Operations | Monitoring | ITSM | DevOps | Cloud

Managing resource scaling in EKS clusters can quickly become complex, especially with fluctuating workloads. Traditional autoscaling solutions often require predefined configurations, which can lead to inefficiencies and unnecessary costs. This is where Karpenter comes in—a powerful, open-source project designed to dynamically manage and scale nodes in response to real-time application demands.

Custom resources are critical components in Kubernetes production environments. They enable users to tailor Kubernetes resources to their specific applications or infrastructure needs, automate processes through operators, simplify the management of complex applications, and integrate with non-native applications such as Kafka and Elasticsearch.

While overprovisioning Kubernetes workloads can provide stability during the launch of new products, it’s often only sustainable because large companies have substantial budgets and favorable deals with cloud providers. As highlighted in Datadog’s State of Cloud Costs report, cloud spending continues to grow, but a significant portion of that cost is often due to inefficiencies like overprovisioning.

Kubernetes offers the ability to scale infrastructure to accommodate fluctuating demand, enabling organizations to maintain availability and high performance during surges in traffic and reduce costs during lulls. But scaling comes with tradeoffs and must be done carefully to ensure teams are not over-provisioning their workloads or clusters. For example, organizations often struggle with overprovisioning in Kubernetes and wind up paying for resources that go unused.

Startups relying on Heroku often hit roadblocks as they scale. Rising costs, technical limitations, and lack of control over infrastructure force many to explore alternatives. One such startup recently migrated 37 applications from Heroku to AWS using Qovery’s DevOps AI Migration Agent. Here’s how they accomplished this migration in less than two hours, saving days of manual work.

As someone who is building a platform that is intended to make Kubernetes operations easier for everyone––I’ve learned a lot about production Kubernetes operations. The main thing I’ve noticed folks getting wrong, is that it isn’t simply a platform, it’s an entire ecosystem.

Learn how to access and use Docker logs to monitor, troubleshoot, and improve your containerized apps in this simple guide for developers.

Sometimes, even when you’re running on a solid infrastructure like AWS EKS, a growing company needs to rethink how they manage it. One startup, fresh off a successful Series A raise, found themselves in just that situation. They had been running two EKS clusters — Production and Staging — managed entirely by their CTO, who is an experienced AWS power user.

As organizations increasingly turn to Kubernetes to support their cloud-native applications, it has become critical for teams to analyze and respond to the dense telemetry data related to this orchestration layer.

Kubernetes popularity continues to grow, with over 60% of organizations maintaining multiple Kubernetes across diverse environments and teams in some capacity. However, as clusters multiply, so do operational challenges: from monitoring hundreds of microservices to responding to and escalating incidents across distributed systems.