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Teams comparing Databricks to cloud-native ML platforms are usually past experimentation. The question is no longer whether machine learning works, but where it should live and who should own it. What looks like a tooling comparison is actually an organizational decision.
This article focuses on how that decision plays out in practice.
What you’re really deciding
You are deciding whether machine learning should be data-centric or platform-centric. Databricks assumes ML is an extension of the data layer. Cloud ML platforms assume ML is a managed service with defined boundaries.
Each approach optimizes for a different kind of control.
Where Databricks holds up
Databricks works best when ML is tightly coupled to data engineering. A common scenario is a team already using Databricks for analytics that begins training models directly on shared datasets.
This approach holds up when:
- Data pipelines are the primary asset
- ML models evolve alongside analytics
- Teams want flexibility over abstraction
- Engineers are comfortable owning complexity
In these environments, Databricks keeps ML close to the data that powers it.
Where cloud ML platforms hold up
Cloud ML platforms shine when ML needs to be standardized and governed. A typical scenario involves multiple teams deploying models into production systems with shared security, monitoring, and compliance requirements.
Platforms like AWS SageMaker, Azure Machine Learning, or Vertex AI fit best when predictability and oversight matter more than flexibility.
Where teams run into trouble
Problems arise when expectations are mismatched. Teams adopt Databricks expecting turnkey deployment, or adopt cloud platforms expecting data-layer flexibility.
Common failure patterns include:
- Duplicate pipelines across systems
- Unclear ownership between data and ML teams
- Platforms enforcing structure before workflows are understood
The tooling rarely fails first. Coordination does.
Who this tends to work for
Databricks fits organizations where ML is a natural extension of analytics and data science. Cloud ML platforms fit organizations treating ML as a shared, governed production capability.
The wrong choice usually reflects unclear ownership, not technical limits.
The bottom line
Databricks optimizes for proximity to data. Cloud ML platforms optimize for operational consistency. Choose based on where ML responsibility should live, not which tool looks more powerful.
Related guides
Enterprise ML platforms
Explains how governance, monitoring, and ownership requirements change once machine learning becomes a shared production dependency across teams rather than a research activity.
Managed vs self-hosted AI infrastructure
Provides deeper context on how infrastructure ownership decisions influence cost predictability, staffing requirements, and long-term system reliability.
Choosing a framework for production LLM apps
Shows how application-layer orchestration decisions interact with ML platforms once models are embedded in user-facing systems.