It’s the question we’re hearing most often from data leaders right now: “Now that we’re on Databricks, do we still need Power BI?” On the surface, it sounds like a tooling preference. Underneath, it’s a cost question, and a fair one.
Both platforms are moving toward each other. Databricks has added AI/BI Dashboards and Genie; Power BI has Copilot. As the overlap grows, running both starts to look like paying twice for the same capability: licences, effort for building and maintaining reports in two places, skills to support each, governing them separately, and often keeping the same data in two homes.
Cost, data, effort, skills and governance all get duplicated, and that’s the real worry behind the question.
But which one wins is the wrong frame. The two are good at genuinely different things, and for most organisations the honest answer isn’t either/or – it’s about what the business needs.
Where Power BI earns its place
Power BI is the industry standard, and that counts for a lot. Most teams already know it, expertise and support are easy to find, and because of that, there’s minimal or no ramp-up cost to get value; people are productive on day one. It’s feature-rich for visualisation and dashboarding, with a depth of formatting and interactivity that’s hard to match.
Its real trump card is sharing. Power BI lets you put a report in front of people who don’t live on your data platform, such as a board, a regulator or an external partner, without giving them access to it. And when your data spans more than one platform, Power BI is the neutral reporting layer that can sit across all of it.
Where Databricks Dashboards earn their place
Databricks AI/BI Dashboards earn their place by removing layers rather than adding them. Because they run on the platform your data already sits on, security and access are inherited automatically, so there’s no separate governance layer to build or keep in sync. There’s also no separate licence to buy either; it’s part of the stack you’re already paying for.
The bigger shift is where the work happens. Dashboards are built right where the data lives, so an idea can be explored and prototyped without exporting anything or switching tools. Add in Genie, the built-in AI, and you’ve got conversational analytics capability built on the data in your Databricks Lakehouse. Your users can then generate a dashboard from a plain-English prompt, cutting the time from question to first answer. For self-service and fast exploration, that immediacy is the point.
How to actually decide
There’s no universal answer, but there is a way to reason about it. Three things tend to settle it:
- Business change impact: what does moving, or not moving, a given set of reports actually cost you in disruption and rebuild?
- Data availability: does the data for this report live entirely in Databricks, or is it spread across platforms?
- Non-negotiable features: external sharing, a specific visual, or a compliance requirement that only one tool meets.
Here’s the part that settles the cost anxiety without forcing a false choice. Even where Power BI is the right front-end, there’s a good case for pushing self-service and exploration onto Databricks Dashboards. It keeps that work on already-governed data, which is exactly where duplication hurts most: you stop copying data into a second home, and you stop maintaining a second governance regime around it.
So, the answer to “do we still need Power BI?” is usually yes, but not for everything and not by default. Treat the two as complementary. Use Power BI for polished, broadly shared, cross-platform reporting, and Databricks Dashboards for governed self-service close to the data. Decide report by report and use by use – not tool by tool. That’s how you get the value of both without paying twice for the overlap.