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.
Enterprise AI is moving fast — from simple chatbots and proof-of-concept demos to autonomous agents, RAG applications, and production-grade inference workflows. But as AI gets more capable, it is also putting an entirely new kind of pressure on the data layer.
For years, the promise of cloud migration has centered on agility, scalability, and cost efficiency. Enterprises were told they could move faster, modernize infrastructure, and unlock new capabilities simply by moving workloads out of the data center and into the public cloud.
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).
You migrated to the cloud with a solid plan. You sized your instances carefully, chose the right storage tiers, and followed architecture best practices. The first few months looked fine. Then the bill started climbing. SLA breaches crept in during backup windows. Your team started scheduling around performance risk instead of operating with confidence. A year later, you’re paying significantly more than projected — and cloud performance issues are still surfacing in the moments that matter most.
If you’re running data-intensive workloads on AWS today, you’ve likely run into the same wall: performance comes at a price — and often an unpredictable one.
This week in Copenhagen, Silk had the opportunity to join SimCorp Global Summit — an event that brings together some of the most forward-thinking leaders in investment management technology.
I’ve spent my career at the intersection of data and enterprise technology — SingleStore, TIBCO, Conga — and I’ve developed something of a sixth sense for the moments when infrastructure and market demand snap into alignment. When they do, the commercial opportunity doesn’t just appear. It arrives all at once.
AWS is built on a compelling promise: near-infinite scalability, global reach, and elastic performance. For teams operating in a single region or serving localized workloads, that promise largely holds true. Auto scaling works. Managed services absorb growth. Performance is predictable enough to plan around.
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.