Actionable Observability for Kubernetes Applications

Dynamic, orchestrated cloud applications are creating a new set of operational and performance challenges ill-suited to legacy monitoring tools. OpsCruise imagines a world of autonomous operations and has innovated a fundamentally different approach.

Intelligent Application Observability for Kubernetes

Predictive Actionable Insights that Provide Clarity amid the Chaos

Traditional monitoring tools are fundamentally flawed for modern apps. OpsCruise is purpose built for containerized K8s applications and that means clearer insights, faster resolution, lower cost and happier customers.

The observability gap

Cloud Applications Introduce Fundamentally New Observability Challenges

Layers of Abstraction & External Dependencies
Cloud environments have complex and tiered layers of dependencies including application middleware,  orchestration (K8s) and infrastructure. Further, many apps have dependencies on 3rd party services which they do not control.
Dynamic, Transient Containers & Serverless
Cloud applications have a highly variable structure given the ephemeral nature of containers and the use of auto-scaling to handle variable workloads. This dynamic nature of the application introduces many blind spots and the need for true real-time visibility.
Lack of SRE/DevOps Engineering Talent
The agility needed in cloud operations has created a need for  more software engineering talent. While top web-scale companies have such skills in their engineering ranks, most enterprises require easier to use tools to achieve the same ends.

Existing Monitoring Tools are Completely Inadequate

Kubernetes & Containers are an Afterthought
Current monitoring tools were architected more than a decade ago for a different era before the move to microservices, and distributed applications running on VMs or physical servers. As a result, they lack the concepts of deployment, operations and workflow native to K8s that SREs and DevOps would expect.
Siloed Views Leads to Swivel Chair Operations
Numerous open source and commercial tools are good at aggregating metrics, logs and traces. Unfortunately, developers and admins in Ops/SRE teams are on their own to make sense  of the thousands of alerts and dozens of graphs generated by these tools. They lack context of how all the services interrelate and a coherent integration of the signals and the metadata.
Their Approach is Proprietary, Intrusive & Expensive
Today’s APM and infrastructure monitoring tools require proprietary agents to be deployed on every host, development teams to instrument their code and all of your raw telemetry to be stored in their respective clouds. That’s time-consuming, expensive, and  potentially negatively impacts your application performance.

Key capabilities

OpsCruise is Not Just Another Single Pane of Glass

Start with democratizing your observability data

OpsCruise is not another monitoring tool. Embed, support and integrate increasingly popular open source and cloud monitoring tools with a wide range of telemetry data.

Own your telemetry data to keep under your control, enable reuse and avoid unnecessarily paying your legacy monitoring vendor to store it.‍

Collect once and apply to many business purposes beyond monitoring -  including security, capacity planning and business analytics.

Add custom metrics such as transactions or business process information.

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COMBINE monitoring and configuration data for contextual visibility

Integrate signals from logs, metrics, flows and traces with important change data from events, configuration and CI/CD platforms in an application graph for powerful context.

Automatically understand how your application components are related to one another, including 3rd party cloud services, and how they are dependent upon Kubernetes and the underlying infrastructure.

See unwanted or unexpected dependencies and cross-region, zone, or datacenter interactions in your environment.

Rewind your topology viewTrack all changes in your stack, no matter whether they are infrastructural, configurations or deployments. See how those changes have impacted your operations over time.

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ENJOY Best-In-Class Contextual
Kubernetes Monitoring

Zero-touch configuration and automatic discovery and monitoring of dynamic, ephemeral microservice workloads running inside containers on Kubernetes and associated cloud services.

Without context switching, bring Kubernetes data together with infrastructure data, application data and logs.‍

Leverage Istio or other service meshes to augment operational flow data for complete coverage of all end-to-end services.‍

With a Dynamic Cluster Map understand the health, interdependencies and service level  performance within your Kubernetes cluster with pre-built, curated visualization. Hierarchical navigation lets you quickly drill down to node, pod and container levels in seconds. ‍

Gain performance visibility into the workloads like Replicasets, Deployments, and Jobs.‍

Quickly isolate Kubernetes issues such as pod evictions, restarts, service unavailability and resource allocations

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Predict Issues Before Customers
Are Impacted

Detect problems using our predictive  behaviour modeling that automatically learns your application’s behavior to surface problems across application components, Kubernetes and the supporting infrastructure.

Visualize your application performance using flow tracing powered by eBPF to track SLOs on latency, error rates or traffic changes between service components in real-time without need for code changes or a service mesh. ‍

Automate your anomaly detection using ML without relying on your setting or tuning thresholds or relying on historical and statistical outliers.

More

Less

Eliminate The War Room

Causation over correlation.  Go beyond correlation that only looks for events that occur around the same time or area. Leverage the knowledge of the application structure through topology walks that can pinpoint problem sources far down the dependency chain.

Continuous and Automated. Don’t be left navigating between different screens - experience automated causal analysis which proactively links unexpected behaviour changes within your application estate and ties it causally to SLO breaches.

Identify a broad range of causes including resources, configurations, application changes, or even changes in customer demand and behavior that can result in performance slowdowns.

‍Knowledge-Augmented ML runs robust automated diagnostics using reasoning to infer possible causes for problems across the full stack of the application.

‍Automate incident management to solve problems faster  by seamlessly integrating with ITSM and collaboration solutions such as ServiceNow, PagerDuty and Slack, to enable real-time CMDB updates, automatic ticketing and auto-triggering of remediation workflows.

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Up & Running In 3 Minutes

Deployed in minutes via Helm and automatically collect metadata on pods, deployments, services and nodes. There is no need to deploy agents on each host, K8s side cars or change code.

‍End to End Automation of many of the steps that require human involvement with traditional monitoring tools, including service map creation, setting thresholds and investigating the source of issues.

‍Integration with tools you use with native integrations with container orchestration platforms like Kubernetes, all major cloud providers, and popular monitoring and alerting tools.

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Architecture

Frictionless and Future Safe

DEPLOYMENT EASY
‍‍No agents, No Kubernetes (K8s) sidecars - traditional monitoring systems require proprietary agents to be deployed in every host or sidecars to be included in every container. OpsCruise leverages open source instrumentation.
BORN CLOUD NATIVE
OpsCruise can support VM hosts, it is K8s/container-centric in its design, visualization and workflow.
NO APPLICATION CODE CHANGES
‍‍ OpsCruise harnesses eBPF tracing and other networking techniques to capture L4/L7 data from the network stack and correlate it with namespaces, tags, and environment characteristics.
LEVERAGE YOUR TELEMETRY BEYOND MONITORING
OpsCruise does not need to be the long-term store for your telemetry. Modern enterprises are centrally collecting this data once for multiple use cases beyond monitoring, including security analytics, chargeback, capacity planning and user experience management.
LOW OVERHEAD
OpsCruise operates with negligible resource overhead so you can safely deploy in any production environment without impacting your application. OpsCruise captures flow data statistics without being in the data path.
WORKS WITH YOUR EXISTING TOOLS
OpsCruise integrates with your existing monitoring, ticketing and incident management tools.

Start with democratizing your observability data

OpsCruise is not another monitoring tool. Embed, support and integrate increasingly popular open source and cloud monitoring tools with a wide range of telemetry data.

Own your telemetry data to keep under your control, enable re-use and avoid unnecessarily paying your legacy monitoring vendor to store it.‍

Collect once and apply to many business purposes beyond monitoring -  including security, capacity planning and business analytics.

Add custom metrics such as transactions or business process information.

More

Less

Eliminate siloed swivel chair operations.
Weave together your monitoring and config data for contextual visibility

Integrate golden signals from logs, metrics, flows and traces with important change data from events, config and CI/CD platforms in an application graph for powerful context.

Automatically understand how your application components are related to one another, including 3rd party cloud services, and how they are dependent upon Kubernetes and the underlying infrastructure.

See unwanted or unexpected dependencies and cross-region, zone, or datacenter interactions in your environment.

Rewind your topology viewTrack all changes in your stack, no matter whether they are infrastructural, configurations or deployments. See how those changes have impacted your operations over time.

More

Less

Best-In-Class Contextual Kubernetes Monitoring

Zero-touch configuration and automatic discovery and monitoring of dynamic, ephemeral microservice workloads running inside containers on Kubernetes and associated cloud services.

Without context switching, bring Kubernetes data together with infrastructure data, application data and logs.‍

Leverage Istio or other service meshes to augment operational flow data for complete coverage of all end-to-end services.‍

With a Dynamic Cluster Map understand the health, interdependencies and performance correlation in Kubernetes clusters with pre-built, curated visualization. Hierarchical navigation lets you quickly drill down to node, pod and container levels in seconds. ‍

Gain performance visibility into the workloads like ReplicaSets, Deployments, and Job.‍

Quickly isolate K8s issues such as pod evictions, re-starts, service unavailability and resource allocations

More

Less

Predict Issues Before Customers Are Impacted

Detect problems using our predictive  behaviour modeling that automatically learns your application’s behavior to surface problems across application components, K8s and the supporting infrastructure.

Visualize your application performance using flow tracing powered by eBPF to track SLOs on latency, error rates or traffic changes between service components in real-time without need for code changes or a service mesh. ‍

Automate your anomaly detection using ML without relying on your setting or tuning thresholds or relying on historical and statistical outliers.

More

Less

Eliminate The War Room

Causation over correlation.  Go beyond correlation that only looks for events that occur around the same time or area. Leverage the knowledge of the application structure through topology walks that can pinpoint problem sources far down the dependency chain.

Continuous and Automated. Don’t be left navigating between different screens - experience automated causal analysis which proactively links unexpected behaviour changes within your application estate and ties it causally to SLO breaches.

Identify a broad range of causes including resources, configurations, application changes, or even changes in customer demand and behavior that can result in performance slowdowns.

‍Knowledge-Augmented ML  runs robust automated diagnostics using reasoning to infer possible causes for problems across the full stack of the application.

‍Automate incident management to solve problems faster  by seamlessly integrating with ITSM and collaboration solutions such as ServiceNow, PagerDuty and Slack, to enable real-time CMDB updates, automatic ticketing and auto-triggering of remediation workflows.

More

Less

Up & Running In 30 Minutes

Deployed in Minutes via Helm and automatically collect metadata on pods, deployments, services and nodes. There is no need to deploy agents on each host, K8s side cars or change code.

‍End to End Automation of many of the steps that require human involvement with traditional monitoring tools, including service map creation, setting thresholds and investigating the source of issues.

‍Integration with tools you use with native integrations with container orchestration platforms like Kubernetes, all major cloud providers, and popular monitoring and alerting tools.

More

Less

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