Question 1

We're a Snowflake shop already. Do we need anything besides Cortex to get proactive analysis?

Accepted Answer

For proactive analysis, yes: the part that comes to you is what Orion adds. Cortex Analyst turns a question into SQL over a semantic model and answers when asked. That is useful, but you still have to know which question to ask. Orion adds the proactive layer. It watches your metrics and auto-detects the significant changes you did not think to ask about. It investigates the root cause and delivers the write-up before anyone requests it. So even on Snowflake, Cortex answers the questions you remember to ask. Orion also catches the ones you did not, which is where the unknown unknowns a stretched data team misses actually live.

Question 2

We've built our semantic layer in LookML and dbt. Does Cortex reuse it?

Accepted Answer

No. Cortex Analyst runs on a semantic model you author specifically for Cortex. That is either a semantic view built in Snowflake with SQL or the Snowsight wizard. It can also be a YAML model file that maps logical tables and metrics to your Snowflake tables. Snowflake can run your dbt projects, but Cortex's semantic model is still authored for Cortex. The docs document no LookML import at all. The business logic you already maintain gets re-created and maintained a second time. Orion instead reads the LookML and dbt you already trust where they live, as the governed logic behind every answer. A cited Knowledge Base adds the context that is not in the model. Nothing to port, and no second copy to keep in sync. And if you have no semantic layer, Orion builds one from your warehouse metadata and Knowledge Base.

Question 3

Our data isn't only in Snowflake. Can Cortex reason across BigQuery and Databricks too?

Accepted Answer

Only once the data lands in Snowflake. Cortex's analytics run within your Snowflake account, and Snowflake can widen what that account sees. Catalog-linked databases sync external Iceberg catalogs, including Databricks Unity Catalog. Openflow ships CDC connectors that copy Postgres, MySQL, SQL Server, Oracle, MongoDB, and BigQuery data into Snowflake. But every route runs through Snowflake, so the data has to land there before Cortex can reason over it. Orion is built the other way around. It connects read-only across the warehouses and sources you already run. It can investigate a question that spans Snowflake, BigQuery, and Databricks together in a single analysis. If your reality is more than one warehouse, that difference decides whether you get the whole picture or one vendor's slice of it.

Question 4

Does Cortex proactively tell us when a metric moves, or do we have to ask?

Accepted Answer

Mostly you have to ask. Cortex Analyst is request-response. Snowflake also ships an anomaly-detection ML function. But it is a separate SQL feature you build and operate yourself, not wired into Cortex Analyst chat. CoWork's subscription-driven briefs and anomaly alerts are the closer analogue, plus an Automations feature Snowflake labels public preview soon. Orion closes that gap: it auto-detects the significant changes in your metrics, runs the root-cause analysis, and delivers the write-up to Slack or email. A Workflow adds the scheduled version, judging whether the numbers are worth flagging and routing the result to the right people. So Cortex answers the questions you ask. Orion also surfaces the ones you did not.

Question 5

How do the numbers stay trustworthy, and does Orion respect our access controls?

Accepted Answer

Orion and Cortex both ground their answers in governed logic. Cortex generates SQL against the semantic model you built in Snowflake, and honors Snowflake's role-based access and row-access policies. Users see only what they are cleared to see. Orion grounds every answer in the semantic layer you already trust, like your LookML and dbt definitions, plus a cited Knowledge Base. Every analysis is captured in a notebook holding the instructions, the logic, and the code. Anyone can check how an answer came to be. It connects read-only through a dedicated least-privilege service user, and 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 re-create inside one warehouse.

Question 6

Can our whole team work with Orion at the same time, or is it one person per chat?

Accepted Answer

Rooms is one shared conversation between your team and Orion. Everyone asks in the same thread, and every message is attributed. Each person works at the access level they already have, so sharing the investigation never means widening access. A per-user chat answers one person at a time. A Room holds the whole team's investigation, analysts and business users together, with Orion in it.

Question 7

Does adopting Orion mean moving data or standing up a new tool?

Accepted Answer

No. Orion is warehouse-native and read-only. It connects to the warehouses you already run and queries them in place. There is nothing to load or migrate first. Cortex, by contrast, is only available once your data is in Snowflake. For anything not already there, adoption means loading it into Snowflake and authoring a Cortex semantic model first. With Orion you point it at what you have, it reuses your existing semantic layer, and you start getting answers without a re-platforming project.

Question 8

What is Snowflake CoWork, and how does it compare to Orion?

Accepted Answer

CoWork is Snowflake's agentic AI experience, renamed from Snowflake Intelligence in June 2026. Its Deep Research feature investigates across your data estate and returns cited reports. It is the right mental model for what Snowflake-native AI looks like today. Two structural differences remain. CoWork runs where your Snowflake data lives, on a semantic model you author there, and reaches tools like Slack, Gmail, and Jira through MCP connectors. Orion connects read-only across Snowflake, BigQuery, Databricks, and Redshift and reuses the LookML and dbt you already maintain. And on proactivity, Snowflake's page describes scheduled briefs and anomaly alerts through subscriptions, plus an Automations feature it labels public preview soon. Orion watches your metrics and delivers the write-up without anyone asking, today.

Question 9

Snowflake bills for the compute its AI uses. Does that change who is incentivized to keep it efficient?

Accepted Answer

It does, and it is worth understanding before you commit. Snowflake sells consumption, so the compute an AI feature burns is revenue for the vendor. Heavier usage is not a problem that model has to solve. Orion is priced per deployment, with no per-message meter. Every extra query Orion runs and every extra token it spends is our cost rather than yours. That gives us a direct reason to reach the answer in as few queries as we can, and to keep getting better at it. Neither model is dishonest. They simply pull in opposite directions, and the direction matters more the more your team actually uses the thing.

Question 10

Can we embed Orion in our own product?

Accepted 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.

Question 11

Can Orion and Snowflake Cortex work together, or should we switch?

Accepted Answer

Yes. Orion connects read-only to Snowflake alongside your other warehouses. It uses the data and structure you already have in Snowflake, and also reaches everything that lives outside it. Teams keep Cortex for in-Snowflake, ask-and-get-SQL questions. They add Orion as the analyst. Orion watches the whole stack, investigates what changed, and delivers the answer without anyone opening Snowsight. You are not choosing one or the other. Orion adds proactive, cross-warehouse investigation on top of what Cortex does inside Snowflake.

	Orion	Snowflake Cortex
Proactive investigation	Auto-detects significant changes in your metrics and writes up why they moved	Cortex Analyst answers when asked. CoWork schedules briefs and anomaly alerts by subscription^↗
Everyone in one live conversation	Everyone 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 copies	Each person gets their own thread. A shared conversation is a static snapshot, and follow-ups start a new thread^↗
Reuses your semantic layer	Reads the semantic layer you already trust, like LookML or dbt, in place	Build and maintain a Snowflake semantic view or YAML model, authored for Cortex^↗
LookML ingestion	Reads your LookML where it lives and converts it into governed metric definitions	No LookML import documented^↗
Context from your existing docs	A cited Knowledge Base synced from GitHub, Confluence, or Notion	Semantic views you author. The Cortex Search integration finds literal column values, not documents^↗
Works across warehouses	Connects read-only across Snowflake, BigQuery, Databricks, and Redshift, and reasons across them in one analysis	Works within your Snowflake account. External Iceberg catalogs can sync in via catalog-linked databases^↗
Runs inside your warehouse perimeter	Connects from outside, read-only, through a least-privilege service user	AI runs in your Snowflake account and honors Snowflake RBAC and row-access policies natively^↗

Orion vs Snowflake Cortex

TL;DR

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