Orion vs Omni

No. Orion is warehouse-native and read-only. It connects to the warehouse you already run, like BigQuery, Snowflake, Databricks, or Redshift. It works from the semantic layer you already trust, whether one you maintain or a layer Orion builds from your metadata and Knowledge Base. There is nothing to port and no new BI tool to stand up. Omni takes the opposite path: the working model lives inside Omni. Its LookML converter and dbt integration import what you already have. Omni can even push changes back to your dbt repository as pull requests. But the destination is still a model your team owns and maintains inside Omni, rather than one read where it lives. The practical difference: with Orion you keep your stack and definitions exactly as they are and get answers on top of them. With Omni, the semantic layer becomes something you rebuild and maintain in a second place.

Omni Routines answer the questions you wrote in advance. Orion also catches the ones you did not. A Routine runs a prompt you write, on a schedule, and delivers a written response to the people you choose. Each run interprets the fresh numbers, which is genuinely useful for recurring reports. Orion does scheduled delivery too, through a Workflow. It re-runs an analysis you have already validated and delivers a dashboard, report, or slide deck on that schedule. But a Workflow can also branch. Its decision steps judge whether the numbers are worth flagging. It then routes to different people depending on the result, rather than mailing the same summary to everyone. Separately, Orion watches your metrics and auto-detects the significant changes you did not think to schedule. It runs the root-cause analysis and delivers the write-up before anyone asks. So Routines answer the questions you remembered to schedule. Orion also catches the ones you did not, which is where the unknown unknowns a stretched data team misses actually live.

Orion reads them in place. It uses your LookML and dbt models where they already live, as the governed logic behind every answer. A cited Knowledge Base adds the context that is not in the model. Nothing moves and there is no second copy to keep in sync. Omni takes a different approach. It ships a LookML converter and a dbt integration that reads your manifest. It can push changes back to your dbt repository as pull requests. Omni does meet you where you are. But the working model is then authored and owned inside Omni. If your goal is to reuse the semantic layer you have already built without maintaining a parallel one, Orion reads it directly. If you want a BI tool whose model you actively own and edit, that is Omni's design.

Orion cuts the request load by doing the analysis and delivering it. Omni cuts it by making asking easier. In Omni, business users query the Omni Agent in the app or in Slack. They continue in spreadsheets, dashboards, or point-and-click exploration, and row-level security applies to results. Orion takes the request off the data team entirely. A business user asks Orion in Slack or its web app. Orion investigates, writes the answer with the numbers and the why, and delivers it to Slack or email. It also delivers scheduled and proactive findings without anyone asking. Every answer is grounded in the semantic layer you already trust and a cited Knowledge Base, and it cites its sources. Users can act on it without an analyst.

Rooms puts your whole team in one conversation with Orion. An analyst and the ops lead ask in the same thread, and every message is attributed. Each person operates at their own access level. A business user can follow the investigation without gaining query rights. Omni has multi-person Q&A too, in Slack threads, where the shared surface is Slack. With Orion the shared thing is the analysis itself: one conversation your data team and your business have together. A room cannot be forked into private copies.

Yes, Orion builds dashboards, and written reports, slide decks, and audio briefings too, from a plain-language request, then delivers them to Slack or email. You refine them by asking rather than dragging fields around, and you get the finished artifact without assembling it by hand. Omni is for hands-on building: pixel-level dashboards, spreadsheets, and interactive exploration. Orion hands you the finished artifact instead. The distinction that matters for buyers: with Orion you never open a dashboard to get an answer. Orion delivers it directly. When you do want a dashboard or a board-ready deck, Orion produces one for you. With Omni, the dashboard is the product, and someone builds and maintains it.

Orion grounds every answer in the semantic layer you already trust, plus a cited Knowledge Base. Every analysis is captured in a notebook, Orion's record of the instructions, the logic, and the code. You can trace exactly how a number was produced and reconcile it with your BI. On access: Orion connects read-only through dedicated least-privilege service accounts, with optional per-user OAuth on BigQuery. Roles scope who can see which projects and data. Omni governs too: it answers from the model you have built in Omni and applies row-level security to results. Both govern. The difference is whose definitions the AI uses. Orion uses the ones you have already standardized, instead of a model you re-create and maintain in a separate tool.

Orion connects read-only across the warehouses and sources you already run. It can investigate a question that spans Snowflake, BigQuery, Databricks, and Redshift together, alongside the context in each. Omni is grounded in the semantic model you build in Omni, so its governed answers come from what your team has modeled there. Orion instead reads across the sources you connect as they are, rather than asking you to bring everything into one modeling layer first. Your data may live in several systems, with no single place that sees all of them. Orion works across them as they are and brings you the answer.

Yes. Embedded Orion is a white-label analyst for your customers, presented under your brand inside your product. Each customer account is isolated: they see only their own data, business context, and permissions, and nothing passes between accounts. Your customers sign in to your product, and Orion inherits who they are and what they can see. There is no second login. You integrate once. Every new account is provisioned with its own branding, data connections, and access controls, so the second customer costs a fraction of the first.

Yes. Orion is built to run alongside the BI tools you already have, Omni included. It connects to the same warehouse Omni queries and reuses the LookML or dbt logic you already have. Its numbers reconcile with what your team already sees in Omni. Teams keep Omni as the BI tool where analysts build and explore. They add Orion to watch the metrics, investigate what changed, and deliver the answer to Slack or email before anyone opens a dashboard. You are not choosing one or the other. Orion adds proactive investigation and delivery on top of the stack you already run. And if you consolidate later, Orion already runs on the foundations your BI is built on. You migrate on your own timeline, with no tech debt to keep around.