Optimizing EBS for EKS: How to Balance Performance and Cost

Modern applications often come with complex storage needs that go beyond data lake solutions like S3. While these storage services are ideal for specific use cases, managing Kubernetes-based workloads in Amazon EKS requires a more tailored approach. For high-performance tasks such as log management, temporary directories, or caching, Elastic Block Store (EBS) is a natural fit—but selecting the right EBS configuration can make a significant difference in both performance and costs.

How to Integrate EBS into your Kubernetes (K8s) Architecture

When it comes to integrating EBS into your Kubernetes architecture, you generally have two options:

• An EBS volume per node
• An EBS volume per pod (also known as PV or Persistent Volume in Kubernetes)

Both approaches have their advantages and challenges. Let’s dive into what makes each one viable—and where the pitfalls lie.

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EBS Volume Per Pod: The Fine-Grained Approach

Attaching an EBS volume to each pod might seem like the perfect way to fine-tune performance for each individual application. It allows you to customize the capacity and performance of each volume based on the specific needs of the containers within each pod. However, this granular control comes at a cost.

Integrating Kubernetes with the EBS control plane via a CSI (Container Storage Interface) driver introduces operational complexity. You’ll need to manage each volume’s lifecycle—tracking the creation, deletion, and rescheduling of pods across nodes. Moreover, accurately sizing these volumes is no small task, and many teams end up "guesstimating," losing the benefits of precise control.

For DevOps teams already stretched thin, managing storage at this level can quickly become overwhelming. Since each pod requires its own volume, the number of EBS resources you need to monitor can increase by a factor of 10 to 20, adding significant overhead to your infrastructure.

EBS Volume Per Node: Simplicity in Scalability

An alternative approach is to allocate a single EBS volume per node. This method is easier to manage and doesn’t require CSI drivers or deep integration with Kubernetes. By assigning one large volume per node, all the pods running on that node share the storage performance, allowing you to optimize performance without worrying about the individual limitations of a single pod’s volume.

However, this raises a new challenge: how do you properly size this node-wide volume? Since Kubernetes dynamically schedules pods across nodes, it’s difficult to predict which containers will land where and how much storage they’ll need.

Many teams fall back on over-provisioning, attaching large EBS volumes to nodes in the hope that they’ll never run out of space. While this works in theory, it leads to wasted storage and inflated costs—a major concern for any FinOps-conscious team.

Auto-Scaling EBS Volumes: A Balanced Solution for Performance and Cost

Managing EBS storage for Kubernetes clusters can quickly become a balancing act between over-provisioning and underutilization. Instead of relying on manual estimates, auto-scaling solutions offer a more dynamic approach by adjusting storage based on real-time demand. Datafy's auto-scaling solution simplifies this process by monitoring storage consumption at the node level and scaling volumes accordingly.

This approach not only eliminates the need for guesswork but can also save you 70%-80% of your EBS capacity costs, ensuring you're only paying for the storage you actually use. In environments where Kubernetes workloads are unpredictable, such a solution helps maintain optimal performance without inflating costs—addressing a key challenge for DevOps and FinOps teams alike.

Interested in learning more about how auto-scaling EBS volumes can optimize your Kubernetes clusters? Get in touch today.