Thanks to our latest update, you can now deploy Streamlit apps seamlessly onto Morph’s cloud hosting platform! 🎉

This enables you to host applications in a secure and scalable cloud environment—not only those built with the Morph framework, but also existing data and AI apps made with Streamlit.

Streamlit Deployment

Streamlit is an excellent choice for developing AI and data apps. Since it enables app development purely in Python, it's particularly well-suited for prototyping. With the latest Morph update, your Streamlit prototypes can now evolve into secure, user-authenticated internal apps accessible directly from the cloud. Once deployed on the Morph platform, it is protected by built-in user authentication, eliminating the need for additional code for user authentication.

Additionally, Morph allows complete runtime customization using Docker, enabling you to freely install and configure libraries and packages according to your requirements.

We've published a detailed tutorial on deploying Streamlit apps with Morph:

https://dev.to/morph-data/tutorial-deploying-a-streamlit-data-app-with-user-authentication-using-morph-1e7j

Cloud Provider Options: AWS and GCP

You can now choose your execution environment from AWS / GCP.

Deploying Streamlit apps specifically requires selecting GCP. For other frameworks, you're free to choose the most suitable platform. This update improves flexibility, allowing you to select regions closer to your operational environment and adjust settings based on your application's needs.

You can also optimize your infrastructure by fine-tuning CPU, memory, concurrency, and other settings to leverage the unique strengths of each cloud provider, aligning precisely with your application requirements.

Provisioning via morph_project.yml

Deployment settings in Morph are now automatically applied by defining build and deployment configurations in the morph_project.yml file. Previously, deploying apps on Morph required configuring a Dockerfile, and it was not possible to easily adjust server performance or regional settings. With this update, developers can effortlessly modify settings through YAML configuration. Changes made and pushed to GitHub automatically update the deployment environment.

version: '1'

# Build Settings
build:
    runtime: python3.12
    framework: streamlit
    package_manager: poetry

# Deployment Settings
deployment:
    provider: gcp
    gcp:
        region: us-central1
        memory: "1Gi"
        cpu: 1
        concurrency: 80
        timeout: 300

Future Outlook

The ideal framework for building internal apps depends heavily on the developer’s preferred tech stack and specific requirements. While AI-assisted coding simplifies development regardless of the chosen framework, challenges often remain around securely sharing applications internally, including user authentication and infrastructure management.

By continuously expanding the range of supported frameworks beyond Streamlit, we aim to free developers from the complexity of internal app-sharing setups. This way, developers can fully concentrate on improving their code quality and functionality.

Be sure to check the latest documentation (https://docs.morph-data.io) to see just how easy Morph makes sharing internal apps—democratizing AI and data utilization within your company to improve operational efficiency and foster innovation.