Accurate, Actionable Alerts with Analytics/AI

Smart Alerts Surface the Right Problems

Alerting Based on Advanced and AI-driven Anomaly Detection

Most traditional tools detect simple threshold-based anomalies, making it difficult to distinguish false alarms from real issues. With VMware Tanzu Observability by Wavefront, you create smart alerts that dynamically filter noise and capture true anomalies.

The top chart shows the volume of traffic into a front-end load balancer. There are two distinct features in this chart:

  • A vertical uptick on the left side of the chart, which is a false alarm
  • A much shallower, but more sustained dip towards the right side of the chart, which represents a true outage.

Let’s assume we used threshold-based alerts. Even if we smoothed the line by taking the moving average, you would see a triggered alarm for both dips.

Further, in the bottom chart, we use a function to show the moving median of the top chart. The moving median removes the first short dip, but not the second dip. Therefore, we can create an alert condition in Tanzu Observability based on the moving median suddenly dipping. With hundreds of analytics functions, Tanzu Observability helps you craft the perfect alert for any given anomaly.

Add AI Genie™ for AI/ML-based automated detection and prediction functions, further simplifying intelligent alert creation.

Simplify Alert Sprawl with Consolidated Alert Creation

Is trying to create and maintain alerts across multiple disparate tools driving you crazy? Tanzu Observability can consolidate metrics from multiple tools for consistent alert creation in one system, raising alert quality across your complete cloud app environment.

Tanzu Observability uniquely provides full-stack, multi-variant alerting. You can create advanced alerts based on multiple metrics across different domains, based on system-wide aggregated metrics and from metrics filtered by tags.

Express alert conditions using Tanzu Observability query language expressions and its 100+ analytics functions. Create as simple or as advanced an alert as your production systems require.

10 Examples of Smarter Monitoring Alerts

Application layer alerting is the best alerting. Yet such alerts need to be actionable to avoid alert noise and unnecessary 2AM wake up calls. Learn how your peers at top digital enterprises use automated analytics to create smarter alerts.

Learn about cloud application anomalies and how analytics-driven alerting can detect them faster and accurately. See how Ops and Dev can implement smarter alerts to auto detect cloud application problems without false positives.

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Fix Issues Faster with Superior Incident Collaboration

With Tanzu Observability, you can easily create different classes of alerts– e.g. critical, informational– and then route alerts by class accordingly.

Collaborate across teams by integrating Tanzu Observability alerts with DevOps tools like  Slack, PagerDuty, ServiceNow, HipChat, OpsGenie, Webhooks, email, etc.

Use Tanzu Observability’s Alert Target Templates to easily scale alert integrations across many types of notification systems.

Use Tanzu Observability’s Alert Target Utility function to make outputs to alert targets more readable, e.g. include a chart with a forwarded alert.

Correlate Alerts and Events on Metric Visualizations

You can display events or alerts as overlays on any metric visualization to correlate them with metric anomalies. Events can include external user events like deployment activities, the firing of an alert or the start of a maintenance window.

Manage Alerts across the Organization at Scale

SREs and DevOps engineers need centralized tooling to ensure alert management at scale across large organizations with many teams.

Tanzu Observability enables teams to customize their own alerts while SREs get centralized views across all alerts. You can also set policy controls to determine who can access which alerts or auto-disable alerts during maintenance windows.

Tanzu Observability also improves alert quality with alert backtesting. Hypothetical alerts can be depicted and fired using historical metrics data. Backtesting enables you to fine tune new or existing alert conditions before saving them.


"[Tanzu Observability] has a great UI for creating truly intelligent, dynamic alerts. Its query language is the best out there, and we love how alert creation is so well integrated right within the dashboard, not as some separate tool within the platform." - Julien Lemoine, Co-founder and CTO, Algolia


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