LogRocket vs. New Relic

It's incredible for anyone who works at a saas and does customer support. It's the first job I've ever used Logrocket at and it adds such a great layer of service

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New Relic its an excellent tool for monitoring services used on the SAAS universe, like web servers, relational and nosql dbms, reverse proxies, text databases, etc. Its also a powerful tool to monitor resource usage on said servers. However, its not well fitted to monitor custom services - if you need to generate alerts based on logs or database information, for example

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• enhances the customer support journey
• allows non-techy personnel like myself to dive into customer issues

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• It gives us insights of applications, infrastructure, clouds very well.
• The alerting system in New Relic helps us to easily identify the root cause which reduces the MTTR.
• It has new features related to AI which helps to troubleshoot any problem in a very short time it gives future predictions also.

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• It's a shame recordings are capped to the previous month, but I believe this might be due to the plan we're on?
• Cropping sessions could be simplified I think - particularly the cropping tool's design

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• And while powerful, building tailored dashboards with organ-specific metrics (such as energy load variance across regions) can be difficult to navigate. The UI isn't as drag-and-drop easy, and query-based widgets typically involve some trial and error for non-devs.
• Alerts may be hypersensitive or over general. I We often get a spam of non-critical alerts while doing load testing, all overhauling to me alone and making it difficult to identify actual issues especially in energy systems where spikes are very common.
• With our expanding fleet of Iot devices, the per-host pricing model is becoming expensive, quickly. More detailed billing based on microservices, or that works at sensor level, would make it more adaptable for energy platforms.

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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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I have given this much rating as I am used New Relic in different sectors and for different use cases like its K8s monitoring, infra monitoring, full stack monitoring as compare to other tools New Relic gives data in a formatted and connected way, and also it is giving us value for money. It also launches new features day by day which helps users to track the issue very quickly. It also supports OTel integrations which is the latest trend of observability tools. thats why I had given this much rating to New Relic.

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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 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.

Data Dog has solutions that look more attractive, but not at their price point. We have also tried to build a solution straight from the Cloud, where our business is built, but some things are too hard to replicate. This shows that New Relic is useful and helps our efficiency.

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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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• 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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