APMQ Make observability data more accessible

We Begin by making observability data more accessible. OpsCruise is not just another monitoring tool - it integrates and supports various open-source and cloud-based monitoring tools, allowing you to access a wide range of telemetry data. By owning your own telemetry data, you have more control over it and can use it for various purposes beyond monitoring, such as security, capacity planning, and business analytics. Additionally, you can collect and store custom metrics, such as transaction or business process information, to be used in multiple ways.

APMQ enhances context by combining monitoring and configuration data

Improve visibility by combining monitoring and configuration data. Integrate data from logs, metrics, flows, and traces with change data from events, configuration, and CI/CD platforms to create a more comprehensive view of your application. Automatically understand the relationships between application components, including third-party cloud services, and how they depend on Kubernetes and the underlying infrastructure. Identify unexpected dependencies and interactions across regions, zones, and data centers. Track changes to your stack over time and see how they have impacted your operations.

With APMQ experience the best Kubernetes monitoring with contextual data

Get the best Kubernetes monitoring experience with contextual data. Zero-touch configuration and automatic discovery and monitoring of dynamic, ephemeral microservice workloads running inside containers on Kubernetes and associated cloud services. Easily bring Kubernetes data together with infrastructure data, application data, and logs without switching contexts. Use Istio or other service meshes to supplement operational flow data for comprehensive coverage of all end-to-end services. The Dynamic Cluster Map allows you to understand the health, interdependencies, and service level performance within your Kubernetes cluster through pre-built, curated visualizations. Hierarchical navigation lets you easily drill down to the node, pod, and container levels. Gain performance visibility into workloads such as Replicasets, Deployments, and Jobs. Quickly identify and isolate Kubernetes issues such as pod evictions, restarts, service unavailability, and resource allocation issues.

APMQ anticipate issues before they impact customers

Predict issues before they affect your customers. Use predictive behavior modeling to automatically learn your application's behavior and identify problems across application components, Kubernetes, and the supporting infrastructure. Visualize your application performance in real-time using flow tracing powered by eBPF to track service-level objectives (SLOs) such as latency, error rates, and traffic changes between service components without requiring code changes or a service mesh. Automate anomaly detection using machine learning without the need to set or tune thresholds or rely on historical or statistical outliers.

Eliminate the need for emergency response teams

Eliminate the need for a war room. Instead of simply looking for correlated events that occur at the same time or place, use knowledge of the application structure through topology walks to pinpoint problem sources deeper in the dependency chain. Continuous and automated causal analysis proactively links unexpected behavior changes within your application estate to SLO breaches, identifying a wide range of possible causes including resources, configurations, application changes, and even changes in customer demand and behavior. Use knowledge-augmented machine learning to run robust automated diagnostics that infer possible causes for problems across the full stack of the application. Automate incident management to solve problems faster by integrating with ITSM and collaboration solutions such as ServiceNow, PagerDuty, and Slack, allowing for real-time CMDB updates, automatic ticketing, and triggering of remediation workflows.

Quick and easy setup

Get up and running in just a few minutes. Deployment is quick and easy using Helm, and metadata on pods, deployments, services, and nodes is automatically collected without the need to install agents on each host, use K8s sidecars, or modify code. Many of the steps that require manual intervention with traditional monitoring tools are automated, including service map creation, setting thresholds, and investigating the source of issues. Integrate with the tools you already use through native integrations with container orchestration platforms like Kubernetes, all major cloud providers, and popular monitoring and alerting tools.