ThoughtSpot Cloud Documentation
ThoughtSpot Cloud™ is our hosted and managed Software as a Service (SaaS) offering. ThoughtSpot Cloud is available on Amazon Web Services (AWS) and Google Cloud Platform (GCP). Customers can choose the cloud and region where they would like their ThoughtSpot Cloud service deployed.
ThoughtSpot Cloud offers multiple advantages over deployment form factors that you have to manage and maintain within your own organization.
Find topics for the common types of ThoughtSpot users.
What’s new in ThoughtSpot Cloud
November 2025 10.14.0.cl
| Features marked as Beta are off by default. To enable them, contact ThoughtSpot support. |
New navigation and homepage coming soon!ThoughtSpot is excited to announce that our brand-new navigation and homepage experience will be enabled by default for all users in the upcoming 10.15.0.cl release. This change does not apply to users of ThoughtSpot in an embedded environment. Highlights of the new navigation and homepage
Starting in 10.15.0.cl, the classic navigation and homepage experience will no longer be available. This change only applies to ThoughtSpot Cloud customers and does not impact ThoughtSpot Embedded customers. For more information, see New homepage and persona-based navigation. |
November 2025 10.14.0.cl updates
Custom sorting on Answers
Custom sorting on Answers is now on by default and available to all users. You can now define and modify a custom sort order for attributes directly in an Answer. Sort order defined in the Answer overrides the sort order defined in the Model for that particular Answer.
For more information, see Define or edit a custom sort order for an Answer.
Other features and enhancements
Spotter coaching access
You can now delegate Spotter coaching responsibilities without granting data model editing permissions. This feature is available for all data model editors and administrators. From the More menu
on any Spotter-enabled data model, you can now grant the "Spotter Coaching Access" to other users or groups. Providing this access enables these users to manage all aspects of Spotter’s coaching, including the ability to view, modify and promote coaching from all users on that data model.
For more information, see Grant coaching access.
Spotter context in reference questions
Adding Spotter context in reference questions is now on by default and available to all users. Rather than simply coaching Spotter to recognize search tokens in reference questions, you can now improve accuracy and generalize the coaching by adding context, natural language explanations of how to apply these tokens in similar queries. For example, you could tell Spotter to always add 'user name', 'user id', and 'user role' when asked about the list of users.
For more information, see Context in Spotter.
Data model instructions Early Access
You can now teach Spotter core concepts, default behaviors, and specific data nuances by adding natural language instructions directly to a data model. These standalone directives act as global rules for Spotter to follow. For example, you can create a rule that makes Spotter automatically apply a "last 30 days" date filter if a user’s query doesn’t specify a time frame. To enable this feature, contact your administrator.
For more information, see Natural language instructions in Spotter.
AI context for Models Beta
You can now add AI context to columns in any Model where you have Can Edit access. This context helps Spotter better interpret a column and its values, using this knowledge to differentiate between similar columns and answer your data questions more accurately. You can auto-generate this context automatically with a single click to get started.
Unlike Spotter context added to reference questions, which helps with similar queries, AI context for each column helps Spotter choose the correct columns and values for all queries. To enable this feature, contact ThoughtSpot support.
For more information, see AI Context.
Bring your own LLM Key
In addition to selecting OpenAI Azure and Google Gemini as the LLM for your Spotter experience, you can now connect your own LLM by providing the LLM Key for one of the supported providers (Azure, Vertex) or via your own gateway. This option allows you to exercise full control over your AI stack, by connecting ThoughtSpot directly to your private LLM endpoints.
Tenant-based column aliasing Early Access
Tenant-based column aliasing introduces the capability to define a column name or description based upon the user’s membership in a ThoughtSpot Org or group. For example, Client 1 may refer to a column as Region, whilst Client 2 may refer to this as State. This feature is an extension of the language-based aliasing. Data analysts can define these aliases via CSV upload or with TML. This feature is particularly useful for embedded scenarios where the same underlying Model is leveraged across multiple clients.
For more information, see Tenant-based column aliases.
Progressive Model filters
Progressive Model filters are now available to all users and on by default. Previously, any filter defined at the Model level applied to every query based on that Model. Now, you can define which table a filter applies to in the Model TML. Once defined, the filter only applies to queries that include columns from that specific table.
For more information, see TML for Models.
Atlas data inclusion in AI and BI system Liveboard
The AI and BI system Liveboard now features group-level filters and a Group tab with visualizations like Group Champions, Most Popular Data Model, and Group Breakdown. Provisioned user information, including both active and inactive users, is now a primary KPI, and replaces the earlier Daily Active Users KPI.
For the Developer
For new features and enhancements introduced in this release of ThoughtSpot Embedded, see ThoughtSpot Developer Documentation.