Monitor Kubernetes
Control the costs and noise that come with monitoring at scale. Analyze and aggregate Kubernetes telemetry data at the source with Edge Delta’s agent fleets. Visualize key Kubernetes metrics and events to gain real-time insights into your environment.
Kubernetes handles scale; your monitoring solution does not
Kubernetes infrastructure is a complex web of dynamic components, constantly changing to align with workload demands. While providing valuable flexibility, this design makes monitoring Kubernetes environments incredibly tricky. Traditional methods elect to send all Kubernetes telemetry data to a centralized observability platform before bulk processing and analysis. This outdated framework results in:
Muddled Visibility
Too much telemetry data obfuscates the issues within your Kubernetes environment. Many Kubernetes components generate logs, leaving you drowning in a sea of data.
High Costs
Collecting and sending every piece of telemetry data to a centralized platform for processing and analysis is a surefire way to explode your observability bill.
Weak Performance
The “centralize-then-analyze” approach introduces significant delays between data creation and insight generation, wasting time when troubleshooting production issues.
Edge-based monitoring
Edge Delta’s distributed architecture is built to support the scale and cardinality of your Kubernetes environment data. Powerful technologies like eBPF are leveraged to collect node-level metrics directly from the kernel, processing it along with all other telemetry as it’s generated at the source. This results in lightning fast query times and AI-powered insights regardless of data volume.
View golden signal dashboards for your Kubernetes workloads right out of the box, and build custom dashboards and alerts to monitor the Kubernetes metrics that matter most to you.
Visualize your Kubernetes environment with ease, and drill down into individual namespaces, workloads, pods, and more.
Query Kubernetes event data in sub-seconds with Edge Delta’s highly performant, time-series optimized search infrastructure.
Ready to take the next step?
Learn more about our use cases and how we fit into your observability stack.
Trusted By Teams That Practice Observability at Scale
“This is not just about doing what you used to do in the past, and doing it a little bit better. This is a new way to see this world of how we collect and manage our observability pipelines.”
Ben Kus, CTO, Box
Join Engineering Teams That Are Embracing AI
It only takes a couple of minutes to start running AI Teammates in production.