
Datadog gives us a single observability layer that ties metrics, logs, traces, and AI-driven insights together. What I like most is how fast it is to instrument new services, define custom metrics, and build dashboards that actually help teams make decisions. We also use Datadog extensively for deploying internal AI agents—its event streams, log ingestions, and metric pipelines make it easy to create intelligent triggers and automated workflows.
The correlation between logs → metrics → alerts is incredibly powerful, and the AI-based anomaly detection has helped us reduce blind spots in our observability stack. Review collected by and hosted on G2.com.
The biggest drawback is cost. Datadog becomes expensive very quickly—especially when log volumes grow or when you create many custom business metrics. Even with strict retention windows and log pipelines, the monthly bill requires constant governance. It’s a powerful platform, but the pricing model can be a challenge for teams that want broad coverage without compromising on granularity. Review collected by and hosted on G2.com.