AI FinOps is applying FinOps practices to AI spend
AI has become a mainstream technology investment as 98% of FinOps teams now manage AI spend, up from 31% just two years ago. However, managing AI cost is only the beginning—the real question is whether AI investments are creating measurable business value. FinOps helps organizations answer that question by connecting AI spending to strategic outcomes, quantifying returns, and ensuring every AI initiative earns its place in the portfolio.
The FinOps Framework provides the cross-functional collaboration, data-driven visibility, and governance needed to do this at scale. Community-generated Working Group resources teach FinOps practitioners to evaluate AI investments and govern AI spend. In addition the Tokenomics Foundation covers AI value from a more technical and broader perspective from the supply chain to monetization.
September 2, 2026
In this FinOps X 2026 lightning talk, Chinni Krishna Abburi of Communities Foundation of Texas tells a concrete, numbers-driven story: a vendor quote for $165,000 a year to modernize their analytics platform, every line item completely defensible (F64 capacity, Power BI Pro licensing, structured training rollout, OneLake storage), and a two-person team that cut it to $65,000 with the exact same data, report, and output by simply right-sizing capacity to F16 with F32 pay-as-you-go for peak weeks.
September 2, 2026
In this FinOps X 2026 lightning talk, Aaron Soto of SHI builds his case around a framework from MIT economist Dr. Sendhil Mullainathan: AI is excellent at closed-world problems, well-understood, data-rich domains like cloud pricing, while humans remain essential for open-world problems, the vague, ill-formed questions that need to be framed before they can even be solved.
September 2, 2026
In this FinOps X 2026 lightning talk, Dippu Kumar Singh of Fujitsu North America opens with the fitness-tracker problem: the best tool in the world doesn't change your habits, and FinOps has the same issue. Tooling keeps improving while old habits stay put.
August 25, 2026
Kafka and Flink costs resist standard rightsizing. Apply the four FinOps Framework Domains to streaming and real-time data platforms to improve value-to-cost.
August 24, 2026
In this FinOps X 2026 lightning talk, Cassio Melo of Meilynx tackles the harder half of AI ROI: not "how much did we spend" but "what did we actually get for it," when cost data lives in cloud billing and value data lives in product analytics or a CRM.
August 25, 2026
Kafka and Flink costs resist standard rightsizing. Apply the four FinOps Framework Domains to streaming and real-time data platforms to improve value-to-cost.
August 10, 2026
Token Economics, or Tokenomics, is the emerging practice of managing the production, consumption, and monetization of token-based AI to generate business outcomes and value. It exists to answer one question, asked across the entire AI lifecycle: is AI driving value?
August 10, 2026
Introducing The Five-Layer Tokenomics Stack. What AI costs is decided in the lower layers. How much AI spend is decided in the upper ones. L1 · L2 · L3 · L4 · L5 silicon.
May 7, 2026
Practical AI for FinOps use cases and prompts mapped to Framework capabilities and designed for cost optimization, reporting, budgeting, and policy workflows.
April 23, 2026
A FinOps practitioner's guide to understanding AI tooling costs across the full lifecycle, from training and tuning to inference, orchestration, and operations.
September 2, 2026
In this FinOps X 2026 lightning talk, Chinni Krishna Abburi of Communities Foundation of Texas tells a concrete, numbers-driven story: a vendor quote for $165,000 a year to modernize their analytics platform, every line item completely defensible (F64 capacity, Power BI Pro licensing, structured training rollout, OneLake storage), and a two-person team that cut it to $65,000 with the exact same data, report, and output by simply right-sizing capacity to F16 with F32 pay-as-you-go for peak weeks.
September 2, 2026
In this FinOps X 2026 lightning talk, Aaron Soto of SHI builds his case around a framework from MIT economist Dr. Sendhil Mullainathan: AI is excellent at closed-world problems, well-understood, data-rich domains like cloud pricing, while humans remain essential for open-world problems, the vague, ill-formed questions that need to be framed before they can even be solved.
September 2, 2026
In this FinOps X 2026 lightning talk, Dippu Kumar Singh of Fujitsu North America opens with the fitness-tracker problem: the best tool in the world doesn't change your habits, and FinOps has the same issue. Tooling keeps improving while old habits stay put.
August 24, 2026
In this FinOps X 2026 lightning talk, Cassio Melo of Meilynx tackles the harder half of AI ROI: not "how much did we spend" but "what did we actually get for it," when cost data lives in cloud billing and value data lives in product analytics or a CRM.
August 24, 2026
Abhinav Lad and Kuntal Patel of Palo Alto Networks trace a year of FinOps for AI, from cost embedded quietly in product features, to vibe coding tools that sneak past a budget during evaluation and then explode in production overnight, to the real shift: agentic AI, where cost stops being deterministic.
