How Silk Delivers What Native Cloud Storage Can’t

This is the first in a series of blog posts about what differentiates Silk in the market — and how Silk can help your enterprise transform the way you cloud. 

Enterprises often find that native cloud storage forces a tradeoff between performance, reliability, and cost. Silk is a software-defined SAN for the cloud that turns native storage into predictable, enterprise block storage, so you get cloud storage for mission-critical applications — databases, AI pipelines, and data-intensive apps — without chronic overprovisioning. 

Silk Live 

Silk Live is the architectural foundation that brings Silk’s differentiated design to life, unifying its core components into a single, cloud‑native block storage experience built for modern, data‑intensive applications. By combining disaggregated, scale‑out building blocks with intelligent automation, Silk Live removes traditional storage constraints and decouples capacity from performance, giving teams the freedom to scale, adapt, and evolve without disruption.  

Delivered consistently across Azure, AWS, and Google Cloud, Silk Live provide multi-cloud storage that brings consistent enterprise storage operations to every environment. This enables organizations to standardize storage operations while retainingfull flexibility in workload placement. The outcome is a storage platform that stays aligned with cloud innovation, simplifies operations, and allows applications to run at their full potential as business and infrastructure needs change. 

Purpose-Built Disaggregated Scale-Out Block Architecture

Silk is cloud-native by design and delivers consistent, high-performance block storage across Azure, AWS, and Google Cloud. That means teams can standardize data services and operations while keeping the freedom to place each workload on the cloud that fits best. 

Silk breaks the old “capacity equals performance” model. Its disaggregated, scale-out design lets you scale capacity and performance independently, so you buy what you need when you need it — rather than paying for idle headroom just to hit SLAs. 

You can scale up, down, in, or out online — with Silk automatically redistributing volumes across nodes and avoiding downtime or reconfiguration. The details matter, but the outcome is simple: flexibility without disruption. 

Deterministic Single-VM Throughput at Cloud Scale

Silk is built to remove storage bottlenecks for the most demanding workloads, delivering up to 35GB/sec to a single VM—high-performance storage for applications and AI that native cloud storage can’t reach. 

Read Throughput Boost is Silk’s capability for read-heavy workloads that need sustained bandwidth — not just peak IOPS. It increases effective read throughput by optimizing how large, sequential (or near-sequential) reads are served across the performance layer, so analytics scans and ML training jobs can stream data faster and more consistently. At the same time, Silk scales to ~2.2M IOPS and sustains sub-millisecond latency for online transaction processing (OLTP) and other latency-sensitive pipelines — without the usual cloud storage tuning burden. 

Co-Developed With Hyperscalers

Silk works directly with hyperscalers to develop and adopt solutions purpose-built for Silk. 

On Azure, Silk worked directly with Microsoft to contribute to the development of Laosv4 VMs, pairing modern compute and SSD options with Silk to reduce spend while preserving performance for AI, analytics, and data-intensive workloads. 

On Google Cloud, Silk leverages n2-standard-64 in its performance layer to raise throughput and IOPS with efficient cost scaling. Silk works closely with Microsoft Azure and Google Cloud to help customers migrate and run mission-critical workloads in the public cloud. Silk also holds Microsoft Certified Software designation and Google Cloud Premier partner status, reflecting platform readiness, certifications, and delivery success. 

Non-disruptive Cloud Evolution Model

Online scaling lets teams capture new cloud economics as they arrive, so storage architecture evolves with the business instead of becoming the next modernization project. Silk customers can upgrade without rebuilds or downtime. 

Because Silk is software-defined and runs on standard cloud building blocks, it can adopt new VM types, disk technologies, and cloud services quickly — without forcing disruptive migrations. 

Silk also uses self-healing automation (including awareness of cloud maintenance events) to boost resiliency and reduce the manual work that often turns cloud “events” into application incidents. 

Release-driven Efficient Gains Improve Economics Over Time

Silk is designed so each release improves price-performance—advancing high-performance cloud storage by taking advantage of the newest cloud compute and storage building blocks as soon as they’re available. As hyperscalers introduce new VM families, faster local SSD options, and better price/performance profiles, Silk can adopt and validate those options quickly — so customers can see higher throughput and lower latency while often reducing the underlying infrastructure footprint and spend. In parallel, Silk’s own engineering efforts continuously drives efficiency through software optimizations and smarter automation (for example, improving data placement, reducing overhead, and streamlining how the system scales and heals). The result is compounding gains over time: more performance, less cost, and fewer operational tradeoffs — without disruptive upgrades or new licensing tiers. 

Built-in data reduction (global dedupe, inline compression, thin provisioning) commonly delivers 2:1 or better savings, cutting storage consumption automatically and transparently. 

Net result: customers get compounding economic benefits as Silk and the cloud platforms keep getting smarter. 

Next in the series: a dedicated post on Silk Live — what it is, why it’s different, and how it delivers predictable performance across clouds without the usual tradeoffs. 

Silk Flow 

Silk Flow is the set of technologies that lets Silk support very different workloads at the same time — transactional and analytic, inference and vectorization — without forcing you to choose a one-size-fits-none storage profile for enterprise block storage. By combining capabilities such as adaptive block size, a log-structured-array design, and byte alignment, Flow continuously shapes IO to the access pattern in front of it, helping each workload get the performance it needs with less manual tuning and fewer compromises. 

Adaptive IO That Automatically Optimizes for Each Workload

Silk uses automatic, adaptive IO to match how each workload actually behaves. Instead of fixed block sizes, it tunes IO patterns for everything from 8K transactions to 32K vectorization, 64K analytics, and 128K inference — so you get strong performance without constant manual tuning. 

Next in the series: a separate post on Silk Flow and adaptive IO — why block size and access patterns matter for mixed database, analytics, and AI workloads (with examples and benchmarks). 

Silk Echo 

Silk Echo brings database‑aware intelligence directly into cloud storage, making enterprise databases easier to operate, protect, and evolve. Built in as standard functionality — not bolt‑on tools — Echo enables fast, space‑efficient copies of production data that can be used safely across recovery, analytics, and development workflows. The result is faster day‑to‑day operations and far less friction when teams need access to current, production‑like data in the cloud. 

Built-In Database-Aware Capabilities — No Add-Ons Required

Silk Echo includes database-aware capabilities as standard features, not expensive add-ons. These capabilities are aimed at making enterprise databases easier to run — and safer to change — in the cloud. 

Instant, zero-footprint snapshots use redirect-on-write techniques, so copies are created in seconds and only consume space as changes occur — ideal for fast recovery, test/dev, and analytics workflows. 

Snapshots and clones can be mounted across zones, regions, and even clouds, enabling streamlined DR and near-instant access to production-like data without the usual duplication overhead. 

Complete Large-Scale ELT/ETL in Minutes — Not Hours

For ELT/ETL at scale, instant thin snapshots and clones turn “copy time” into “pointer time.” In one real-world example, Sentara Healthcare’s daily ETL windows dropped from 10–14 hours to ~3 hours, with downtime reduced to minutes for most systems. 

Teams can spin up current production copies for analytics, dev/test, and UAT quickly — without dragging on production performance or paying for full duplicate datasets. 

Next in the series: a dedicated Silk Echo post covering instant snapshots/clones and database-aware capabilities — how they change Day 2 operations, DR, and analytics workflows in the cloud. 

Silk Live, Flow, and Echo — Combining to Address the Needs of Your Enterprise 

Silk Live, Flow, and Echo work together as an integrated platform designed to help enterprises run their most critical, data-intensive workloads with greater confidence, efficiency, and control in the public cloud. Together, they remove the traditional tradeoffs between performance, cost, and operational complexity, allowing teams to modernize applications, support real-time AI use cases, and adapt to changing business demands without rearchitecting or disruption. By simplifying how storage is delivered, scaled, and optimized across cloud environments, Silk enables organizations to turn infrastructure into a strategic advantage — improving application responsiveness, increasing agility, and aligning cloud spend more closely with actual business value. 

Storage Optimized for Real-Time AI Inference

Real-time AI inference depends on low-latency access to live production data — something many cloud storage options deliver only by overprovisioning or copying data into separate systems. 

Silk keeps high-performance storage close to compute so models can run against fresh production data as concurrency scales — without complex data duplication or unpredictable latency spikes. 

That matters for fraud detection, recommendations, and other latency-sensitive enterprise use cases where storage variability shows up as business variability. 

Proven to Reduce Cloud Infrastructure Costs by 40%+

Savings typically come from right-sizing performance vs. capacity, built-in data reduction, and instant snapshots/clones that eliminate full-copy workflows — turning storage from a cost amplifier into a cost-control lever. 

Customers use Silk to cut cloud infrastructure costs while improving performance. Silk’s customers have reported real savings of 45–71%, backing up projections from total-cost of ownership (TCO) analyses.  

Next in the series: a separate post that connects Live, Flow, and Echo end-to-end — how they support real-time AI and data-intensive apps, and where teams typically find the fastest ROI. 

Why Trust Silk? 

Backed by 20+ patents, Silk combines instant snapshots, database-aware capabilities, adaptive IO, and proven cost savings to deliver predictable performance at materially lower cloud cost — so teams can move critical databases, analytics, and AI to the cloud with confidence. No rewriting or refactoring required. 

Silk rethinks cloud block storage for mission-critical applications: disaggregated scale, extreme performance, and multi-cloud flexibility, and the ability to adopt new cloud capabilities without disruption.