# Best AIOps solutions for cloud infrastructure monitoring?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">I’m trying to find the Best AIOps solutions for cloud infrastructure monitoring. I am analyzing this more as a cloud-ops question than a generic monitoring question. The decision gets messy because some platforms win on telemetry breadth, some on automatic dependency mapping, and some on reducing event noise after the data is already flowing. What I’m specifically looking for in these AIOps platforms is:</p><ul>
<li>tools that give fast cloud-wide visibility,</li>
<li>tools that automatically explain service impact,</li>
<li>and tools that reduce cloud alert noise without hiding real issues.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">The broader <a class="a a--md" elv="true" href="https://www.g2.com/categories/aiops-platforms?utm_source=chatgpt.com"><strong>AIOps Platforms category</strong></a> is a good starting point before the product list. Here are my top choices based on the above mentioned criteria:</p><ol>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/datadog/reviews?utm_source=chatgpt.com"><strong>Datadog</strong></a><strong> </strong>— Best fit for cloud-first teams that want metrics, logs, traces, and cloud-service data correlated in one place. It looks especially strong when the real need is fast instrumentation plus dashboards that many teams can actually use.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/dynatrace/reviews?utm_source=chatgpt.com"><strong>Dynatrace</strong></a> — More compelling when automatic discovery, problem evolution, business impact, and root-cause context matter more than manual dashboard building.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/logicmonitor/reviews?utm_source=chatgpt.com"><strong>LogicMonitor</strong></a> — A practical option when “cloud monitoring” still includes a meaningful amount of on-prem and multi-cloud complexity. Its hybrid observability, topology mapping, and intelligent log analysis make it more than just a cloud-only play.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/new-relic/reviews?utm_source=chatgpt.com"><strong>New Relic</strong></a> — A strong candidate for engineering-led teams that already think in telemetry and want service maps, transaction visibility, and broad open-source integrations without forcing a totally new workflow.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/splunk-it-service-intelligence-itsi/reviews?utm_source=chatgpt.com"><strong>Splunk IT Service Intelligence (ITSI)</strong></a> — I’d keep this in the mix for enterprises that already have Splunk data in motion and want a service-centric layer for predictive monitoring and integrated workflows.</li>
</ol><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">For teams running one of these in production, where do you still end up doing the most manual work: instrumentation, alert tuning, or cross-team triage when a cloud issue spans apps, infra, and service ownership?</p>

##### Post Metadata
- Posted at: 4 months ago
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;Curious to hear from folks running these in prod, which one actually cuts through the noise during incidents? A lot of tools promise AI-driven alert reduction, but do they really reduce pager fatigue or just reshuffle alerts into a different dashboard? Also, how well do they handle multi-cloud and Kubernetes without constant tuning?&lt;/p&gt;

##### Comment Metadata
- Posted at: 4 months ago




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