At Civo, we’ve always believed that the cloud industry should be fair, open, and accessible to companies of all sizes - not just the select few with deep pockets and large platforms. When the Open Cloud Coalition (OCC) was first proposed, we immediately saw it as an opportunity to cut through the noise and amplify the important voices of the cloud industry.

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.

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.

Let’s be honest: setting up Kafka monitoring on Kubernetes can feel like you’re trying to solve a puzzle without all the pieces in place. Between connectivity snags, configuration issues, and keeping tabs on resource usage, it’s easy to feel like you’re constantly firefighting. But tackling these issues head-on with a few go-to solutions can save a lot of headaches down the road.