PointFive raises $60M to curb AI infrastructure costs

PointFive raised $60 million in a Series B financing led by Accel, according to The Wall Street Journal, which reports the round values the company at $500 million post-money. Coverage from Calcalist, SiliconANGLE, Globes and VentureBurn lists participation from Index Ventures, Salesforce Ventures, Entree Capital, Perpetual Growth, Vesey Ventures and Sheva Ventures, and reports the financing brings PointFive's total capital raised to $96 million.

Globes and VentureBurn report PointFive was founded in 2023 by Alon Arvatz, Gal Ben-David and Amir Hozez and maintains a presence in Tel Aviv, London and the United States. The company markets a cloud and AI efficiency platform that detects waste across cloud infrastructure, data platforms and AI workloads and routes automated remediation to engineers; reporting cites new offerings described as an AI Efficiency OS and a companion product called TokenShift. Globes reports the company grew annual recurring revenue sixfold across 2024 and 2025.

Globes reports customers say they observe roughly 30% average cloud-cost savings and a high return on investment. These are vendor- and customer-reported figures rather than independently audited results, so they should be read as early adopter outcomes pending reproducible before-and-after telemetry.

Tools that govern AI infrastructure cost generally compete on three technical problems: accurate attribution of spend to specific models and pipelines, detection of inefficiency across compute and storage, and automated remediation that does not degrade performance. As enterprise AI deployments add larger models, longer context windows and always-on agent workflows, cloud spend becomes more variable, which is a common driver of demand for visibility-plus-automation products. Backing from strategic investors such as Salesforce Ventures alongside Accel signals investor interest in startups that promise direct, measurable savings.

Practitioners and procurement teams should evaluate any cost-optimization vendor on measurement fidelity, the safety of automated remediation, and support for model-specific metrics. Adoption and integration of products like the AI Efficiency OS and TokenShift with orchestration and governance tooling, plus public case studies detailing baseline spend and measurement windows, will indicate whether the headline savings hold up at scale.