A step-by-step guide to containerizing a FastAPI application with Docker and deploying it to the cloud for consistent, production-ready delivery.
Modern applications rarely exist in isolation. They move between laptops, staging servers, and production environments.
Each environment has its own dedicated, missing libraries, or slightly different configurations. This is where many “works on my machine” problems start.
Docker was created to solve this exact problem, and has become a fundamental skill for building and deploying software even today.
In this article, you’ll learn how to Dockerize the LoganIalyzer Agent project and prepare it for deployment.
We’ll first understand what Docker is and why it matters. Next we’ll walk through converting this FastPy-based project into a Dockerized application. Finally, we’ll cover how to build and upload a Docker image so it can be deployed to a cloud platform like Seolla.
You only need a basic understanding of Python for this project. If you want to learn Docker in detail, go through this detailed tutorial.
What we will cover
What is Docker?
Docker is a tool that packages your application with everything it needs to run. This includes operating system libraries, system dependencies, Python versions, and Python packages. The result is called a Docker image. When this image runs, it becomes a container.
A container behaves the same everywhere. If it runs on your laptop, it will run the same way on a cloud server. This consistency is the main reason why Docker is so widely used.
For the Loginizer agent, this means that FastPy, Lengchain, and all Python dependencies will always be available, regardless of where the app is deployed.
Why Docker Matters
Without Docker, deployment usually involves manually installing dependencies on the server. This process is slow and error prone. A missing system package or incorrect Python version can break the app.
Docker removes this uncertainty. You define an environment once, using a docfile, and reuse it everywhere. This makes it easier to onboard new developers, streamline CI pipelines, and reduce production bugs.
For AI-powered services like the Loginizer Agent, Docker is even more important. These services often rely on specific library versions and environment variables, such as API keys. Docker ensures that these details are controllable and repeatable.
Understanding the plan
Before containerizing an application, it is important to understand its structure. The Loginizer agent consists of a FastPI backend that serves the HTML frontend and exposes an API endpoint for login parsing.
The backend depends on Python packages such as fastpy, langchain, and openaiclient. It also depends on the environment variable for the openai API key.
From Docker’s point of view, this is a normal Python web service. This makes it an ideal candidate for containerization.
At this point, you should clone Project Repository to your local machine. You can run the app using the command python app.py
Writing a Dockerfile
dockerfile is the recipe that tells Docker how to build your image. It starts from a base image, installs dependencies, copies your code, and defines how the application should start.
For this project, the lightweight Python icon is a good choice. Dockfile might look like this:
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
EXPOSE 8000
CMD ("uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000")
Each line has a purpose: provides a base image to Python and keeps the working directory files organized.
Dependencies are installed before copying the full code to improve build caching. The exposed directive documents the port used by the app. The command starts the Fast API server.
This file alone turns your project into something that understands Docker.
Handling environment variables in Docker
The Loginizer agent relies on the OpenAI API key. This key should never be hardcoded into the image. Instead, Docker allows environment variables to be passed at runtime.
During local testing, you can still use .env file When running a container, you can pass variables using Docker’s environment flags or your deployment platform settings.
This separation preserves secrets and allows the same image to be used in multiple environments.
Building the Docker image
Once the daco file is ready, building the image is straightforward. From the root of the project, you run the docker build command:
docker build -t loganalyzer:latest .
Docker reads the Docker file, executes each step, and generates an image.
This image contains your FastPy app, HTML UI, and all dependencies. At this point, you can run it locally to verify that everything works as before.
Running the container locally is an important validation step. If the app works inside Docker on your machine, it’s very likely to work just as well in production.
Testing the container locally
After creating the image, you can start a container and map its port to your local machine. When the container is started, UVCorn runs inside it, just like it did outside of Docker.
docker run -d -p 8000:8000 -e OPENAI_API_KEY=your_api_key_here loganalyzer:latest
You should be able to open a browser, upload a log file, and receive analysis results. If something fails, the container logs will usually point you to missing files or incorrect paths.
This feedback loop is fast and helps you fix problems before deployment.
Preparing the image for deployment
At this point, the Docker image is ready to be uploaded to the container registry. A registry is a place where Docker images are stored and shared. Your deployment platform will later pull an image from this registry.
We will use dockerhub Promoting our image. Create and run an account docker login Command to verify it with your terminal.
Let’s now tag and push our image to the repository:
docker tag loganalyzer:latest your-dockerhub-username/loganalyzer:latest
docker push your-dockerhub-username/loganalyzer:latest
Adding a docker image to seula
The final step is to upload the Docker image for deployment.
You can choose any cloud provider, such as AWS, Digitalisan, or others, to run your application. I’ll use Siola for this example.
Seoul is a developer-friendly PAAS provider. It offers application hosting, database, object storage, and static site hosting for your projects.
Each platform will charge you to create a cloud resource. Siola comes with a $20 credit for our use, so we won’t charge for this instance.
Login Click on Seola and Applications -> Create New Application:
You can see your linking option Container storage. Use the default settings. Click on “Create Application”.
Now we need to add our openui API key to the environment variables. Once the application is created click on the “Environment Variables” section, and save OPENAI_API_KEY value as an environmental variable.
Now we are ready to deploy our application. Click on “Deploy” and click on “Deploy Now”. The deployment will take 2–3 minutes to complete.
Once done, click on “View App”. You will see the request ending with the URL sevalla.app.
Congratulations! Your Login Analyzer is now Dockerized and live.
From this perspective, deployment becomes easier. A new version of an app is just a new Docker image. You can push an image to storage and Sevilla will automatically pull it.
Final thoughts
Docker turns your application into a portable, predictable unit. For the Loginizer agent, this means the AI ​​logic, the Fast API server, and the front end all move together as a single instance.
By cloning the project, adding a Docker file, and building an image, you turn a local prototype into a definable service. Uploading this image to Sevilla completes the journey from code to production.
Once you get comfortable with this workflow, you’ll find that Docker isn’t just a deployment tool. It becomes the core of how you design, test, and ship applications with confidence.
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