Redefining What’s Possible on AWS: How Silk Pushes Cloud Infrastructure to Its Performance Limits

Cloud teams want more from AWS. They want applications that respond instantly, scale smoothly, and deliver consistent performance with any load. Yet many workloads, especially high speed operational systems and real time AI inferencing pipelines, can hit the limits of native AWS storage long before they reach the limits of compute. 

At Silk, our R&D team has focused on one goal: reveal the true performance potential of the cloud by optimizing from the ground up. Our latest benchmark runs show exactly what becomes possible when the right architecture is applied. 

Below is a simple breakdown of the performance delivered by a single Silk c.node. This lets architects size and plan environments with complete confidence. 

Silk Performance Per c.node on AWS

Our latest qualification testing used r8i.16xlarge instances. Each c.node produced: 

• 216,000 read IOPS 

• 150,000 write IOPS 

• 3,500 MB per second read bandwidth 

• 1,450 MB per second write bandwidth
   

These numbers reflect how much performance exists in AWS infrastructure when combined with Silk’s cloud acceleration layer. Silk can deploy as many DataPods and c.nodes in an AWS region as you like so you can deliver effectively unlimited levels of performance. 

Why Silk Reaches These Performance Levels

Performance is separate from capacity

Silk delivers I/O performance through the c.node layer, not by increasing disk size. This removes the need to oversize volumes or pay for unused capacity. 

Data flows through the AWS compute network

Traffic to Silk DataPods is not subject to the rate limits assigned to native block storage,  this avoids common IO bandwidth bottlenecks and produces lower and more predictable latency, even with lower core counts. 

Silk DataPods can also be shared between many workloads avoiding the need for costly silos of performance. 

Linear scale out

Each c.node adds more throughput and IOPS. Architects can grow performance simply by increasing node count. There are no diminishing returns and no complicated tuning paths. 

No changes to your applications

Silk presents standard block devices to virtual machines. Databases, microservices, and analytics workloads run normally with no refactoring. 

What This Unlocks for Your Applications

Transformed Application User Experience

With the bottleneck moved away from the data layer, applications that depend on fast access to operational data become dramatically more responsive. User interactions that once stalled on storage latency now complete instantly, even under heavy load. This translates directly into smoother application behavior, faster transaction processing, and a consistently responsive experience for ender users. 

Real Time AI Inferencing That Responds Instantly

AI models that depend on immediate access to live operational data benefit from higher bandwidth and lower latency. Inferencing pipelines become faster, more accurate, and more consistent. 

Stronger Performance For Mission-Critical Databases 

Engines such as Oracle, SQL Server, and PostgreSQL gain higher throughput and more predictable behavior under load. 

Lower Cloud Costs Through Smarter Sizing

Silk removes the need to provision premium EBS volumes or large instances just to get acceptable performance. Teams can right size without sacrificing speed. 

A Predictable Way to Design for Performance

Silk provides clear per c.node performance metrics. Architects can calculate total performance by multiplying the number of nodes they choose to deploy.  When combined with Silk’s included capabilities like: data reduction, thin provisioning, zero-footprint snapshots, and clones, this creates a simple and predictable planning model that avoids complexity. 

Final Thoughts

AWS offers a powerful foundation for modern applications, but native storage options do not always expose the full performance potential available in the compute layer. Silk is designed to unlock that potential, giving teams a direct path to sub-millisecond latency and very high throughput inside AWS, Azure, and Google Cloud. 

Silk’s leading performance capability, when combined with our unique ability to optimise the data layer using Silk Echo, gives you the power to optimise your workflows. This also enables you to democratise your data for many additional use cases at no extra cost. 

If you want to explore these results in more detail or test Silk with your own workloads, our team is ready to help.