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Orion vs Looker Conversational Analytics

Looker Conversational Analytics explains one metric at a time. Orion takes your LookML across your whole stack.

Looker Conversational Analytics is Gemini chat over your LookML Explores, in Looker, Gemini Enterprise, and apps built on its API. Orion reads the same LookML and works across the warehouses you run. On its own, it investigates why your metrics moved. It builds the dashboard when you ask for one, and delivers the answer to where you already work. Keep Looker. Orion extends it.

TL;DR

Same LookML, whole stack

Orion reads the LookML you already have and reasons across the warehouses you run. Looker CA answers over your LookML Explores, or BigQuery through its API.

Investigation, not one metric

Looker's agentic workflows (Preview) can send a Gemini key-driver analysis of the change that triggered them. Orion investigates across everything you connect.

Chat to dashboard

Ask Orion for a dashboard and it builds one, plus reports and decks, delivered to Slack or email. Looker CA's chat does not produce a dashboard.

What is the difference between Orion and Looker Conversational Analytics?

Looker CA explains the metric you point it at. Orion investigates across your stack and takes the same LookML further.

Orion

Orion connects to the warehouse you already run and reuses the LookML you already trust. Your data team and business users share one governed place to get answers. Business users ask their own questions in Orion’s chat or in Slack, and get governed answers without waiting on the data team’s queue. Orion connects read-only, watches your metrics, investigates why they moved, and delivers a written answer to Slack or email before anyone asks. Teams can share one conversation with it, each answered at their own access level. Your semantic layer, working across your whole stack and beyond the people who log into Looker. And if you build software yourself, you can embed Orion in your own product as a white-label analyst for your customers.

Looker Conversational Analytics

Looker Conversational Analytics is Google’s Gemini-powered chat over your LookML Explores. It has grown past the Looker UI. The Conversational Analytics API went GA in June 2026 for BigQuery and Looker. Agents publish to Gemini Enterprise (preview) and custom apps through its API, and agentic workflows can push alerts to Slack, email, and mobile. The limits are depth and direction. Key-driver analysis is optional, in Preview, and explains only the change that triggered a workflow. A chat does not produce a dashboard.

How do Orion and Looker Conversational Analytics compare?

Orion is best for

LookML teams whose questions outgrow the Looker instance

Looker Conversational Analytics is best for

Conversational Q&A over governed LookML Explores

OrionLooker CA
Proactive investigationAuto-detects a change and delivers a written root-cause narrativeAgentic workflows are in Preview. Optional Gemini key-driver analysis explains the change that triggered the workflow↗
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 copiesPer-user chat. Google documents Explore conversations as typically limited to one user, and not shareable↗
Turns a chat into a dashboardBuilds dashboards, reports, and slide decks from a plain-language requestThe chat does not produce a dashboard. Results open in an Explore as a manual step↗
Reaches your whole stackReasons across the warehouses you connect, in one investigationGrounded in LookML Explores, or BigQuery through the Conversational Analytics API↗
Reaches people who are not Looker usersConnects through one dedicated Looker user, with optional repo access. The people asking need no Looker account of their ownEach person asking must be granted Looker permissions of their own↗
Delivered where you workSends the answer to Slack or emailWorkflow alerts reach email, Slack, and the Looker mobile app (Preview)↗
First-party Google integrationIndependent overlay. Delivers to Slack or emailOne vendor across Looker, Gemini Enterprise, and Google Chat, governed by your Google contract↗
Governed exploration inside LookerNot a BI tool. Reads your LookML from outsideNative to the Looker experience your analysts already govern↗

What does your LookML buy you outside Looker?

Looker Conversational Analytics is a great way to ask questions of your LookML models. The question has to fit the Explore or agent you point it at.

Orion reads the same LookML and puts it to work across your whole stack. It watches your metrics, investigates why they moved across the warehouses you connect, and delivers the answer to Slack or email. The business logic you built in LookML pays off beyond the people who open Looker.

When should you choose Looker Conversational Analytics instead?

The Looker Conversational Analytics vs Orion decision is not rip-and-replace: you keep Looker and your LookML either way. The question is who does the investigating and where the answers go.

Choose Looker CA if

  • Your questions live inside what LookML models, and governed conversational Q&A is the need.
  • Your organization is deep in the Google ecosystem: Gemini Enterprise, Google Chat, one vendor contract.
  • Alerts on metrics you define, with per-workflow key-driver summaries, cover your proactive needs.

Choose Orion if

  • You want changes investigated beyond the triggering metric, across every warehouse you connect.
  • You want chat to produce the artifact: dashboards, reports, and decks built from a request and delivered.
  • You want your LookML paying off for people who never open Looker, in Slack or email.

Orion vs Looker Conversational Analytics: what do buyers ask most?

No, and it is not meant to. Looker Conversational Analytics is a strong way to ask questions of your LookML models conversationally, inside Looker, for the people who work there. Orion is complementary: it reads the same LookML you have already built and puts it to work across your whole stack. It watches your metrics, investigates why they moved, and delivers a written answer to Slack or email, including to people who never open Looker. A common setup keeps Looker and Conversational Analytics for in-Looker exploration and adds Orion for proactive, cross-warehouse investigation that comes to them.

Yes. Orion reads your LookML through a dedicated least-privilege Looker user, with optional read-only access to the LookML repository. It uses your dimensions, measures, and joins as the governed logic behind every answer, the same definitions your team already trusts. Looker Conversational Analytics also grounds its answers in LookML, from inside Looker or an app built on Google's Conversational Analytics API. That is always through your Looker connection. Orion reuses that investment across the warehouses you connect. A cited Knowledge Base adds the context that is not in the model. Same semantic layer, put to work in more places.

Looker Conversational Analytics is genuinely good at what it does: Gemini-powered, multi-step analysis grounded in your governed LookML. Three things it does not do, per Google's own documentation. First, its proactive layer, agentic workflows, is in Preview, and the optional Gemini key-driver analysis explains only the change that triggered the alert. Second, the Explore or agent you point it at bounds the conversation. The cause of a change that lives in another source stays out of reach. Third, a chat does not produce a dashboard. Results open in an Explore as a manual step. Orion adds all three. It auto-detects significant changes and investigates the root cause across the warehouses you connect. It delivers the written explanation to Slack or email, and builds the dashboard, report, or deck when you ask for one.

No, not anymore, and it is worth being current here. The Conversational Analytics API went GA on June 23, 2026 for BigQuery and Looker. Agents your team builds can publish to Gemini Enterprise (preview) and custom apps through the API, with agent-to-agent (A2A) support in Preview. Looker's agentic workflows can push alerts with Gemini key-driver analysis to Slack, email, and mobile. The differences that remain are depth and direction. The key-driver analysis is optional, in Preview, and explains the change that triggered the workflow. And a chat does not produce a dashboard on its own. Orion investigates beyond the triggering metric, across every warehouse you connect. It hands you the finished artifact: a written narrative, a dashboard, or a deck, in Slack or email.

Not directly. You can ask it questions, get charts and answers back, and open the results in an Explore. From there, building the dashboard is a manual job in Looker. There is no documented path from a conversation straight to a saved dashboard. Orion does this directly. Ask for a dashboard, report, or slide deck in plain language. Orion builds the artifact and delivers it to Slack or email, grounded in the same LookML definitions. If your business users' follow-up to every good answer is 'can I get this as a dashboard,' that difference is the workflow.

Through Looker's connections it reaches the warehouses Looker models, including BigQuery, Snowflake, Redshift, and Databricks, and the Conversational Analytics API adds direct BigQuery grounding. But the Explore or agent you point it at bounds a single conversation. A question that spans sources, or reaches outside what is modeled, falls out of scope. Orion is built to reason across the warehouses you connect directly, in a single investigation. A question that touches Snowflake and BigQuery together is one analysis. If your data and questions live beyond what Looker models, that is the difference.

Not for the people asking. Orion connects read-only to the warehouses you already run and reads your LookML through one dedicated Looker user, with optional read-only repository access. Everyone else gets value from that semantic layer without a Looker account of their own, and without an admin enabling Gemini in Looker. Looker Conversational Analytics runs inside Looker with Gemini in Looker enabled, or in an app your team builds on Google's Conversational Analytics API. Orion is a way to let the LookML you have already invested in pay off for people and questions that live outside the Looker instance. And if part of your stack has no LookML at all, Orion builds a semantic layer for it from your warehouse metadata and Knowledge Base.

Rooms lets a team share one conversation with Orion. The data lead and the marketer work the same thread, and attribution tracks who asked what. Each participant acts at their own access level. Viewers can follow without being able to query. Chat in a BI tool answers one person at a time. A Room makes the conversation itself the shared workspace, with Orion in it.

Orion connects read-only through dedicated least-privilege service accounts per source. For Looker that is a dedicated analyst-level user, and on BigQuery it can run queries as the asker through per-user OAuth. Credentials are encrypted at rest, and two independent layers of roles, tenant and group, scope who sees which projects, data sources, and Knowledge Base pages. Every analysis is captured in a notebook: the instructions, the logic, and the code, so you can see how each answer was produced.

Keep Looker either way. This is not a rip-and-replace decision. Looker Conversational Analytics is the right fit if your questions live inside what LookML models. It also fits when your users are in the Google ecosystem and want conversational Q&A over governed Explores. Orion is the better fit if you want the same LookML working proactively across your whole stack. That means changes detected and investigated beyond a single triggering metric. It also means dashboards and reports built from a request, and answers delivered to Slack or email, including to people who never open Looker. Many teams run both: Conversational Analytics for in-Looker questions, Orion for the investigation and delivery layer on top.

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, and coexistence is the natural starting point. You keep Looker and Conversational Analytics as the governed, in-Looker analytics your team relies on. You add Orion as the analyst. Orion reuses the same LookML, watches metrics across the whole stack, investigates what changed, and delivers the answer to Slack or email. What tends to happen from there: as Orion answers more of the day-to-day questions, fewer of them start with someone opening a dashboard. Because Orion already understands your LookML, how much you lean on each tool stays your decision, on your timeline, with no forced migration.

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