We are often asked what’s the difference between Anodot and Datadog. Since both platforms monitor data at scale, using machine learning to detect anomalies and incidents, the differentiation might be unclear. So we’re using the real estate here to quickly clarify what each platform is built for, and why – despite some overlaps in features – these are two fundamentally different creatures.

If you are an existing Datadog customer, you probably know that it’s a great observability tool when it comes to collecting IT and APM health data. Datadog specializes in ingesting infrastructure and application machine data (logs), indexing them, making them searchable, and then providing BI tools, such as alerts, dashboards and reports, to monitor them. What Datadog is not is a self-service means for detecting business incidents across the enterprise.

Datadog falls short for:

  • Revenue & cost monitoring
  • Customer experience monitoring
  • Partner monitoring
  • Detection of slow leaks
  • Alert reduction

In the context of business monitoring, Datadog has the same limitations as other AIOps/APM platforms, in that its data coverage is limited and that hinders its ability to reduce time to detection and resolution of incidents across the business.

These prove to be the most challenging of Datadog’s limitations:

Siloed data and detection of revenue-impactful incidents

Datadog monitors log and app data. But not all revenue impactful issues can be observed through machine metrics. Very often, revenue issues occur without leaving a trace in the app or infrastructure data. For example, a surge in hourly cloud costs because of increased queries, a drop in traffic from a partner that’s testing out competitors, or a slump in conversions and purchases due to campaign efficiency issues will not show up on your infrastructure or application monitor — but will directly translate to revenue loss. Only monitoring and correlating between 100 percent of your data and metrics can surface these common types of common revenue bleeds.

Limited business and CX monitoring

Datadog doesn’t monitor business KPIs, and offers no business analytics. Many companies today try to bypass this significant blindspot by feeding business metrics into Datadog. In practice, however, this strategy fails because business metrics are fundamentally different to machine data. Business data is characterized by dynamic context, unknown topology and irregular sampling rates. The sheer volatility and unpredictability requires a diverse library of algorithms to monitor. That’s why monitoring business KPIs with Datadog fails to produce accurate and effective monitoring or reduce time to detection for revenue-impactful issues.

Manual monitoring with limited AI

While Datadog does offer limited anomaly detection capabilities, it requires users to decide when to apply anomaly detection, to what KPI, and at what conditions. Datadog’s bolted-on AI provides only a few algorithms, basic anomaly and outlier detection, manual correlation and has no root cause analysis. With Datadog, you need to manually create a monitor for each metric and set fixed alert options, such as deviations, algorithm, seasonality, daylight savings, rollup interval, and thresholds, for alerting and recovery. Of course, it is humanly impossible to scan thousands of KPIs in order to decide which KPI is eligible for anomaly detection and how it’s best evaluated.

Anodot, as a full-fledged Business Monitoring platform, is built from the ground up for the fastest detection and resolution of revenue-impactful incidents across the business. As opposed to infrastructure and application monitoring solutions like Datadog, Anodot sits higher up in the stack and provides real-time detection of incidents across the entire business to proactively detect revenue- and cost-impacting issues. Anodot leverages robust, patented machine learning technology to both learn the behavior of every single metric at extremely high granularity and map the network of correlations between metrics across silos. Anodot then mines the stream of incoming data to rapidly identify and score anomalies.

There are some significant contrasts to consider when evaluating the two platforms. Mainly, Anodot differs from Datadog in that it:

Monitors 100% of data

Why only monitor infrastructure and apps—when you can monitor your entire business? Not all impactful issues can be observed through IT and application metrics. Very often, revenue, cost, customer experience and partners issues occur without leaving a trace in the app or infrastructure data. Only monitoring and correlating between 100 percent of your data and metrics can surface these common types of revenue bleeds. Anodot scales to billions of metrics, with no restrictions to data or hardware.

Works autonomously

There’s no need to manually create a monitor or set alert conditions or thresholds: all metrics and dimensions are monitored all the time with the most appropriate alert conditions and algorithms. Anodot automatically selects the most appropriate algorithm for every metric, adapts to changes in metric behavior, and can switch algorithms in case patterns change. There are no limitations on metrics with strong trends and recurring patterns: Anodot’s robust algorithm library is built to autonomously baseline and monitor any type of signal.

Fastest incident detection and correlation

Anodot monitors data in real time and continuously. Its patented correlation engine automatically works across all data sources, metrics and dimensions. complete picture of every incident, including root cause analysis, for lightning-fast resolution. Alerts include grouped anomalies and their shared dimensions to create a complete picture of each incident to accelerate root cause analysis and to improve collaboration across business/product/DevOps teams. Anodot’s customers are able to catch incidents 15x faster across the business, cutting incident-related costs by 70 percent.

Download the comprehensive Anodot vs. Datadog comparison

Written by Amit Levi

As Anodot's VP Product and Marketing, Amit Levi brings vast experience in planning, developing and shipping large-scale data and analytics products to top mobile and web companies. A product and data expert, Amit has a unique ability to explain complex requirements in simple words. His product leadership has led to major revenue growth at both Yokee Music and Cooladata.

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