The cloud bill arrives. It’s up again — 20%, 30%, sometimes more. You know exactly why: you provisioned your way through another performance problem last quarter. Scaled up the VMs, moved to a premium storage tier, bought more IOPS than you needed to finally get the throughput your database required. The performance problem went away. The cost problem got worse. And somewhere in the back of your mind, you already know it’ll happen again.
If your organization has recently adopted a FinOps framework, you are likely familiar with the “honeymoon phase” of cloud cost optimization. In the early days of your FinOps journey, the wins come quickly and easily. You deploy a cloud cost management tool, gain visibility into your multi-cloud environment, and immediately spot the low-hanging fruit. You terminate zombie instances, delete unattached storage volumes, right-size a few glaringly overprovisioned virtual machines, and lock in significant discounts through Reserved Instances (RIs) and Savings Plans (SPs).
If you listened to Jensen Huang’s recent interview on Stratechery, one thing is abundantly clear: AI has officially crossed the threshold from experimental to operational.
ViVE 2026 in Los Angeles brought together healthcare IT leaders from across the country — and what stood out most wasn’t hype, it was reality. Conversations on the showroom floor revealed that healthcare organizations are wrestling with several core infrastructure, performance, and data challenges that threaten clinical outcomes, cost efficiency, and innovation velocity.
Artificial intelligence (AI) inference is where artificial intelligence delivers real business impact – or quietly fails. As AI systems move from experimentation into production, the challenge is no longer model training or accuracy. It is inference latency.
Clinical AI is rapidly moving from experimentation into the core of healthcare operations. What once lived in research environments or offline analytics is now embedded directly into clinical workflows. Imaging analysis, diagnostics, operational intelligence, and real-time decision support increasingly operate alongside electronic health records and other systems of record.