# Best tools for automated incident detection

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">When I explored G2’s Enterprise Monitoring category, I was looking for <a class="a a--md" elv="true" href="https://www.g2.com/categories/enterprise-monitoring">the best tools for automated incident detection</a>. </p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Based on reviews and my exploration, these three platforms stood out:</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/dynatrace/reviews">Dynatrace</a>: I noticed the emphasis on automated discovery and AI-assisted root cause analysis, which sounds ideal when incidents span apps, infra, and cloud services. But in practice, did the automation actually help teams detect incidents sooner, or did you still need a lot of manual tuning to avoid false positives and keep it accurate?</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/datadog/reviews">Datadog</a>: I liked the idea of pulling metrics, logs, traces, and user signals into one place, especially for catching issues before customers report them. But did it truly improve automated incident detection across large environments, or did teams end up with lots of alert rules and dashboards that required constant maintenance to stay useful?</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/ibm-instana/reviews">IBM Instana</a>: What stood out to me was the “fast time-to-value” positioning for tracing and correlating app issues with underlying infrastructure. Did it deliver reliable automated detection in real enterprise setups, or were there gaps once you scaled beyond a few services?</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"></p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Alongside the tooling, I’m also looking at the practice side of this, things like AIOps approaches and what teams do to improve signal quality once automation is turned on. If you have a strong root-cause workflow that pairs well with automated detection, I’m curious what made it stick.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"></p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">If you’ve reviewed any of these tools, I’d be keen to know what was the implementation curve like for automated incident detection? </p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"></p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"></p>

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- Posted at: 5 months ago
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