In this comparison, we highlight the key differences between Fluent and Looker, addressing questions about the intended users for each solution, their technical distinctions, and their scalability within an organization.
#2: Flexibility vs. Locked-In Tooling
Looker studio is an add-on within the Looker suite, which makes it part of a 'locked-in' solution. If you're committed to using Looker as your long-term data solution, it may be an ideal option. If you work across multiple data tools or foresee making changes to your data stack in future, you might appreciate the flexibility that Fluent offers. Fluent connects with internal and external data sources, such as your semantic layer or data catalog, allowing you to leverage existing business logic wherever it's stored.
#3: Accuracy-First vs. Answer-First
Fluent focuses on clarity and answerability before generating responses. If the data team has defined the underlying logic, Fluent clarifies any specifics and only then answers. This makes it an ideal self-serve solution that business users can trust not to resort to hallucinations or guessed responses. Looker prioritises first-time answer generation, which is helpful to data professionals iterating on LookML, but is less suitable for business users simply looking for accurate, on-demand insights they can firmly trust.
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