Hi @prasuanu1222,

For the first question, it has been answered before on this forum. Genie is more of a managed service. You can find details about the underlying models on this page. This can change as Databricks switches to the best model available at the time. 

From a cost perspective, Genie costs have two parts. LLM usage charges and the existing compute cost. Compute is billed separately and unchanged by Genie Paygo. For Genie Spaces specifically, the FAQ says every user gets 150 DBUs of free usage every month, covering Genie, Genie Spaces, and Genie Code. Many users never exceed it. So, that should give you an indication. You can also check this community post. 

For the second question... if the goal is simply to surface a Genie Space within another website or application, the API is no longer the only option. Databricks now documents a native iframe-based option here... Embed a Genie Space in an external app. That page explains how a workspace admin must first allow the embedding domain, after which a space author can generate iframe code directly from the Share dialog.

The important limitation is that this does not bypass throughput limits by having many spaces. The iframe path is documented as 20 questions per minute per workspace across all Genie Spaces, not per space.

By contrast, the public docs for the Conversation API say the free tier is best-effort five questions per minute per workspace across all Genie Spaces. So if you were avoiding the API because you have a large number of spaces, that concern is valid, but the same general principle still applies... the limit is enforced at the workspace level rather than separately for each space.

So...

• If you only need to embed the existing Genie experience inside another app, use the documented iframe approach. Embed a Genie Space in an external app.
• If you need a fully custom UX, custom orchestration, or application-controlled rendering, then the API is still the right path. Use the Genie Spaces API.
• If you go down the API route, the docs explicitly recommend retry logic and exponential backoff to handle throughput constraints gracefully.

If this answer resolves your question, could you mark it as “Accept as Solution”? That helps other users quickly find the correct fix.