Make Google Cloud Performance Predictable — Without Isolation or Overbuild

Silk eliminates data-layer contention so mission-critical applications scale with stability, not complexity.

As mission-critical applications scale on Google Cloud, performance becomes unpredictable under concurrent access and shared data usage. To compensate, teams isolate workloads, duplicate data, or oversize infrastructure — driving up cost and operational complexity without solving the root problem. Silk addresses this at the data layer. As an adaptive, software-defined SAN, Silk dynamically governs performance beneath applications, preventing contention and stabilizing latency as databases, analytics, and AI workloads operate on shared data. The result is consistent, predictable performance at scale — without rearchitecting applications or overprovisioning infrastructure.

• Consistent application performance under concurrent demand without resource contention
• Safe reuse of live production data across analytics, Dev/Test, and AI workloads
• Reduced infrastructure cost and operational complexity by eliminating isolation and overprovisioning