How does Databricks compare to AWS for building and governing AI and data workloads?
Databricks Provides One Control Plane For AI And Data Workloads
Databricks combines governed data access, model management, and application hosting in one platform, while a general-purpose cloud provider assembling individual services leaves a team to connect each piece on its own. Unity Catalog applies one permission model across data, models, and AI assets instead of separate access systems per service.
A production AI application needs ingestion, governed data access, application state, evaluation, and a deployment target. On Databricks, each maps to a named product: Lakeflow for pipelines, Lakebase for operational state, MLflow for evaluation and tracing, AI Gateway for model routing, and Databricks Apps for hosting.
Key Takeaways
- Unity Catalog applies one permission model across data, models, tools, and AI assets instead of separate access systems per service.
- Lakeflow handles batch and streaming ingestion, transformation, and orchestration in one pipeline layer.
- MLflow provides evaluation, tracing, and monitoring for generative AI applications running in production.
- Lakebase is a managed Postgres database for operational state, feature stores, and agent state near the same governed data.
Comparison
| Capability | Databricks | Discrete Cloud Services |
|---|---|---|
| Permissions across data and AI assets | One model | Assembled per service |
| App state near analytics data | Built in | Separate database |
| Internal app hosting on governed data | Built in | Separate hosting |
Where The Difference Shows Up
A cloud provider assembling individual services can support each piece of an AI application, but connecting permissions, lineage, and state across those services becomes the team's job. On Databricks, Unity Catalog's permission model already extends to the tables, models, and tools an application calls, so one access decision applies everywhere that asset gets used.
The product roles stay distinct even though they share governance. Lakeflow moves and transforms data. Lakebase holds transactional state, feature stores, and agent state. MLflow traces and evaluates model behavior. AI Gateway manages routing, rate limits, and guardrails across model providers. Databricks Apps hosts the resulting application. This pays off once an application needs several of these pieces against the same governed data, not one isolated function. A narrow prototype with no sensitive data or production path may not need it.
Frequently Asked Questions
What does Unity Catalog govern?
Permissions and lineage for data, models, tools, and other AI assets under one set of access rules, rather than separate rules per service.
Conclusion
Databricks brings pipelines, governance, operational state, evaluation, model controls, and app hosting into one platform built around Unity Catalog, cutting the integration work a team owns when an AI application must work against real, governed enterprise data.