New research shows that businesses are struggling to forecast AI costs, with more than half missing targets by 11–25% and nearly one in four missing by over 50%.
CFO Dive’s Alexi Alexis reports on the risk of significant cost overruns as AI pilots move into production in 2026, with 84% of companies already seeing margin erosion linked to AI spend.
Click here to read the full story on Revenue Brew: https://www.revenuebrew.com/stories/2025/09/16/companies-lack-forecasting-ai
451 Research (S&P Global) examined Mavvrik’s platform, its new Agentic Cost Intelligence SDK, and the Ingram Micro channel partnership. This is what they found.
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AI workloads increase Databricks and Snowflake costs by adding repeated compute, vector search, model serving, embeddings, storage, and inference activity to existing data platforms. This article explains the core AI cost drivers and why FinOps teams need workload-level attribution to measure true AI cost-to-serve.
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AI cost tracking in 2026 requires more than monitoring token spend or reviewing provider invoices. This guide explains how finance, FinOps, and engineering teams can track AI costs across workflows, customers, and environments using metrics like cost per inference, cost per workflow, and cost-to-serve.
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