Unified Monitoring with Datadog
Overall Satisfaction with Datadog
in my org, Datadog is used to for monitoring the application performance, and managing logs across different environments. we use this for tracking latency, throughput, and errors in our microservices. We use it for creating custom dashboards and alerts on deployments. also for monitoring cloud resources and Kubernetes clusters. this makes debugging faster and cross team interaction is smooth.
Pros
- Tracking Microservice Performance
- managing the logs
- Monitors Cloud and Kubernetes
- Speeds Up Debugging and dashboards
Cons
- costing incerease based on the logs volume.
- Creating monitors, dashboards, or logs requires some context on Datadog syntax.
- role based access needs to be more granular if someone has larger team members
- dashboards are making easier to find the pods going into crash loop due to memory issues
- less time to debug and finding the RCA's
- Increase the productivity of each developer.
- Prometheus and Grafana
we primarily use Kubernetes, and Prometheus is great for collecting time series metrics, especially in Kubernetes. and Grafana is used for dashboards. As these are open source, we host them and manage them internally. We choose Datadog because of its logs, traces, and alerting, where it adds value. Setting up Datadog is easier than Grafana.
Do you think Datadog delivers good value for the price?
Yes
Are you happy with Datadog's feature set?
Yes
Did Datadog live up to sales and marketing promises?
Yes
Did implementation of Datadog go as expected?
Yes
Would you buy Datadog again?
Yes

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