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Google Cloud delivers powerful primitives for elasticity and scale. But when production applications, analytics, and AI workloads begin sharing infrastructure, performance variability becomes difficult to control. As concurrency grows, teams oversize Compute Engine instances, isolate applications, duplicate data, and accept rising operational overhead to protect SLAS.
Silk eliminates the root cause: performance tied to machine type and disk selection. By introducing a governed data layer beneath applications, Silk decouples performance from infrastructure sizing – ensuring consistent latency and throughput under concurrent demand. Applications, analytics, and AI can safely share live production data without overbuilding or manual tuning.
Maintain predictable latency and throughput as access scales.
Avoid permanent over-provisioning while meeting performance targets.
Reduce complexity caused by isolation, duplication, and manual tuning.
Enable real-time access to production data without impacting application performance.
Restore services quickly with minimal operational disruption.
Silk integrates directly with Compute Engine VMs instances, Persistent Disk and Hyperdisk, VPC networking, and zonal and regional availability models. It operates transparently within Google Cloud environments, stabilizing performance without requiring applications changes.
Make Google Cloud behave predictably – even under peak demand.
Whether standardizing on Google Cloud or operating across multiple clouds, Silk provides a consistent performance layer that enables organizations to migrate critical databases faster, maintain operational stability, meet financial governance requirements, and scale confidently as concurrency and AI demands increase.
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