Datadog vs. Grafana Loki

Datadog works really well with complex microservices architecture like any E-commerce platform which will be having multiple services but they all are interdependent to others so in this scenario Datadog will be best to monitor these as it will show the transactions also between those microservices. If you are using multiple services in your architecture whether it will be cloud services or on prem services Datadog will be the best choice to monitor all those service with in Datadog so that you can see everything in a single place. But if you are having small architecture and few services in that then in that scenario you can use Datadog but it will be little costly as compared to other but obviously the features are very well.

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Grafana Loki can compile data logs for easy exploration of a service and enable quick troubleshooting and error tracing for engineers who may not have deeper access. However, Loki is dependent on the service logging complexity, which, depending on the logger, may not be robust enough and limit the value offered.

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• Create Dashboards as per application, environments, and Custom metrics in one panel.
• Log aggregation, one-stop Application monitoring tools for the whole infrastructure.
• Playbooks, SLA definition, success and error quotas, request visualizations.
• DB monitoring, Serverless stack monitoring.
• Alerting of Production incidents so we can quickly resolve the issues on time.

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• Access to many open-source dashboards, access to add many data-sources to gather and visualize data from.
• Grafana Loki does well gathering of logs from various data-sources, we can also filter the logs based on our needs.
• One stop solution for all the logs and monitoring.

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• Alert windows cause lag in notifications (e.g. if the alert window is X errors in 1 hour, we won't get alerted until the end of the 1 hour range)
• I would appreciate more supportive examples for how to filter and view metrics in the explorer
• I would like a more clear interface for metrics that are missing in a time frame, rather than only showing tags/etc. for metrics that were collected within the currently viewed time frame

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• Learning curve is very steep
• Longer term log insight is a challenge
• Very easy on AWS, integration with other systems can be more of a challenge

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There is some room for improvement, but the Datadog team sends out updates frequently, and the UI is user-friendly for engineers, with no significant loading issues or region-specific problems. That was one of the key reasons we preferred Datadog; our company has employees worldwide, and it wasn't difficult to transition to the tool.

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Grafana Loki makes accessing and viewing service logs easy for engineers who may not be familiar with going into service. However, useability can be limited if engineers are unaware of what the queries should look like or where in the service to direct Loki to look for logging.

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The support team usually gets it right. We did have a rather complicate issue setting up monitoring on a domain controller. However, they are usually responsive and helpful over chat. The downside would be I don’t think they have any phone support. If that is important to you this might not be a good fit.

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We are still trying other products, but people still like Datadog. After setting up a dashboard, it's great for monitoring instances on Datadog. Also, the DevOps team had a good time setting up Datadog. It means Datadog was way easier to set up compared to those others.

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First and foremost if Grafana Loki is based on CNCF open source projects so organizations can get freedom to choice to configure it at your own other main thing is Grafana Loki is totally free of cost and we can deploy it on our infrastructure. On compared with other managed services like Datadog, New Relic it is very expensive and we also don't have much control on the tools we use.

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• Saved us (time & money) from developing our own monitoring utilities that would pale in comparison
• Alerts allow us to remedy issues before our customers even know about them
• Tracking resource usage over time allows us to better plan for future needs, before it becomes a pain-point.

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• Company wide (realtime) insights in complex aws setup is great
• Dashboarding options are easily set up

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