
I like having one trusted source of metrics across teams, with attributes, metrics, and security defined once and reused in dashboards, embedded apps, and AI bots. Embedding dashboards saves me from building a reporting UI, and central security filters handle row-level access. The REST API is very complete, and mstrio-py makes scripting admin and data tasks easy for developers. I also like the flexible Embedding SDK, solid performance on large datasets with in-memory cubes, and reliable SSO and security filters. Automating user provisioning, report deployment, and cache refreshes has removed a lot of manual admin work from our sprints. Business teams can use governed datasets in Library and ask self-service questions with AI bots. We upgraded to get newer AI features, the Mosaic semantic layer, faster releases, and less infrastructure to maintain. The cloud environment was provisioned quickly, and connecting Snowflake was simple. Review collected by and hosted on G2.com.
I find the learning curve steep, especially with schema objects and project setup in Workstation. There are many legacy concepts and tools, so it’s not always clear how to do something, and documentation for newer API features can be thin. The REST API reference needs more request and response examples for authentication, error codes, and pagination; the Embedding SDK docs could use complete React and Angular sample apps, and mstrio-py docs need more end-to-end scripts. I’d also like a clearer guide for promoting objects between dev, test, and prod using migration packages and Git. Version control and CI/CD for objects are harder than with code-first BI tools, licensing is expensive, and the UI can feel heavy compared to modern tools. Migrating existing projects, rebuilding some schema objects, and testing security filters took a few weeks, and getting new developers productive with Workstation and the object model requires training time. Review collected by and hosted on G2.com.


