Product

OrionEmbedded

By Industry

RetailFintechManufacturingMiningSupply ChainSaaS

Resources

AI Readiness DiagnosticCompareBlogChangelogPressDocumentation

Company

About

Orion vs Omni

Omni is a BI tool your team builds its model inside. Orion delivers the answer from the stack you already have.

Omni is a BI tool where your analysts model your metrics and build dashboards. Orion sits on the warehouse you already run and reuses the semantic layer you already trust. It gives your data team and business users one place to get answers, delivered to where you already work. Nothing to rebuild.

TL;DR

Reuse everything. Rebuild nothing.

Orion sits on the warehouse you already run and the semantic layer you already trust. Omni's model is built and maintained inside Omni.

One room for both teams

Your analysts and business users share one Orion thread and watch the same answer stream. Omni's Slack Agent answers in channel threads.

It comes to you

Orion watches your metrics and delivers the answer to Slack or email. With Omni, you write the prompt and set the schedule. Orion also catches what you did not schedule.

What is the difference between Orion and Omni?

Omni is a place your analysts build in. Orion is the bridge to your business.

Orion

Orion connects to the warehouse you already run and reuses the semantic layer you already trust, like LookML or dbt. Your data team and business users share one source of answers. Business users ask Orion in its chat or in Slack and get the answer on the spot, without waiting on the data team. Orion watches your metrics, digs into why they moved, and sends the answer to Slack or email before anyone thinks to ask. Your whole team can share one conversation with it, each person at their own access level. It sits on top of your stack, with nothing to rebuild. And if you build software yourself, you can embed Orion in your own product as a white-label analyst for your customers.

Omni

Omni is a capable BI and analytics tool. Your team models your metrics in Omni, then works with them through spreadsheets, dashboards, and a chat interface that answers over that model. It is a strong place for analysts to build, and the analysis lives inside the model your team builds and maintains in Omni.

How do Orion and Omni compare feature by feature?

Orion is best for

Teams that want answers delivered, not authored

Omni is best for

Analyst teams that want to author and own their BI tool

OrionOmni
Proactive investigationAuto-detects the significant changes in your metrics, then investigates whyRoutines run prompts you author, on a schedule you set↗
Everyone in one live conversationEveryone posts into one thread and watches the same answer stream, each at their own access level. The Room stays the single record, and nobody can copy it into a private threadIn Slack, the Omni Slack Agent answers in channel threads and takes follow-ups there. In the app, a shared chat shows cached results from its creator↗
Nothing to rebuildReads the semantic layer you already trust, in place, with no model to rebuild and no second copy to keep in syncA LookML converter imports your model, dbt descriptions sync in, and your team maintains the model inside Omni↗
Semantic layer ingestionReads your LookML and dbt straight from their repos and reuses the dimensions, measures, and models you already defined to write better SQL. Your repo stays the source of truth. No semantic layer yet? Orion builds one from your metadata.Converter migrates LookML into Omni's model, which your team then owns↗
You own your contextA cited Knowledge Base your team owns, kept as markdown, synced from GitHub, Confluence, or Notion, and open to the other AI tools you useBusiness logic lives in Omni's model. Model changes push to a branch in your dbt repo, and you open the pull request↗
Reaches your whole stackReasons across every source you connect: Snowflake, BigQuery, Databricks, Redshift, and more, with Looker, dbt, and your Knowledge Base as contextQueries run through your Omni model. Integrations supply app and file context↗
Hands-on data modelingOrion reads your model. It does not replace your modeling workflowA full modeling layer your analysts author, with a push to your dbt repo for review↗
Dashboard and spreadsheet authoringGenerates dashboards, reports, and decks from a plain-language request. You refine by describing the change, not by dragging fields aroundFull BI authoring: spreadsheet tabs, dashboards, and pivot-table exploration↗

How does Orion replace the dashboard-and-wait loop?

With a BI tool, someone builds the dashboard, your team checks it, and an analyst digs in when a number looks off. Orion runs that entire loop for you. It watches your metrics, investigates why they moved, and sends the answer to Slack or email.

You keep the warehouse and the definitions you already trust. You just stop living in dashboards to get a decision.

When should you choose Omni instead?

The Omni vs Orion decision comes down to who authors the model and how answers reach the business.

Choose Omni if

  • You want your analysts to author and own a full BI tool: the modeling layer, spreadsheets, and pixel-level dashboards.
  • You want to build analytics incrementally and hands-on, rather than receive finished analysis as the product.
  • You are consolidating BI onto one enterprise tool your whole team standardizes on.

Choose Orion if

  • You already run a warehouse and a semantic layer, and you do not want to rebuild or maintain either in a second place.
  • Your data team is underwater and business users need investigated answers without waiting on an analyst.
  • You want the why behind a metric change found and delivered to Slack or email, including changes nobody scheduled.

Orion vs Omni: what do buyers ask most?

No. Orion is warehouse-native. 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 imports your model, and its dbt integration syncs in descriptions, tests, and tags. It pushes model changes to a branch in your dbt repository, where you open the pull request. 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 sends the result to different people depending on what it found, 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. That gap 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 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 pushes model changes to a branch in your dbt repository, where you open the pull request. 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 spreadsheet tabs, dashboards, or pivot-table 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's Slack Agent answers in channel threads and takes follow-ups there, so the shared place is Slack. In the app, a shared chat shows cached results from when the creator ran them. With Orion the shared thing is the analysis itself: one conversation your data team and your business have together. A room cannot be copied into a private duplicate.

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 describing the change rather than dragging fields around. Dashboards have filters, and you click a number to find out why it moved. You get the finished dashboard or deck without assembling it by hand. Omni is for hands-on building: pixel-level dashboards, spreadsheets, and interactive exploration. Orion hands you the finished dashboard or deck 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 a service account you scope to the data it may analyze, 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 to the warehouses and sources you already run. It can investigate a question that spans Snowflake, BigQuery, Databricks, and Redshift together. 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. So you can check its numbers against 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 nothing left behind to maintain.

More Orion comparisons

vs Sigmavs Hexvs Looker CAvs ThoughtSpot
See all comparisons

See what your team looks like with an AI Analyst.