Integrating Pingdom with SignifAI’s Artificial Intelligence and Machine Learning Platform
Every Site Reliability Engineer (SRE) knows that the more DevOps tools you use to ensure adequate monitoring coverage, the more likely you’ll end up overloaded with alerts and data analysis tasks. Paradoxically, this avalanche of alerts and data might actually cause you to miss precisely the issues you are trying to identify. To help in this situation, SignifAI delivers powerful real-time and predictive insights to DevOps teams by correlating their event, log and metrics data using a combination of artificial intelligence, machine learning and the team’s own expertise.

SignifAI offers 60+ integrations right out-of-the-box with technologies like AWS, New Relic, AppDynamics, Nagios and Pager Duty. The available integrations cover the most popular DevOps tools used for infrastructure, application, notification, collaboration and deployment tasks. In this week’s post we’ll take a look at how easy it is integrate Pingdom’s web and end-point monitoring capabilities with SignifAI’s machine intelligence platform to start making powerful correlations with the other tools in your DevOps toolchain.
What is Pingdom? Pingdom monitors your external web sites and other endpoints, tracking whenever your website is down or if transactions are slow. Pingdom tests and verify your website’s availability every minute, automatically.
The benefits of Pingdom and SignifAI integration
Our mission at SignifAI is to augment a DevOps team’s existing tools with the power of AI and machine learning so they can perform faster and more accurate root cause analysis. We also designed our platform so that it would fit into existing triage workflows vs working in a way that is different to what a team is unaccustomed to.
Integrating Pingdom with SignifAI delivers the following benefits above and beyond what Pingdom alone offers:
- Correlation: Pingdom is a data silo of availability monitoring data. SignifAI uses AI and machine learning to correlate Pingdom’s data with additional sources of monitoring data including logs, metrics and events like Spunk, Elastic, Datadog, etc. In turn, these powerful correlations will give you much richer context around an alert than just the associated Pingdom data.
- Alert Noise: SignifAI doesn’t just correlate the monitoring data from different tools, it correlates the associated alerts as well. So, instead of getting multiple alerts from multiple tools concerning what ultimately is a single issue, SignifAI intelligently groups all the relevant alerts together into an issue card. This makes it easy to see what alerts should be prioritized, which tools are reporting issues and what the underlying data is that can help get you to a remediation faster.
- Predictive Insights and Anomaly Detection: SignifAI looks at your Pingdom data both in real-time and historically to then correlate it with your other log, metric and events data to surface predictive insights and outliers in real-time or in daily, weekly and monthly alerts.
- Knowledge Capture: SignifAI’s machine intelligence is trained using algorithms based on industry and vendor best practices, SignifAI’s own operational expertise plus most importantly, your expertise. As your team resolves issues, SignifAI uses your documented solutions, hints, annotations, retros and runbooks to deliver a fully customized monitoring solution specifically tailored to your environment.


Getting started: Active Inspector vs Web Collector
There are two different ways in which SignifAI can integrate with Pingdom:
Web Collector
SignifAI’s Web Collector sensor integrates over a webhook and passively collects the data and metrics you’ve already configured to capture in Pingdom.
Active Inspector™
SignifAI’s Active Inspector™ collects information in a secure way using a platform-specific API. Using this method of collection, you are not required to configure collected metric/event types or limits. Instead, SignifAI will automatically collect the most relevant data points, events, and metrics with the highest value for analysis and actionable information. It then then applies anomaly detection algorithms and predictive analysis to notify you in advance of potential issues. This means SignifAI will detect potential problems from your Pingdom data without any pre-configuration of alerts or setting up a webhook. Providing pattern analysis and surfacing unknown patterns from your entire data already exists inside your Pingdom platform.
Integrating Pingdom with SignifAI’s Active Inspector

- On the Integration tab of Pingdom register a new application. Name it “SignifAI.”
- Log in to the main SignifAI console, navigate to the Sensors tab and select Pingdom.
- Copy your new Pingdom API key into SignifAI.
- You’ll need to also provide your username with login credentials to Pingdom. This is your organization user or your personal user@domain.
Integrating Pingdom with SignifAI’s Web Collector
With SignifAI’s Web Collector you can connect to Pingdom and automatically analyze all test results, metrics and events correlated with your other monitoring tools like Splunk, AWS and New Relic. In order for SignifAI to collect all notifications and events in SignifAI, a webhook setup is needed. Here’s how:

Web Collector Installation
- Log in to the main SignifAI console, navigate to the Sensors tab.
- Select Pingdom and navigate to the Collector URL tab.
- Copy the SignifAI Web Collector for Pingdom URL.
- On the Integration tab of Pingdom, add new webhook integration and enter the SignifAI collector URL.
- On each monitoring check, add a notification using the webhook.
Next steps
- Sign up for a FREE trial
- Watch the 2 minute product demo
- Watch the 30 minute in-depth company and product presentation
Originally published at blog.signifai.io on August 22, 2017.

