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All the
credit.

None of
the sprints.

Rated #1 in embedded analytics.
Take the credit. We won't tell.

Focus your team's time, tokens, and talent on what sets your product apart. ThoughtSpot Embedded handles the analytics guts so you get all the glory.

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Powering Intelligent Apps Everywhere

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TS Customer Thrive
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Gartner

2026 Leader - Gartner® Magic Quadrant™ for Analytics & BI

G2

Leader - G2 Grid for Embedded Business Intelligence

Nucleus

2026 Leader - Nucleus Research Embedded Analytics Value Matrix

Big impact. Built right in.

Hear how customers are bringing powerful AI analytics into their products with ThoughtSpot Embedded.

Thrive Learning customer

Thrive Learning

Shipping AI in days, not quarters.

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Onlyfy by XING customer

Onlyfy by XING

The case for owning your domain, not your dashboards.

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Endpoint customer

Endpoint

Shifting your ROI focus to customer outcomes.

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Intuitive customer

Intuitive

How to deliver accuracy without the maintenance tax.

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All the credit. None of the

tech debt.token bill.heavy lifting.

Building a feature takes a weekend. Owning it takes forever: every upgrade, schema change, and model update lands on your team. ThoughtSpot owns the analytics layer, so your roadmap stays yours.

See How It Works
Woman leaning on stack of retro monitors

All the credit. None of the

tech debt.token bill.heavy lifting.

AI makes building faster and easier, but every token spent on analytics is a token taken from your core product. ThoughtSpot doesn't charge for tokens, so your AI budget goes where it differentiates you.

See How It Works
Man in velvet suit wearing a crown

All the credit. None of the

tech debt.token bill.heavy lifting.

Multi-tenant security, semantic layers, query performance: the scary stuff. It's infrastructure nobody sees, and everybody depends on. We carry it, so your best engineers don't have to.

See How It Works
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Learn more about embedded intelligence

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The case for building the right things

Everyone is building with agentic AI, but only the winners know what not to build. Focus your team, tokens, and talent on what differentiates you, and trust ThoughtSpot to embed the rest.

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Where your AI budget should actually go

Learn how to manage the cost of building and owning AI analytics features, so you can minimize your token bill and maximize your profit margin.

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Embedded analytics calculator

Take this 2-minute assessment to see what owning analytics in-house really costs, and how much engineering time you could redirect to your core product.

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Frequently asked questions

1. What is ThoughtSpot Embedded Analytics?
ThoughtSpot Embedded enables SaaS companies to integrate agentic analytics, interactive dashboards, and AI-powered insights directly into their applications. With embedded agents and natural-language data exploration, your customers can ask questions about their data, identify trends, and explore business metrics without switching between tools.
2. Can I white-label embedded analytics with ThoughtSpot?
Yes, ThoughtSpot Embedded offers granular customization options that help you align the analytics experience with your application's branding and user interface. Depending on your configuration, you can customize themes, colors, text, fonts, and embedded components to match your product's design.
3. How does ThoughtSpot Embedded pricing work? Are there token-based costs?
ThoughtSpot Embedded offers pricing options designed to meet different product requirements and deployment needs. ThoughtSpot does not meter or charge for LLM tokens, so you don't pay separately based on token consumption. Your platform usage is governed by your subscription. If you use your own LLM provider, additional fees from that provider may apply. Visit the ThoughtSpot pricing page to explore available options.
4. How quickly can I integrate and launch embedded analytics into my SaaS application?
ThoughtSpot Embedded provides the Visual Embed SDK & REST APIs, MCP, and developer tools, including SpotterCode, an AI coding agent to help you integrate analytics into your application. A proof of concept may take less time than a production deployment, which requires additional work on data modeling, authentication, access controls, testing, and UI customization. Your timeline depends on your application's architecture, data readiness, and integration requirements.
5. How does ThoughtSpot Embedded improve the accuracy of AI-generated answers?
ThoughtSpot uses a governed semantic layer to give AI a consistent understanding of business metrics, relationships, and definitions. This helps Spotter interpret questions in the context of your business data and return answers based on defined business logic. Answer quality still depends on data quality, semantic modeling, and query interpretation, so testing and validation remain important for delivering reliable AI-generated insights.
6. Is ThoughtSpot Embedded secure for multi-tenant SaaS applications?
Yes. ThoughtSpot Embedded supports multi-tenant architectures with authentication and access-control capabilities, including row-level security. You can configure permissions to restrict users to the data they're authorized to access. Your implementation should also account for tenant isolation, authentication, and access policies so customers can access only their permitted data.
7. Which data platforms does ThoughtSpot Embedded support?
ThoughtSpot Embedded lets you integrate analytics into your application using data connected to ThoughtSpot. Supported data platforms include Snowflake, Amazon Redshift, Google BigQuery, Databricks, ClickHouse, and other cloud data warehouses and databases. Available connectors and capabilities depend on your data source and configuration.
8. How can I embed AI-powered analytics into my SaaS application?
You can integrate ThoughtSpot's analytics experiences into your SaaS application using the Visual Embed SDK, REST APIs, and MCP. Depending on your requirements and product configuration, you can embed agents, dashboards, visualizations, search, and supported AI-powered analytics experiences. Implementation typically involves connecting your data, configuring authentication and access controls, selecting the components to embed, and integrating them into your application's interface. This process can be accelerated with SpotterCode,ThoughtSpot's AI coding agent.
9. How does ThoughtSpot Embedded compare to building an analytics solution in-house?
Building analytics in-house gives your team direct control over the architecture, user experience, and feature development, but it also requires ongoing investment in AI and model updates, semantic layers, query processing, security, and overall maintenance. ThoughtSpot Embedded provides analytics capabilities, embedding tools, and AI-powered data exploration that can reduce the need to develop and maintain these components from scratch. The right approach depends on your customization requirements, existing infrastructure, engineering capacity, and long-term product roadmap.
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