This article is based on reporting originally published by TechTarget’s SearchITOperations in February 2020.
Dynatrace Expands Kubernetes Observability for AIOps
Dynatrace expanded the number of metrics its Davis AIOps engine can ingest from Kubernetes infrastructure, sharpening automated root-cause analysis and laying the groundwork for autoremediation of container workload incidents. The move was driven by rapid enterprise adoption of cloud-native application architectures and the consolidation of Kubernetes as the dominant container orchestration standard.
What Changed: Platform-Level Kubernetes Metrics in Davis
Prior to this update, Dynatrace integrated with Docker containers and provided visibility into containerized application performance. The expanded release added direct integration with the Kubernetes API and the Prometheus time-series monitoring tool, giving Davis visibility into:
- Kubernetes cluster state, including pods, nodes, and network connections
- Dependency mapping between workloads across the cluster
- Critical Kubernetes events and state changes
- Platform-level performance metrics surfaced alongside application telemetry
By correlating these infrastructure signals inside Davis, teams can identify the root cause of incidents in Kubernetes environments more quickly and with greater accuracy than was previously possible with container-level data alone.
Real-World Context: Moving from Docker Swarm to AKS
Mark Kaplan, Senior Director of IT at Barbri — a Dallas-based company offering legal bar review courses online — described the practical impact of the gap that the update addresses:
“We were already using Dynatrace with Kubernetes, but the type of detail about the Kubernetes cluster they’re supplying hasn’t been available.”
Mark Kaplan, Senior Director of IT, Barbri
Barbri had recently migrated from Docker Swarm to Azure Kubernetes Service (AKS) and was preparing to test the Dynatrace Kubernetes monitoring update. Kaplan’s concern was straightforward: without deep cluster visibility, the first signal of an infrastructure problem might be a customer complaint — an unacceptable outcome for a business delivering time-sensitive exam preparation services.
Autoremediation and the NoOps Strategy
Richer Kubernetes metrics in Davis feed directly into Dynatrace’s broader NoOps strategy — the goal of reducing or eliminating manual operational intervention through intelligent automation. With this update, Dynatrace positioned the following capabilities as available for teams ready to adopt them:
- Automated service desk ticket creation integrated with third-party ITSM systems when anomalies are detected
- Script-triggered remediations to restart pods or other Kubernetes resources in response to detected failures
- Business impact correlation via Dynatrace’s Digital Business Analytics tool, which links infrastructure monitoring data to business performance metrics
Company officials acknowledged that full unattended automation remains a future state for most customers, with adoption expected to be gradual.
Competitive Landscape: AIOps Vendors Race to Match Kubernetes Maturity
Dynatrace was not alone in deepening Kubernetes support. By early 2020, several major observability and AIOps vendors had invested significantly in cluster-level monitoring:
- AppDynamics (Cisco): Released the AppDynamics Cluster Agent in November 2019, providing automated root-cause analysis and Kubernetes visibility, with business performance analysis through its Business iQ tool. Cisco added autoremediation capabilities the following month via an integration between AppDynamics and Cisco Intersight, as well as support for an early-stage Cisco HyperFlex Kubernetes platform.
- LogicMonitor: Introduced autodiscovery and topology mapping for Kubernetes clusters, with AIOps capabilities still maturing at the time.
- New Relic: Similarly added Kubernetes autodiscovery and topology maps, also continuing to develop its AIOps feature set.
- Moogsoft: Cited by some early AIOps adopters as an alternative, particularly in environments where APM licensing costs were a barrier.
The competitive pressure reflected the broader market shift: as Kubernetes cemented its role as the enterprise container orchestration standard, monitoring vendors were expected to provide cluster-level intelligence rather than merely container-level metrics.
“Containers are just packages, and now there’s a common way to manage those packages with a standard API. Before, there was still too much variation at that orchestration layer, but now there’s a clear preference for Kubernetes.”
Mary Johnston Turner, Analyst, IDC
The Uncertain Future of Kubernetes NoOps
While the technical foundations for AIOps-driven automation were taking shape, analysts cautioned that the industry remained divided on how far hands-off automation should go in practice.
“You need some level of analytics to get through this transition to cloud-native technologies. Customers value faster analysis and a set of recommended remediations, but most still want a little bit of human intervention before they hit the big red button to ‘make it go.'”
Mary Johnston Turner, IDC
Ephemeral workloads and complex microservices interdependencies make it difficult for human operators to keep pace without automation assistance. However, the threshold for trusting fully automated remediation — where no human approves the action — remains high for most enterprise teams, particularly where business-critical workloads are involved.
Pricing Pressure on APM and Observability Tools
One structural challenge cutting across the entire observability market is licensing cost. Some early AIOps adopters reported choosing tools like Moogsoft specifically because APM-based monitoring can become prohibitively expensive at scale in containerized environments — where the number of instrumented processes, pods, and ephemeral services can grow rapidly.
IDC’s Turner predicted ongoing pricing experimentation across the industry:
“The whole industry is still figuring out how to adjust pricing for cloud and on-demand workloads. We’ll see experimentation with different licensing models. Customers need to decide how broadly it’s worth paying for monitoring and observability. Historically APM was limited to high-value apps, but with containers it’s important to have a full view of the enterprise.”
Mary Johnston Turner, IDC
Dynatrace’s own customer base included organizations like Barbri that considered the platform worth the investment, but pricing pressure from cloud-native scaling dynamics will likely continue to influence purchasing decisions across the observability space.
Key Takeaways
- Dynatrace extended Davis to ingest Kubernetes API and Prometheus metrics, adding cluster, pod, node, and network visibility to its AIOps engine.
- The update enables faster root-cause analysis for Kubernetes incidents and supports automated remediation workflows for teams ready to adopt them.
- Kubernetes monitoring data from Davis also feeds Dynatrace’s Digital Business Analytics tool, connecting infrastructure health to business outcomes.
- Competing vendors including AppDynamics, LogicMonitor, and New Relic were simultaneously deepening Kubernetes support, reflecting the platform’s consolidation as the enterprise standard.
- Most enterprise teams still prefer human approval before automated remediation actions, even where automation is technically available.
- APM licensing costs at container scale remain a concern, and the industry is expected to experiment with new pricing models suited to dynamic, cloud-native environments.