Most Of The Cost Is Hidden
Semantic layer, security, governance, and iteration are pricey. Embedding inside your product (SDK, multi-tenancy, auth white-labeling) adds a whole other layer.
Embedded Analytics Estimator
How Much Would
Building Analytics In-House Cost You?
The best product leaders put their teams' time, talent, and tokens toward building what sets them apart—not analytics infrastructure. Uncover the true price of maintaining embedded analytics in-house, and what you could achieve by spending those resources on what differentiates you instead.
4 questions
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See what building it yourself would really cost.
Enter your business details to reveal the full 18-month estimate, including hidden production costs, annual ownership costs, and the engineering capacity you could return to your roadmap.
Your Estimate
Your Cost To Build And Run Embedded Analytics In-House For 18 Months
~$0
0 eng-years
Capacity Taken From Your Roadmap
Owning the analytics foundation takes resources from your core product. Reclaim it and focus on building differentiated customer experiences instead.
Ongoing Ownership Never Stops
~$0/yearMaintenance, infrastructure, and AI inference remain after launch and increase as adoption grows. Costs rise steeply at enterprise data volumes.
AI Inference Adds Up Fast
~$0In-House Path
Build Everything Yourself
Timeline~0 months
Team0 engineers
Engineering Effort0 eng-years
FoundationYou Own It Forever
Your team owns the analytics layer, embedding, governance, security, infrastructure, and maintenance.
VS
★ Recommended Path
Build With ThoughtSpot Embedded
Timeline~3.5 months
Team~3 fractional people
Engineering Effort0.4 eng-years
FoundationThoughtSpot Owns It
Same outcome and same units. Directional comparison based on ThoughtSpot customer implementations.
MIT finds teams that buy from a specialist succeed about 67% of the time, versus 33% for internal builds.
