Breaking Free From AWS IOPS Limits

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.

Amazon EBS volumes come with IOPS caps. EC2 instances must be sized not just for compute, but to meet storage throughput demands. And when workloads spike, the only reliable way to maintain performance is often to overprovision infrastructure — allocating far more resources than the application actually needs.

It works. But it’s inefficient, expensive, and difficult to scale. And as AI and real-time applications put even more pressure on data access, these limitations are becoming harder to ignore.

Why AWS Performance Breaks Down at Scale

AWS provides powerful building blocks — but for data-intensive workloads, performance is tightly coupled to infrastructure sizing.

To achieve higher IOPS and lower latency, teams are forced to:

• Increase EBS volume size just to unlock more IOPS
• Move to larger EC2 instance types to access higher throughput
• Continuously tune and rebalance storage configurations

This creates a system where performance is constrained by service-level limits, not application requirements.

The result is a familiar trade-off: You can get the performance you need — but only by paying for significantly more infrastructure than you actually use.

Decoupling Performance from AWS Infrastructure

Silk changes this dynamic by decoupling performance from the underlying AWS infrastructure. Instead of relying solely on native EBS constraints and EC2 sizing, Silk introduces a software-defined data layer that virtualizes cloud storage and removes IOPS bottlenecks. This allows organizations to achieve consistently high IOPS and ultra-low latency without being bound by EBS limits or forced into oversized EC2 instances.

Because Silk operates independently of AWS storage constraints, performance becomes predictable. Workloads can scale based on application needs — not infrastructure workarounds — and operations teams no longer need to constantly tune environments to maintain SLAs.

Ending the Overprovisioning Cycle

For years, teams have accepted overprovisioning as the cost of doing business in AWS. If you need more performance, you scale up — whether you need the extra capacity or not.

Silk eliminates that pattern entirely. By optimizing how data is delivered to applications, Silk allows compute and storage to be sized appropriately, rather than inflated to meet IOPS requirements. That means you can run the same workloads on smaller EC2 instances, with more efficient use of EBS, while still achieving the performance levels required for mission-critical applications.

The outcome is a fundamentally different operating model — one where organizations can achieve up to 10x faster performance while reducing costs by as much as 50%. 

From AWS to Anywhere (Without Re-Architecting)

While many organizations start with AWS, they rarely stay confined to a single cloud. What makes this approach powerful is that it doesn’t stop at AWS. The same software-defined model extends across Azure and Google Cloud, allowing teams to maintain consistent performance and operational simplicity wherever workloads run — without rewriting applications or re-architecting environments.

AWS gives you flexibility — but when it comes to data-intensive workloads, performance is still tied to infrastructure in ways that drive cost and complexity.

Silk breaks that dependency. By removing IOPS constraints, eliminating overprovisioning, and delivering predictable performance, organizations can finally run high-performance workloads in AWS the way they were meant to — efficiently, consistently, and at scale.