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Orion vs Sigma

Sigma is where your team builds the analysis. Orion writes it and brings you the answer.

Sigma is a warehouse-native, spreadsheet-style tool where your team builds analyses, dashboards, and data apps. Sigma Assistant, formerly Ask Sigma, queries in natural language. Orion connects read-only across the warehouses you already run and reuses the semantic layer you already trust, like LookML or dbt. It investigates why your metrics moved and delivers the answer to where you already work. Nothing to build.

TL;DR

A proactive analyst, not another workspace

Orion investigates and hands you the answer. Sigma is a spreadsheet workspace your team builds the analysis in.

Catches what nobody thought to watch

Sigma's alerts run scheduled threshold and anomaly checks you configure. Orion also investigates the changes nobody thought to configure.

The why, written up and delivered

Orion investigates and writes up why a metric moved. Sigma is where your team builds the workbook to look into it.

What is the difference between Orion and Sigma?

Sigma is a place your team builds in. Orion is the bridge to your business.

Orion

Orion connects read-only 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 governed source of answers. Business users ask Orion in its chat or in Slack and get a governed answer on the spot, without waiting on the data team’s backlog. It keeps watch on your metrics, works out why they moved, and lands the answer in Slack or email unprompted. A whole team can ask in one shared conversation, 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.

Sigma

Sigma is a strong warehouse-native BI tool. Its spreadsheet interface lets business users and analysts explore live warehouse data, build dashboards and data apps, and write data back. Sigma Assistant queries in natural language, and Sigma Agents automate monitoring and actions. It is a capable place to build and analyze, but the analysis is something your team assembles inside Sigma.

How do Orion and Sigma compare feature by feature?

Orion is best for

Teams that want the answer before anyone builds a workbook

Sigma is best for

Spreadsheet-style exploration on live warehouse data

OrionSigma
Proactive root-causeAuto-detects a change, including ones nobody configured, and delivers a written narrativeScheduled threshold and formula-defined anomaly alerts you configure, dispatching to Slack, email, webhooks, and more↗
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 cannot be forked into private copiesLive Edit makes workbooks multiplayer. Sigma Assistant is a personal panel, and no shared thread is documented↗
Reuses your semantic layerReads the LookML and dbt you already trust, in place, across dbt Cloud and dbt CoreA Sigma data model your team builds. Its dbt Semantic Layer is dbt Cloud only↗
LookML ingestionReads your LookML where it lives and converts it into governed metric definitionsNo LookML import documented↗
Analyst, not a workspaceInvestigates and delivers the answer, with nothing to buildA spreadsheet workspace your team builds analyses and apps in↗
Spreadsheet-style explorationNot a spreadsheet UI. Delivers written analysis and artifactsA spreadsheet interface over live warehouse data that business users already know↗
Dashboard and data-app authoringGenerates dashboards, reports, and decks on request. Not an authoring environmentDashboards, data apps, and write-back your team builds hands-on↗

Who builds the analysis: your team or Orion?

Sigma gives your team a fast, familiar place to build: spreadsheets over live warehouse data, dashboards, and data apps. But someone still has to decide what to look at, build the workbook, and turn it into an answer a stakeholder can act on.

Orion does that work. It watches your metrics, investigates why they moved across the warehouses you connect, writes the answer, and delivers it to Slack or email. The question gets answered without anyone building a workbook.

When should you choose Sigma instead?

The Sigma vs Orion decision comes down to whether your team wants to work in the data or have the answer brought to them.

Choose Sigma if

  • Your business users want to explore live warehouse data in a spreadsheet interface they already know.
  • Your team builds dashboards, data apps, and write-back workflows hands-on.
  • Alerts on metrics and conditions your team defines cover your proactive needs.

Choose Orion if

  • You want the changes nobody configured investigated and delivered to Slack or email.
  • You want to reuse the LookML and dbt you already maintain, dbt Cloud or Core, with nothing rebuilt in a second model.
  • You want answers written up with the why, not a workbook someone still has to build and read.

Orion vs Sigma: what do buyers ask most?

Orion adds the investigation Sigma leaves to your team: the recurring 'why did this move' questions stop turning into new Sigma workbooks. Sigma is a warehouse-native place to build: spreadsheets over live data, dashboards, and data apps, with Sigma Assistant to query in natural language. Orion is the analyst on top of the warehouses you already run. It watches your metrics, investigates why they moved, and delivers a written answer to Slack or email before anyone opens a workbook. Sigma is the workbench your team builds analysis in. Orion brings the finding to the people who need it.

No. Sigma Assistant, formerly Ask Sigma, is a conversational way to query your data inside Sigma. You ask a question, and it returns charts and insights you then explore in the workbook. It is request-response, and the analysis lives in Sigma. Orion flips what your team builds. Instead of building each analysis, you build the context once: the semantic layer Orion reads and a cited Knowledge Base the whole org reuses. Orion builds the analyses from that shared context, writes up what it found and why, and delivers it, proactively rather than waiting to be asked. Sigma Assistant helps someone in Sigma get an answer faster. Orion is the analyst that catches the change no one asked about and explains it, wherever your data lives.

Scheduled, yes. Self-directed, no. Sigma has conditional exports and alerts that run threshold and formula-defined anomaly checks on a schedule, and deliver to email, Slack, webhooks, Teams, and more. Sigma Agents add scheduled monitoring and actions. All of that is genuinely useful. The difference is that the setup is author-defined. Someone picks the metric and configures the check that decides when an alert fires. It catches the things you already knew to watch. Orion auto-detects the significant changes you did not think to define, investigates the root cause, and delivers the write-up. Sigma alerts on the conditions you set. Orion also surfaces the ones you did not, which is where the problems a stretched team misses actually live.

Partly, and less than Orion. Sigma builds its own semantic layer through Sigma data models. It can reuse a dbt Semantic Layer, but only dbt Cloud, not dbt Core. Its docs show no LookML import. So your existing logic is either rebuilt in Sigma or partially connected through dbt Cloud. Orion instead reads the LookML and dbt you already trust in place, across both dbt Cloud and dbt Core. That is the governed logic behind every answer. A cited Knowledge Base adds the rest. Nothing to rebuild, and the semantic layer you already maintain is the one Orion uses. And if you have neither, Orion builds a semantic layer from your warehouse metadata and Knowledge Base, so there is still nothing to rebuild.

Orion and Sigma both ground answers in governed logic and honor permissions. Sigma applies column- and row-level security on its data models, and grounds Sigma Assistant in admin-configured sources. Orion grounds every answer in the business logic you already trust, your LookML and dbt definitions plus a cited Knowledge Base. Every analysis is captured in a notebook: the instructions, the logic, and the code. You can see exactly how each answer was produced, and re-run it. It connects read-only through dedicated least-privilege service accounts, with optional per-user OAuth on BigQuery. Roles on two levels, tenant and group, scope who can see which projects and data. The difference is whose definitions the AI uses: Orion uses the ones you have already standardized, instead of a model you rebuild in the tool.

Rooms lets several people work one question with Orion at the same time, in a single conversation. Each message carries its author, and each participant keeps their own access level, so finance can watch the investigation unfold without anyone widening permissions. Co-editing a workbook shares the document. A Room shares the analysis: one conversation your data team and your business have together, with Orion in it.

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 connects read-only to the same warehouses Sigma sits on, so they coexist. Teams keep Sigma as the place analysts and business users build spreadsheets, dashboards, and data apps. They add Orion as the analyst. Orion monitors metrics across the whole stack, investigates what changed, and sends the written answer to Slack or email. Sigma handles the building. Orion handles the proactive investigation so the follow-up questions never pile up on the data team. 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.

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