What changes when your Python app needs governed data, model endpoints, or agentic workflows
by Databricks Staff
Python has become the default language for data-intensive work, AI applications and internal tooling. That's created a new kind of hosting problem — one that looks like an infrastructure question on the surface, but is really a data architecture question underneath.
For a simple web app or public API, picking a hosting platform is a familiar exercise: traffic volume, framework support, deployment workflow, cost. For a dashboard that queries a data warehouse, a model endpoint that calls enterprise data or an agentic app that orchestrates multiple services, the hosting decision and the data access decision are the same decision. Where you run the app determines what the app can reach — and at what latency, with what governance and under whose security controls.
This guide covers the Python hosting landscape: what differentiates the main environment types, how to match them to your workload and what changes when your app is built around data and AI. If you're building a straightforward web application, most platforms will serve you well. If you're building something that needs to read governed data, call a model endpoint or run an AI agent, the field narrows considerably — and the tradeoffs are worth understanding before you build.
