New Relic APM, Logs + Infrastructure Monitoring (PAYG with Free Tier)

Since I'm a performance engineer, I typically use New Relic day-to-day for investigating any performance bottlenecks identified during our performance testing of any application. I look at the results, identify the root cause behind performance issues, and create dashboards to monitor the observability perspective of the tool for production as well as the QA environment.

New Relic helps me every day because whenever we face any issue, the very first thing we do is check the dashboard we have created. We browse through the dashboard to look into different aspects including CPU, memory, logs, and different transactions. Once we identify what might be the reason, we either directly go into those metrics in detail to look for the root cause or look into transactions to identify if a performance bottleneck exists, then at what layer or step it has occurred and what is causing that performance issue. Once we identify that, it is very easy to look into the code for that particular method or area to pinpoint the root cause, inform the developers about the issue, and identify possible solutions for it.

I have been using New Relic extensively for performance engineering troubleshooting and root cause analysis for our large-scale SaaS environment, which has become one of the primary tools we heavily rely on during our performance testing and production issues investigations. I appreciate New Relic's ability to provide end-to-end visibility across the entire application stack, and its APM capabilities are particularly useful for our day-to-day tasks by identifying slow transactions, bottleneck methods, external service dependencies, database performance issues, and error hotspots. We frequently use transaction traces, distributed tracing, thread profiling, database analysis, and external call breakdowns to pinpoint where the response time is being spent. With our Kubernetes workloads moving to containerized environments, the Kubernetes monitoring capabilities that New Relic provides are valuable as they allow us to correlate application performance with pod-level metrics, resource utilization, deployments, and infrastructure behavior, helping reduce the time required to identify performance bottlenecks. The integration we have seen between APM, Kubernetes monitoring, logs, and distributed tracing provides a much more complete picture than using disconnected tools. The NRQL feature allows us and our team tremendous flexibility to build custom dashboards, perform deep analysis, and answer specific questions that may not be available through standard views. The dashboarding capabilities we rely on are powerful, allowing both engineers and leadership to view performance trends and operational metrics meaningfully. Leadership is not technical, so being able to create that dashboard helps, and for engineers, alerts can be automatically triggered without needing developers or QA engineers to investigate further. If there's nothing wrong, they are happy that all is well, and if there is an issue, alerts are triggered, prompting them to go to New Relic to identify the cause and analyze the issue.

The best feature of New Relic is the effectiveness of the observability platform. The combination of APM, distributed tracing, Kubernetes monitoring, logs, database visibility, profiling, and analytics capability significantly reduces our troubleshooting time, and those are the features I appreciate about New Relic.

I think we utilize all of the features available due to our architecture, where we must go through everything. There is a sequence in how we go through these features: first, we look at the dashboard for a holistic overview, then check the transactions to identify degraded ones, move to traces to find the cause of slowness, and might have to go into logs, database queries, external calls, or distributed tracing.

While I appreciate many aspects of New Relic, I believe the product could improve in some areas—specifically, some advanced capabilities can have a learning curve for new users, and the licensing and consumption model can be difficult to predict, particularly in environments generating large volumes of telemetry data. Organizations may need to invest time in proper instrumentation and dashboard design, as we did, to reach the current stage we are now.

The licensing and consumption model can be unpredictable due to dependency on telemetry data, making it challenging for environments generating large volumes to estimate costs. Organizations need to invest time to explore New Relic's extensive functionality and properly instrument features to realize their full impact. Additionally, designing dashboards is not straightforward; users need to create their NRQL queries before they can fully understand the value.

Regarding the user interface, I have noticed changes in past years, with some features we appreciated in previous versions and others in new ones. One issue we have reported is the text overlaying on dashboards when editing, which makes it challenging to navigate. Another issue is that some users experience blurred text, requiring a system restart and login to rectify.

I have been using New Relic for more than eight years now.

Before using New Relic, we used Grafana, Splunk, VividCortex, and Opster to monitor and observe each layer, requiring different tools for each, which slowed us down. With New Relic, we can visualize all underlying systems architecture and metrics in one place, which helps us correlate issues faster and easier, allowing us to save almost seven to eight hours of analysis time previously spent on multiple systems and tools.

New Relic is deployed in our organization as a private cloud or possibly hybrid cloud, as it can function both ways.

I advise that New Relic is the best APM tool available, providing everything you need to pinpoint root causes for performance bottlenecks and serving as an effective observability tool for production telemetry metrics. There are open-source tools that are easier to use but have limitations, whereas New Relic offers a wide variety of useful features for performance engineers, SREs, and leadership. While it is costly, the valuable data and time saved justify the investment, so I encourage others to give it a try.

I would rate New Relic an eight out of ten. We utilize nearly 70 to 80 percent of its features, so eight seems appropriate given the ease with which users can work and instrument New Relic.

I choose eight out of ten as we are yet to explore 20 percent of New Relic features, and the ease of creating alerts, dashboards, and instrumentation often requires expertise. Live tracing of a transaction would enhance the platform, allowing us to trace transaction data flow with an architectural diagram to visualize time spent, which would elevate my rating to a 9 or 10.

The time savings initially amounted to seven to eight hours per person per week, and increased to almost 24 hours after creating dashboards with automated alerting systems in place. Now, we can directly get alerts during our tests and have a holistic view of the architecture's performance, enabling us to quickly identify any bottlenecks. Since we save time, we can create better dashboards and invest more time in harnessing other areas of the applications we test. My overall rating for New Relic is eight out of ten.

My day-to-day activities in New Relic include infrastructure monitoring, APM monitoring, browser monitoring, and database monitoring. In cloud environments, I monitor multiple clouds like AWS, GCP, and Azure. The features I use inside New Relic include alerts, service levels, parsing rules, dashboards, workloads, and muting rules.

Recently, I received an alert for a login failure in my application, so I went to New Relic and checked where the issue was. Since we have set up complete distributed tracing of that particular journey and introduced custom correlation IDs for all the journeys, whenever we get any error or transaction, we obtain that particular correlation ID for that transaction. That correlation ID is unique for all transactions, so when I got that login error during my recent troubleshooting, I checked the alert in New Relic to understand why it was triggered. We discovered an internal server issue by examining the logs in New Relic and troubleshooted that issue effectively.

The main use cases with New Relic include browser monitoring and cloud services monitoring. In cloud services monitoring, we are using Lambda functions from AWS, and from Azure, we are using APIM, app gateway, and Azure functions. We use New Relic to monitor those particular resources to identify where we are encountering issues and what challenges we face. The recent features that New Relic has launched, including AI agent integration, are very helpful for faster troubleshooting, allowing us to easily diagnose the root cause of any incidents. I am looking forward to that particular feature in the future.

The features that performed very well include custom visualization in New Relic, which allows me to create a dashboard tailored to our specific needs. There are no restrictions on charts, and by using React, I can easily create those types of dashboards. New Relic has also introduced a good feature for Agentic AI integration, and they have launched the One App integration. This integration allows different types of applications within a cluster to be included in the form of APM. Additionally, a new feature launched troubleshoots issues automatically; for example, when I received an alert for my EC2 machine usage reaching ninety percent, I got a notification in my Slack channel, and by giving a thumbs up, New Relic's SRE agent connects with the AWS Bedrock agent to troubleshoot automatically and scale up the EC2 machine without manual intervention. These features of New Relic stand out significantly.

The AI integration helps us in different ways, particularly in root cause analysis (RCA). I was using the AI RCA feature in New Relic's incident tab, which provides a button to generate RCA by checking details of past events related to that particular incident. This allows me to easily identify issues and troubleshoot them. For instance, after integrating my EC2 machine with New Relic, I received an alert at two a.m. for memory usage reaching eighty percent. After receiving that alert in my notification channel, I enabled the AI agent to provide a complete RCA and solution for the issue. Once I approved the suggested solution, the AI agent automatically scaled up my EC2, allowing me to troubleshoot the issue efficiently without further intervention. Using these New Relic features significantly reduces our mean time to detect (MTDD) and mean time to resolution (MTTR).

Other features include the custom visualization capability, which allows us to better visualize our data. The default dashboards in New Relic have a limited number of widget types, so for specific visualizations such as spider maps, I cannot create that with the default widgets. Thus, the custom visualization feature is very helpful for that. New Relic has recently launched NR Lens, which allows querying data from different sources; previously, New Relic only provided access to data within their database (NRDB), but now we can query data from platforms such as Google Sheets. Integrating various platforms with New Relic simplifies the data querying process, and there are excellent Agentic integrations with notification channels such as ServiceNow, enabling easy communication with New Relic AI. These are powerful features of New Relic.

I have noticed discrepancies between New Relic's documentation and Terraform resources. For example, there have been instances where new features launched in the New Relic UI have not been updated in the Terraform provider. Improving the synchronization between the UI and Terraform would be very beneficial for us.

I would also point out that the query section within the UI has slowed down in response times over the past few months. Previously, querying anything in New Relic provided quicker results, so reducing the time taken to provide query results would be helpful for everyone.

I have been using New Relic for three plus years.

New Relic is stable.

Regarding New Relic's scalability, it excels at the enterprise level for cloud integrations that can utilize tags. However, for other integrations such as APM or Kubernetes, it is less scalable as each application requires its own agent for integration.

The customer support experience was good, with faster troubleshooting provided by New Relic's support team.

We previously used Grafana, Prometheus, and Datadog, but found them to be costly. In addition, Grafana and Prometheus do not offer the same range of features that New Relic provides, which is why we switched.

We observe a return on investment with New Relic. Previously, we needed more manpower to troubleshoot issues and determine exact RCAs, which consumed both time and money. After implementing New Relic, we have decreased staffing requirements while saving time and money.

I evaluated other options, including Datadog, Dynatrace, Coralogix, Grafana, Prometheus, Azure Monitor, and AWS CloudWatch.

Compared to other observability competitors, New Relic offers better pricing for their features, and the user interface is user-friendly.

Using New Relic, we can showcase business KPIs data and compare it with trends from the last month or year. We can easily check whether our business performance is declining, stable, or improving, as well as the revenue generated from our website. New Relic allows us to analyze this data efficiently if we are sending it to their platform. Additionally, it has a wide range of integrations with third-party platforms, major clouds, and multiple backend languages.

My advice for others considering New Relic is to focus on cost, as it is competitive compared to other options. I would also recommend the excellent features New Relic provides within this price range, such as the Agentic AI feature, SRE agent capabilities, custom dashboards, and custom visualization, which help in proactivity regarding alerts and awareness of potential issues. I would rate my overall experience with New Relic a nine out of ten.

I use New Relic to troubleshoot my applications and look at metrics and logs to solve problems in my day-to-day work. In my daily use, my application operates at a large scale, and because of this, we need to look at the metrics to check the health of the application and monitor the movements of the applications.

In my opinion, the best feature that New Relic offers is the filtering of the metrics and the logs. When we need to look at the metrics with New Relic, I can filter by the name of the application, which is very important and has helped me. With the metrics from New Relic, we can improve the performance and reduce the downtime because we can identify problems and solve them more quickly. New Relic has positively impacted my organization because it has helped improve the performance and reduce the downtime of the application.

I think New Relic can be more simple to use because for beginners it can be more difficult to use, so I think New Relic can improve in that aspect. There can be more improvements, and I think New Relic can be more useful, as it is hard for beginners.

I work in my current field of software engineering for one year and five months.

New Relic is stable and reliable for me.

New Relic has good scalability and can handle my growing needs.

I previously used Datadog before New Relic, and I switched from Datadog because I thought it was simpler to use. In my day, I use both Datadog and New Relic along with other applications.

I evaluated other options before choosing New Relic, including Datadog, Grafana, and OpenTelemetry.

One relevant metric I appreciate is the metrics of the helpful applications and the performance metrics from New Relic. My advice to others looking into using New Relic is to study and construct your metrics with good patterns. I would rate this review an 8.