I’m relatively new to the Power BI service and I’m confused about where I should be publishing my reports. What are the actual functional differences between "My Workspace" and the shared "App Workspaces"? Specifically, I want to know about the limitations regarding collaboration, app publishing, and what happens to the data if I happen to leave the company or change roles.
3 answers
The biggest difference lies in ownership and collaboration. "My Workspace" is your personal sandbox; it’s tied strictly to your user account. While you can share individual reports from it, you cannot collaborate with others on the underlying datasets, nor can you publish a Power BI App from a personal workspace. In contrast, shared workspaces (V2) allow for role-based access (Admin, Member, Contributor, Viewer). This makes them essential for team projects. If you leave the company, anything in "My Workspace" becomes inaccessible to others unless an Admin manually intervenes, whereas shared workspaces persist regardless of individual personnel changes.
That makes sense for long-term projects, but is there a storage limit difference between the two? I noticed my personal area has a 10GB limit, but does a shared workspace pool storage differently if we are on a Premium capacity
Shared workspaces are the only way to go for "production" content. You get better governance, version control through OneDrive integration, and the ability to bundle reports into Apps.
Exactly, Karen. I always tell my team to treat "My Workspace" as a draft area only. Once a report is ready for any kind of audience, it should be moved to a shared workspace. It also makes it much easier to manage Row-Level Security (RLS) since you can test roles across the team more effectively in a shared environment.
You've hit on a key point, James. In a standard Pro environment, both "My Workspace" and shared workspaces hit that 10GB per-user cap. However, if you move a shared workspace to a Premium Capacity (P SKU) or Fabric Capacity (F SKU), the storage limit jumps significantly—up to 100TB for the entire capacity. This is why enterprise-level data science projects always use shared workspaces; they allow you to handle massive datasets that simply wouldn't fit in a personal "My Workspace" account.