Snowflake gives data teams a place to store, process, and govern company data. The harder part is making that data useful across the business, especially when an answer depends on company terminology or information that still lives in operational tools outside the warehouse.
Supper connects directly to Snowflake so employees can ask questions, investigate the results, and work with data from other connected sources. Snowflake remains responsible for the warehouse and its native governance. Supper uses that environment alongside the company's business definitions and other data sources to answer questions in context.
For Snowflake customers, getting started is fairly light. After the company provisions a system account, Supper can inspect the permitted data, build an initial semantic model, incorporate existing Semantic Views, and create an evaluation corpus for testing. Users can typically begin asking questions within one or two days.
The roles of Supper and Snowflake
Companies use Snowflake as a data platform for warehousing, processing, sharing, and governing company data. Its native AI capabilities include Cortex Analyst and Cortex Agents, which can answer natural-language questions and complete multi-step work within Snowflake.
Supper gives a wider group of employees a way to work with Snowflake data. Users can ask questions conversationally, continue an analysis through follow-up questions, create charts, and save recurring work as reusable Skills. Supper can use SQL to query Snowflake and Python when an analysis requires calculations or additional processing.
Many business questions also depend on context that is difficult to capture in warehouse definitions alone. The meaning of a metric may depend on an internal rule, an exception to the normal calculation, or information maintained in another system. Supper keeps that context in its semantic model and applies it when answering questions.
Connecting Supper to Snowflake
A company begins by provisioning a Snowflake system account for Supper. Supper uses that account to inspect the database structure available to it, create the initial semantic model, incorporate any available Semantic Views, and generate an evaluation corpus that tests how well the model answers questions.
The customer's part of the initial connection generally takes about five minutes. Supper completes the automated setup and testing in the background, with users typically able to begin asking questions within one or two days. The exact timeline depends on the size and condition of the Snowflake environment.
Supper queries Snowflake directly. The company can keep its existing warehouse architecture and data pipelines, while Snowflake continues to process the warehouse queries.
The semantic model develops further after launch. Questions from real users expose missing definitions and areas where Supper needs more context. Supper can identify many of these issues automatically, while decisions involving company-specific business rules remain with the customer. During the first month, this review usually requires anywhere from a few minutes to a few hours of customer involvement.
How Supper uses Snowflake Semantic Views
Snowflake Semantic Views define business concepts and the relationships needed to query them. They give Cortex Analyst context about what the data means and how it should be used.
Supper can ingest Semantic Views as part of its own semantic model. If the company's Snowflake definitions are complete and trusted, Supper can accept them as the approved definition of the data. If the existing coverage needs more context, Supper can use it as a starting point and supplement it.
Supper's semantic model can include company terminology, calculation instructions, known data issues, source-selection guidance, and reusable analytical processes. This knowledge is maintained at the model level so it can be applied consistently across users and questions.
This also saves data teams from rebuilding semantic work they have already completed in Snowflake. Semantic Views can carry into Supper, while Supper can maintain the additional business context needed for analysis across the company's broader data environment.
Analysis across Snowflake and SaaS data
Important business information often remains spread across operational applications. A company may keep historical transaction and product data in Snowflake while the latest account activity remains in its CRM.
Take customer retention as an example. Answering the question may require revenue and product usage from Snowflake, along with current customer activity from the CRM. Supper can determine which sources are relevant, query them according to the user's permissions, and apply the company's definition of an at-risk customer.
The person asking the question does not have to know where each field lives or how the systems relate. They can ask the business question directly and continue investigating the result within the same conversation.
This lets teams analyze Snowflake alongside connected SaaS applications. It also means that a useful answer does not have to wait until every relevant field has been loaded into Snowflake and modeled there.
Permissions and administrative oversight
Snowflake enforces the privileges and data-protection policies attached to the system account and role used by Supper. Because Supper connects through a system account, it does not automatically assume each employee's individual Snowflake identity.
Snowflake makes parts of its access-control structure available through account metadata, including role privileges in GRANTS_TO_ROLES and supported policy assignments in POLICY_REFERENCES. When the Supper system account can read the relevant metadata, Supper can use that information as an input to its entitlement model. Administrators can then apply Supper's user-level allow and deny rules across Snowflake and connected SaaS sources.
Administrators can review how people are using company data through Supper's Admin Control Center. They can see the questions being asked, investigate how data is being accessed, and review issues found in the semantic model. This gives the data team continued oversight after AI analytics has been made available to the wider company.
When should a Snowflake customer use Supper?
Snowflake Cortex may cover the use case when analysis remains within Snowflake and the intended users already work comfortably in its environment.
Supper is relevant when a company wants to make Snowflake data accessible through a dedicated business analytics product or when questions regularly depend on information outside the warehouse. It also gives the data team a place to manage company-specific definitions and review how employees use AI to access data.
Supper uses seat-based pricing. The cost is tied to the number of users rather than the number of tokens consumed during an analysis, making costs easier to anticipate when questions require several analytical steps.
Frequently asked questions
Does Supper replace Snowflake?
No. Snowflake continues to provide the warehouse, compute environment, and native data governance. Supper connects to Snowflake and gives business users another way to analyze the data stored there.
How does Supper connect to Snowflake?
The customer provisions a Snowflake system account for Supper. Supper uses that connection to inspect the permitted database structure, build its initial semantic model, run evaluations, and query Snowflake directly.
Does Supper copy Snowflake data into another warehouse?
Supper queries Snowflake directly for warehouse analysis. Companies do not need to migrate or duplicate their Snowflake warehouse before they can begin using Supper.
How is Supper different from Snowflake Cortex Agents?
Cortex Agents provide a managed platform for creating and running agents within Snowflake. Supper provides a business-facing analytics experience with its own semantic model, reusable Skills, administrative controls, and support for questions that use Snowflake alongside connected SaaS sources.
Can Supper use existing Snowflake Semantic Views?
Yes. Supper can ingest Snowflake Semantic Views and treat them as approved definitions or use them as inputs that can be supplemented with additional business context. The company chooses the approach that matches its existing Snowflake model.
How long does the Snowflake setup take?
Provisioning the connection generally takes about five minutes of customer work. Supper can usually prepare the initial semantic model and evaluation corpus within one or two days, after which users can begin asking questions.