Terra AI value-based pricing

This pricing model turns Terra AI from a software line item into part of the capital decision itself. When a mining or reservoir team is choosing where to drill, how many wells to test, or whether a site is bankable, the economic stakes sit in avoided dry holes, faster delineation, and safer development plans, so pricing can map to project scope and expected ROI instead of user seats. The high touch deployment model reinforces that logic because customers are buying better decisions on a live asset, not just logins to a tool.

• The product sits directly in expensive workflows. Terra ingests drill, seismic, geochemistry, and reservoir data, builds many plausible 3D subsurface models, then recommends the next drillhole, survey, or development scenario. That makes the natural budget owner a project or asset team with capex at risk, not an IT seat budget.
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• Comparable vendors show why value based pricing works here. VRIFY sells exploration software to 26 clients, while KoBold and VerAI capture upside through asset ownership or JV economics. Terra sits between these models, selling software fees against decisions that can move project value by millions without needing to own the asset itself.
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• Incumbents like Seequent, SLB, and Halliburton are embedded workflow systems, so Terra is easiest to sell as a premium layer on top of existing geology and reservoir tools. OMV's work with Terra on CCS decision making shows the product being used inside a real operating workflow where development choices matter more than per user access.
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Over time, this pushes Terra AI toward fewer, larger accounts with recurring expansion inside each asset program. As the company productizes more of the deployment work and proves outcomes across mining, geothermal, and CCS, pricing should move further toward annual platform agreements tied to portfolio level decision support, while keeping project based services as the wedge into each new operator.