Stackify vs. New Relic vs. Scout | APM Tool Comparison

Stackify vs. New Relic vs. Scout | APM Tool Comparison

Stackify Retrace primarily supports Java, .NET, PHP, Nodej.js, Ruby, and Python applications. New Relic supports Java, node.js, Python, Go, PHP, .NET, and Ruby. On the other hand, Scout APM supports Ruby, Python, Node.js, PHP, Elixir & Phoenix, in addition to Error Monitoring, Database Monitoring and External Services Monitoring.

Comparison Summary

The core features of Stackify are detailed tracing on SQL queries and application/server log management. It supports Async & .NET Core and detailed code level profiling. In contrast, New Relic provides a setup of real-time instrumentation and analytics.

Scout APM provides detailed insights on performance directly on its dashboard. It identifies slow database queries, N+1 database queries, errors, and memory bloat.

Scout provides better transaction traces by breaking down memory allocation in addition to timing metrics. Scout APM can easily backtrace slow method calls to identify performance bottlenecks and tie them to source code. Database Monitoring provides charts to display data across each query (not just the top five expensive queries).

Another important feature of Scout is, it’s easy to compare performance in a small slice of time to the normal performance. It also provides an improved development Profiler.


Stackify Retrace integrates with Jira, Slack, Axosoft, Azure DevOps, and AWS AutoScale.

New Relic provides integrations of all the services on major cloud providers. Also, It can integrate with Slack, Lighthouse(ticketing system), Databases, and Kubernetes.

Scout integrates with popular developer tools such as GitHub, Rollbar, Bugsnag, Sentry, Slack, PagerDuty, and Honeybadger.

Core APM Product Features

Stackify Core Features

Tracing SQL Queries

Stackify helps trace SQL queries, including ORM Queries, Relations, and its Associations. 

Managing Server Logs

You can configure Logging frameworks such as NLog, log4j, Serilog, log4net and logback, and more. Stackify collects different logs to monitor the system:

Code Level Profiling

It involves monitoring memory usage, CPU for the components or code level. This process, however, adds overhead to the application and may affect the application’s performance. Retrace uses an optimized method to minimize the overhead with efficient performance.  

Some of the metrics for code profiling are,

New Relic Core Features

Easy to set up real-time instrumentation and analytics

New Relic Dashboard provides metrics such as response time, throughput, error rate, and transactions.

The analysis provides a clear understanding of the whole system’s performance. An APM tool needs to offer a profound experience and clear picture of the analysis for performance monitoring.

A drawback with profiling in New Relic is the system load spikes when it starts crushing data in the background.

Mapping Application Performance to the end-user experience

New Relic analyzes end-user experience through real-user monitoring, synthetic monitoring, and mobile app performance analysis.

New Relic provides a Deployment Marker to describe the impact of that code changes on your application. It’s important to understand this for better decision-making for the business requirement.

Error analysis in real-time with on-demand diagnostic tools

An APM tool’s essential function is to help the developer quickly understand the potential source of that issue and debug to find a solution when a problem occurs. 

New Relic agents provide the ability to analyze errors using an error stack trace.

It provides a diagnostic tool such as Thread Profiler, which offers functionality to see a periodic sampling in real application traces. 

Scout APM Core Features

In-depth Performance Analysis 

Scout is an easy-to-use APM tool that requires a much simpler and streamlined setup process than New Relic or Stackify. Scout APM provides an algorithmic agent that digs through your application and provides in-depth analysis and insights. For example, it provides analysis on 

Fast and Efficient Transaction traces

Transaction traces analyze time spent on a single web request or execution of a single background job.

Database Monitoring

Monitoring a database is crucial for application performance. An expensive query may cause other queries to run slower. So, it’s important to monitor and analyze them before significantly impacting your application.

Usability and Dashboards

Stackify UI

Stackify Dashboard has five sections: Dashboard, Monitoring, Performance, Errors, and Logs.


In the dashboard, you will see information about Health, Performance, and Errors. The Health section will show Availability, User Satisfaction, HTTP Error, and User Satisfaction calculated by Apdex. Logs or APM profiler will calculate HTTP Error metrics.



It will report how well your application performs overall as perceived by the users. The chart will describe your different requests such as Fast, Sluggish, Too Slow, and Failed requests. 


In the Errors Section, you can see New, Regressed (or) All Errors. You can also see an overall error rate reported from exception logging or APM profiler.  

New Relic UI


Web transaction time shows the time it took for each transaction. On the top right, is the Apdex score (User satisfaction).

In the middle, you get details about each transaction and its response time. Error rate shows the overall error rate and its average.  

Transaction traces

New Relic provides cross-application tracing if your app depends on another application you’ve monitored with NewRelic. It includes a list of expensive transactions and their details.

Web Endpoints

It shows all the web endpoints and their resource consumption. It also shows the top five web transactions and their throughput.

When you click on the web endpoint, it details its performance breakdown, throughput, and transaction traces. A Histogram displays the details about transaction response time. 

Database Monitoring

Database Monitoring shows the top database operation by its time consumption, query time, and throughput. You can sort the database queriest by most time consumed.

Scout APM UI


Scout dashboard shows the overview of different categories. They are,

Scout digs through the data and generates great insights into application performance. For example, it provides metrics and insights on slow database queries, N+1 database queries, errors, and memory bloat.

Transaction traces