In today's data-driven SaaS landscape, providing robust analytics capabilities has shifted from a competitive advantage to a baseline expectation. For product teams, this raises a critical question: should we build analytics capabilities in-house or embed a vendor solution?
At first glance, building seems straightforward, but the true costs extend far beyond initial development estimates. When time-to-market, opportunity cost, and long-term scalability are factored in, the equation tilts heavily in favor of buying.
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For many teams, the idea of building analytics in-house starts with a seemingly simple dashboard requirement. A developer or two, a few sprints, and you're done. However, this underestimates the scope, complexity, and ongoing demands of incorporating analytics capabilities into your products.
Visible costs include salaries for multiple specialized roles, such as BI developers, data engineers, UX designers, and product managers.
However, beyond salaries, a significant opportunity cost is often overlooked: redirecting engineering resources to analytics development prevents advances in your core product differentiators. This diversion of talent can significantly impact your primary product development roadmap.
Even if you allocate sufficient resources, timelines are almost always underestimated. A "quick build" often turns into a multi-quarter initiative. By the time it's ready, market needs may have evolved.
The iceberg of hidden costs
The less visible costs are the ones you don’t see coming—and prove to be the most damaging. Building analytics is not a one-and-done project. The real complexity appears post-launch:
Maintenance: Changing data sources and schemas
Compliance: Adapting to evolving security, governance, and compliance standards
Optimization: Performance optimization as data volumes grow
User Experience: Continuous iteration based on customer feedback
Advanced Features: Agentic conversational experiences, natural language search, AI-driven insights, mobile responsiveness, and more
These complex areas require specialized skills (and in recent times, we can throw AI skills into the mix too) that often don’t exist in your core team. What begins as a small add-on can spiral into a major long-term engineering commitment.
“You’re not just embedding a technology. What you’re actually doing is bringing onboard the ThoughtSpot team with all its experience, technical skill, and all the work they have already done,” said Donald Farmer, Principal at TreeHive Strategy. “By embedding a powerful AI-enabled application like ThoughtSpot, what you’re actually doing is not just embedding a technology, but all the experience, insight, knowledge, and best practices that comes with that.”
Modern embedded analytics platforms, such as ThoughtSpot Embedded, solve these problems out-of-the-box. What could take 12–18 months to build internally is often delivered in weeks with a partner.
“It was a very quick process for us,” said Duessa Holscher, Principal Product Manager at Act-On. “We embedded it and developed our core data model and dashboards in about 30 days. Then we expanded it and released it in GA for all of our clients within 90 days.”
Buying provides the benefits of embedded analytics instantly:
Agentic interfaces for true self-service analytics
AI-powered insights with actionable recommendations
Enterprise-grade security and compliance baked in
Scalable architecture that grows with your product
And perhaps most importantly, your product and developer teams stay focused on what makes your product great.
To truly compare Build vs. Buy, consider more than just upfront costs. You also need to consider:
Time-to-value: How quickly can you deliver analytics capabilities to customers?
Feature velocity: How rapidly can you evolve your analytics offerings as market expectations change?
Customer retention: Will improved analytics increase renewal rates?
Revenue potential: Are analytics a driver of upsells or a completely new revenue stream?
In fact, many SaaS companies find that embedded analytics helps:
Improve NPS and retention by empowering end users with self-service insights
Open new revenue streams through premium analytics tiers
“One of my favorite ways we’re using NLP is in our reporting through ThoughtSpot, where we overlay the capability,” said David Mayer, Global Director of Data, Analytics & Quality at Hyatt. “Instead of having to write a query or mess around with parameters on a report, a customer, for example, a hotel owner or operator can simply ask a question. Let’s say a hotel general manager (GM) wants to know how their property performed during the same period last year. They can just use plain language to ask that question and the results will come up.”
Ask yourself:
Is analytics a core differentiator for your product, or a supporting capability?
Do you have the in-house expertise for continuous analytics innovation?
How fast must you move to stay competitive?
What level of flexibility and customization will your customers expect?
For many, the calculus increasingly favors embedded solutions. This trend reflects the growing recognition that partnering with analytics specialists often yields better results more quickly than building from scratch.
ThoughtSpot Embedded allows you to deliver world-class analytics experiences with minimal code and maximum impact. From agentic analytics experiences to intuitive, interactive dashboards, ThoughtSpot helps you:
Empower customers with true self-service analytics
Maintain enterprise-grade performance and governance
Accelerate your time-to-market and reduce engineering burden
Create new revenue streams
Leapfrog your competition
“We are on this journey to transform and define the future of healthcare compliance and revenue integrity,” said Ritesh Ramesh, CEO at MDAudit. “ThoughtSpot Embedded has been the execution tool for us to enable that outcome.”
Final thoughts: Focus on what makes you unique
Your core product is your competitive advantage. Analytics should amplify its value, not distract from it.
By partnering with specialists like ThoughtSpot, you gain sophisticated capabilities your customers demand, while keeping your team focused on building the future of your product.
Want to learn more about evaluation criteria for embedded analytics and discover how quickly you could go live?
