FinOps and AI cost

Cost visibility that leads to better engineering decisions.

Potacium helps teams understand where cloud and AI spend is going, which costs are avoidable, which commitments are safe, and how to keep optimization from becoming a one-time cleanup exercise.

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Where Potacium Helps

Cloud cost issues rarely live only in the bill. They show up in architecture, deployment habits, data movement, storage lifecycle, query patterns, tagging, ownership, forecasting, and the way finance and engineering talk to each other.

Potacium connects those signals into a practical FinOps operating model, including AI workload and inference spend where usage can grow quickly and accountability is still forming.

Typical Outcomes

  • Cloud and AI spend review with waste, risk, and decision points separated.
  • Rightsizing, commitment, storage, transfer, database, and workload recommendations.
  • Allocation model for teams, products, environments, and business units.
  • Forecasting, anomaly detection, and recurring optimization cadence.

Engagement Scenarios

This work is useful when spend is rising faster than revenue, AI usage lacks controls, teams disagree on ownership, commitments feel risky, or finance needs a clearer view of unit economics.