New Relic Solutions [Private Offer Only]

The main requirement for clients using New Relic Solutions [Private Offer Only] is primarily application performance issues, root cause analysis, high latency, errors, and services failures. Sometimes it is used for infrastructure sizing and performance bottleneck identification. Performance bottleneck analysis is always used from an infrastructure point of view. However, infrastructure sizing and cloud optimization are not used as drastically.

Regarding integration, we are not directly integrating New Relic Solutions [Private Offer Only] with Jira or Slack, nor are we using it with cloud services. Our team directly uses the New Relic Solutions [Private Offer Only] platform to create integration with email services instead of Jira. One of the reasons we want to have human intervention before New Relic sends out a notification to specific people. We do not directly perform the integration.

When we talk about performance metrics with New Relic Solutions [Private Offer Only], the first critical metric for us is response time latency, which measures how long requests take to complete as an average latency, then P50, P95, P99 latency, and endpoint level response time. The second critical parameter is throughput, which is the request per second, transaction per minute, or how many concurrent requests are being processed.

The third critical point is error rate, which includes HTTP 5XX or HTTP 4XX responses, failed transactions, exceptions, or timeout rates. These are the most critical performance metrics we use. In addition to these, we use other parameters that are also valuable, but these are the critical metrics we rely on.

I believe the dashboards impact our application performance positively. The dashboards are effective because they help us very specifically. The dashboards are customizable, and we use them in the way we want, rather than simply using whatever dashboard New Relic Solutions [Private Offer Only] has provided by default.

From my recommendation perspective, the impacted areas are response time, throughput request, error rate, CPU monitoring consumption, garbage collection, and startup times. These are the areas where we typically use the dashboards, and they have had a positive impact on our organization.

The biggest advantage of New Relic Solutions [Private Offer Only] from an application performance issue identification point of view is that it helps us address application performance through the APM tool they provided. This helps us identify application level issues and bottlenecks. When there are production issues or sporadic issues, identification of such issues may not be possible directly using functional testing at testing time. We use the root cause tools specifically coming from APM for investigation and troubleshooting.

As a part of the application performance analysis, we mainly try to identify which services or areas of the application are having latency issues or response quality issues. This is used mainly as a part of L1 and L2 support. Additionally, when it comes to latency issues and service failures from AWS or third-party services, New Relic Solutions [Private Offer Only] helps us trace where the particular issue is coming from and put an alert mechanism system on top of it, which triggers alerts and sends emails to the respective stakeholders. If certain services are failing frequently, all stakeholders at the customer end are automatically notified based on the severity of the issue.

Regarding the use of AI-powered anomaly detection mechanisms in New Relic Solutions [Private Offer Only], my answer is both affirmative and negative. Our concurrent users are not consistent throughout the year and throughout the day. As we are part of the education sector, our utilization happens specifically at certain times. AI anomaly detection might not apply to us very frequently because it compares behavior against its peers. For example, during back to school season, we have maximum transactions and maximum CPU and memory usage, which is not the same as throughout the year. Sometimes New Relic Solutions [Private Offer Only] and other AI tools detect this as an anomaly when it is not.

Where we use anomaly detection is mainly on the application performance side, when there is an unexpected latency increase, error spike, throughput drop, or potential service degradation. We also use it on cloud usage and spending signals to understand if there is any anomaly happening in the utilization of cloud infrastructure. However, from an infrastructure perspective, we may not use it frequently, but for cloud usage, spending, and application performance, we do use it. I would not say we use it religiously 100 percent; we use it as one of the influencers.

Dependency tracking with New Relic Solutions [Private Offer Only] helps resolve performance bottlenecks in our case because that is how we do root cause analysis. Without the dependency matrix and dependency tracking, we would not be able to see service to service latencies or internal APIs, microservices, and internal APIs, as well as the request volumes received for specific services. Recently, one of our services, based on the dependency matrix, we were able to optimize it. Instead of the service calling another service once to provide specific results, we were actually calling those services multiple times in a loop, which was causing the problem.

Additionally, dependency tracking helps us identify the database and cache, specifically Redis and others, and determine which areas we need to cache as a part of Redis and other solutions. We can analyze database query execution time and pool utilization. One example involved a customer who provided third-party APIs to us, which were pulling our application performance down. New Relic Solutions [Private Offer Only] dependency matrix helped us prove that it was not an application which we built that had performance issues, but rather a performance issue of the third-party library and APIs which we are using, as the response time was coming slow from that side. This enabled us to present our case to the customer.

I believe New Relic Solutions [Private Offer Only] needs to work on AI anomaly detection and reduce false positive alerts through the context threshold. Regarding dependency visibility and root cause analysis, New Relic Solutions [Private Offer Only] should be able to detect single points of failure and cascading failures instead of allowing users to perform tracing activity manually. This would be beneficial because maximum time goes into manual tracing activities.

Additionally, New Relic Solutions [Private Offer Only] should help us identify if there are thread pools and connection pools in performance optimization, as pool data visibility enables us to determine whether we need to increase the pool size or connection size.

We started working with New Relic Solutions [Private Offer Only] over 8 years ago.

When a customer asks for my choice, my first priority is New Relic Solutions [Private Offer Only], my second priority is Datadog, and my third priority is Grafana.

We have not noticed any issues with the scalability of New Relic Solutions [Private Offer Only] up to this point.

The installation process for New Relic Solutions [Private Offer Only] was smooth, and we never faced any problems.

Our deployment of New Relic Solutions [Private Offer Only] is mostly on-cloud.

AWS is the native cloud we use with New Relic Solutions [Private Offer Only]. We primarily use only AWS, and I do not see any instance where we have implemented New Relic Solutions [Private Offer Only] on Azure or GCP.

We are not a consumer of New Relic Solutions [Private Offer Only]. We are mainly integrators.

The cost of New Relic Solutions [Private Offer Only], as compared to other products in parallel, is something I have always recommended. However, when it comes to price, there are other tools that beat it. My recommendation is always for New Relic Solutions [Private Offer Only], but we acknowledge that other alternatives may be more cost-competitive.

Datadog is a tool we compare to New Relic Solutions [Private Offer Only] regarding cost. Whenever customers ask us for our recommendation, they specifically expect us to provide multiple options. Another alternative which is more affordable is Grafana Cloud. We have not used Dynatrace, but we can see that Datadog and Grafana are the main alternatives. Most customers probably choose Datadog. Grafana is not at the same level because it is mostly at the free tier with very few paid services. Datadog is a good option, but it is not comparable to New Relic Solutions [Private Offer Only]. My first preference is always New Relic Solutions [Private Offer Only], followed by Datadog, and then Grafana in that order.

There are no specific reasons why customers choose Datadog instead of New Relic Solutions [Private Offer Only]. I need to clarify because this decision is not mine. Our responsibility is recommending these tools to customers and providing what they offer in terms of pluses or minuses. However, the choice of platform is a decision made by the customer. From what I can observe, I can say that integration is one of the points where Datadog has a major strength compared to New Relic Solutions [Private Offer Only]. Their security portfolio is also good, though New Relic Solutions [Private Offer Only] has different plans for security features. If I want to incorporate additional security features, I have to purchase additional plans.

The pricing model which Datadog provides is different, which might be influencing the decision. Datadog operates on a per-head per-product basis, whereas New Relic Solutions [Private Offer Only] is more integration and user-based. New Relic Solutions [Private Offer Only] expects a minimum of 100 GB monthly ingestion and one full platform user. Datadog offers free infrastructure options and often provides extended trials.

I would rate New Relic Solutions [Private Offer Only] as 9.5, with the 0.5 deduction mainly related to how they can optimize the cost of their plans and simplify their plans specifically for smaller projects and startup environments. I would rate Datadog as 4.75 and would rate the solution overall as 9.