Datadog vs. New Relic

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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I have used New Relic in different scenarios like monitoring my production infrastructure and applications which helps us to reduce the downtime of my applications and websites and also I have used the synthetic monitoring feature which helps to proactively monitor our websites availability. Along with this I have also used New Relic for cloud resources cost monitoring which helps to reduce my cloud cost. Also I have used mobile application monitoring which helps me to trace the sessions easily and I can easily reduce my RCA through the help of that.

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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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• New Relic APM allows us to follow up transactions across services and trace performance bottlenecks in real-time, crucial when monitoring the processing of energy loads or predictive maintenance algorithms.
• It gives us deep visibility into our cloud servers, containers and IOT gateways, so we can catch CPU spikes or memory leaks which can impact the data we ingest from the field devices.
• We develop custom dashboards for monitoring trends of power consumption, abnormality in sensors and API health. In conjunction with alerting, it makes sure we are fixing issues before customers even see them.

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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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• Support for SQL like query with more functional features of analysis while viewing distributed tracing.
• support for very low level specific integration from APIs to classes to functions to piece of code
• More detailed documentation, as we faced issues while integrating for the first time.

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The only issue that we have had with New Relic is that the price might be a little expensive for smaller companies. The amount of data you store in New Relic impacts the cost, and can get away from you if you don't work closely with the vendor. Overall though the application is top notch.

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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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New Relic helps in observability setup for the critical environments and getting known about the issues and troubleshoot the applications and services. Alerts helps in knowing the abnormal state of the system and services, Dashboards are used for visualizing the key metrics and muting the unwanted notifications and dropping the extra data from the source.

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Never observed an outage

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there are times where browser cache will cause issues that require you to clear your browser before continuing.

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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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The support team has been really helpful and resolved most of the issues on time. However, for a couple of issues, several follow-ups were needed to elicit a reasonable response. The issue was deeply technical and could have been investigated only by their Architects, and bringing them into the ticket took longer than needed

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It's better to start by implementing New Relic in one project and test everything. Try to follow best recommended practices and read all the official documentation. Everything seems well tested. Then, start by installing agents to the rest of your projects and keep a close look to all logs and metrics New Relic gives you.

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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New Relic has full stack visibility and gives us all options for observability like one stop shop. It gives you front end, backend as well synthetic monitoring capabilities. Every other feature built into one cost model (usually) which ties to data that you send, it helps you leverage all features without having to pay additional charge for feature

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Agent deployment is easily integrated into our workflow. Adding the agent to new servers is quick and painless

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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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• We were able to quickly identify our most time consuming APIs. In some cases we were able to bring down times for some apis from 4s to 200ms.
• We were able to identify our slowest database queries and optimize them for quicker response times.

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