Lead Engineer at a tech vendor with 51-200 employees
Real User
Top 5
Apr 13, 2026
My main use case for Datadog Services revolves around APM, and we are also using it for metrics. The metric solutioning, along with sending the metrics and getting it displayed on the dashboard, is extremely good with Datadog Services, and we have a lot of performance dashboards set up where we analyze what is wrong with the system when something goes down. Whenever an incident comes in, we just open our dashboard to check which components are showing spikes, whether they are CPU spikes, memory spikes, or load averages, or if there are some network bottlenecks, and all this analysis is done via our Datadog Services dashboards. To facilitate this, we emit system metrics, and there are some custom metrics also which indicate the success criteria, along with the amount of documents shared in Datadog Services to ensure that things work as expected. We have a sampling rate of about thirty percent, which I think we've reduced to twenty percent now, as the data was really high when using Datadog Services. In this scenario, what we observed is that we have significantly reduced our billing with Datadog Services when we started using sampling. The only problem I see with Datadog Services is the cardinality factor. If we increase the cardinality, the billing becomes extremely high, and when I'm sending a lot of metrics, one-on-one metrics are fine. However, when the cardinality increases and if there are unique events sent inside Datadog Services via the OTEL collector, we encounter many problems. Otherwise, the solutioning of Datadog Services works excellently well with no issues.
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My main use case for Datadog Services revolves around APM, and we are also using it for metrics. The metric solutioning, along with sending the metrics and getting it displayed on the dashboard, is extremely good with Datadog Services, and we have a lot of performance dashboards set up where we analyze what is wrong with the system when something goes down. Whenever an incident comes in, we just open our dashboard to check which components are showing spikes, whether they are CPU spikes, memory spikes, or load averages, or if there are some network bottlenecks, and all this analysis is done via our Datadog Services dashboards. To facilitate this, we emit system metrics, and there are some custom metrics also which indicate the success criteria, along with the amount of documents shared in Datadog Services to ensure that things work as expected. We have a sampling rate of about thirty percent, which I think we've reduced to twenty percent now, as the data was really high when using Datadog Services. In this scenario, what we observed is that we have significantly reduced our billing with Datadog Services when we started using sampling. The only problem I see with Datadog Services is the cardinality factor. If we increase the cardinality, the billing becomes extremely high, and when I'm sending a lot of metrics, one-on-one metrics are fine. However, when the cardinality increases and if there are unique events sent inside Datadog Services via the OTEL collector, we encounter many problems. Otherwise, the solutioning of Datadog Services works excellently well with no issues.