: You write a regular Python script. Whenever you want to add a title, a slider, or a “Predict” button, you just type a single line of Python code (e.g., st.button(“Predict”)). Streamlit automatically generates a beautiful, modern web page for you and connects it to your model.
• The Catch: Because Streamlit builds the visual layout for you, you don’t have as much fine-tuned control over how the app looks. It will always look like a “Streamlit app.”
• Real-life analogy: Streamlit is like buying a fully equipped food truck. The kitchen and the ordering window are already built-in together. You cannot easily change the shape of the truck, but you can start serving food (predictions) immediately.

Which one should you choose?

Here is a quick breakdown to help you decide:

Practical Scenario

• Use Streamlit if: You just finished a cool housing price predictor for a class project and want to send a link to your friends or a recruiter so they can play around with sliders and see the predictions on a neat dashboard.
• Use Flask if: You are hired by Zillow. Zillow already has a massive website (a frontend). They just need you to build a Flask API so their existing website can silently send data to your model and get a prediction back.