Alternatives to AppDynamics logo
New Relic, Splunk, Nagios, Datadog, and ELK are the most popular alternatives and competitors to AppDynamics.
76
75
+ 1
46

What is AppDynamics?

AppDynamics develops application performance management (APM) solutions that deliver problem resolution for highly distributed applications through transaction flow monitoring and deep diagnostics.
AppDynamics is a tool in the Performance Monitoring category of a tech stack.

AppDynamics alternatives & related posts

New Relic logo

New Relic

14.3K
2.8K
1.9K
14.3K
2.8K
+ 1
1.9K
SaaS Application Performance Management for Ruby, PHP, .Net, Java, Python, and Node.js Apps.
New Relic logo
VS
AppDynamics logo
Compare New Relic vs AppDynamics
New Relic logo
New Relic
VS
AppDynamics logo
AppDynamics

related New Relic posts

Sebastian Gębski
Sebastian Gębski
CTO at Shedul/Fresha · | 4 upvotes · 206K views
atFresha EngineeringFresha Engineering
Logentries
Logentries
Sentry
Sentry
AppSignal
AppSignal
New Relic
New Relic
GitHub
GitHub
Git
Git
Jenkins
Jenkins
CircleCI
CircleCI

Regarding Continuous Integration - we've started with something very easy to set up - CircleCI , but with time we're adding more & more complex pipelines - we use Jenkins to configure & run those. It's much more effort, but at some point we had to pay for the flexibility we expected. Our source code version control is Git (which probably doesn't require a rationale these days) and we keep repos in GitHub - since the very beginning & we never considered moving out. Our primary monitoring these days is in New Relic (Ruby & SPA apps) and AppSignal (Elixir apps) - we're considering unifying it in New Relic , but this will require some improvements in Elixir app observability. For error reporting we use Sentry (a very popular choice in this class) & we collect our distributed logs using Logentries (to avoid semi-manual handling here).

See more
Julien DeFrance
Julien DeFrance
Full Stack Engineering Manager at ValiMail · | 3 upvotes · 30.9K views
atStessaStessa
Datadog
Datadog
New Relic
New Relic
#APM

Which #APM / #Infrastructure #Monitoring solution to use?

The 2 major players in that space are New Relic and Datadog Both are very comparable in terms of pricing, capabilities (Datadog recently introduced APM as well).

In our use case, keeping the number of tools minimal was a major selection criteria.

As we were already using #NewRelic, my recommendation was to move to the pro tier so we would benefit from advanced APM features, synthetics, mobile & infrastructure monitoring. And gain 360 degree view of our infrastructure.

Few things I liked about New Relic: - Mobile App and push notificatin - Ease of setting up new alerts - Being notified via email and push notifications without requiring another alerting 3rd party solution

I've certainly seen use cases where NewRelic can also be used as an input data source for Datadog. Therefore depending on your use case, it might also be worth evaluating a joint usage of both solutions.

See more
Splunk logo

Splunk

132
67
0
132
67
+ 1
0
Search, monitor, analyze and visualize machine data
    Be the first to leave a pro
    Splunk logo
    VS
    AppDynamics logo
    Compare Splunk vs AppDynamics
    Splunk logo
    Splunk
    VS
    AppDynamics logo
    AppDynamics

    related Splunk posts

    Grafana
    Grafana
    Splunk
    Splunk
    Kibana
    Kibana

    I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

    See more
    Nagios logo

    Nagios

    548
    383
    92
    548
    383
    + 1
    92
    Complete monitoring and alerting for servers, switches, applications, and services
    Nagios logo
    VS
    AppDynamics logo
    Compare Nagios vs AppDynamics
    Nagios logo
    Nagios
    VS
    AppDynamics logo
    AppDynamics

    related Nagios posts

    Conor Myhrvold
    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 9 upvotes · 361.2K views
    atUber TechnologiesUber Technologies
    Nagios
    Nagios
    Grafana
    Grafana
    Graphite
    Graphite
    Prometheus
    Prometheus

    Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

    By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

    To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

    https://eng.uber.com/m3/

    (GitHub : https://github.com/m3db/m3)

    See more

    related Datadog posts

    Robert Zuber
    Robert Zuber
    CTO at CircleCI · | 8 upvotes · 35.4K views
    atCircleCICircleCI
    Looker
    Looker
    PostgreSQL
    PostgreSQL
    Amplitude
    Amplitude
    Segment
    Segment
    Rollbar
    Rollbar
    Honeycomb
    Honeycomb
    PagerDuty
    PagerDuty
    Datadog
    Datadog

    Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

    We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.

    See more
    StackShare Editors
    StackShare Editors
    Flask
    Flask
    AWS EC2
    AWS EC2
    Celery
    Celery
    Datadog
    Datadog
    PagerDuty
    PagerDuty
    Airflow
    Airflow
    StatsD
    StatsD
    Grafana
    Grafana

    Data science and engineering teams at Lyft maintain several big data pipelines that serve as the foundation for various types of analysis throughout the business.

    Apache Airflow sits at the center of this big data infrastructure, allowing users to “programmatically author, schedule, and monitor data pipelines.” Airflow is an open source tool, and “Lyft is the very first Airflow adopter in production since the project was open sourced around three years ago.”

    There are several key components of the architecture. A web UI allows users to view the status of their queries, along with an audit trail of any modifications the query. A metadata database stores things like job status and task instance status. A multi-process scheduler handles job requests, and triggers the executor to execute those tasks.

    Airflow supports several executors, though Lyft uses CeleryExecutor to scale task execution in production. Airflow is deployed to three Amazon Auto Scaling Groups, with each associated with a celery queue.

    Audit logs supplied to the web UI are powered by the existing Airflow audit logs as well as Flask signal.

    Datadog, Statsd, Grafana, and PagerDuty are all used to monitor the Airflow system.

    See more
    ELK logo

    ELK

    155
    77
    0
    155
    77
    + 1
    0
    The acronym for three open source projects: Elasticsearch, Logstash, and Kibana
      Be the first to leave a pro
      ELK logo
      VS
      AppDynamics logo
      Compare ELK vs AppDynamics
      ELK logo
      ELK
      VS
      AppDynamics logo
      AppDynamics

      related ELK posts

      Wallace Alves
      Wallace Alves
      Cyber Security Analyst · | 1 upvotes · 2.9K views
      nginx
      nginx
      Logstash
      Logstash
      Kibana
      Kibana
      Elasticsearch
      Elasticsearch
      ELK
      ELK
      Portainer
      Portainer
      Docker Compose
      Docker Compose
      Docker
      Docker

      Docker Docker Compose Portainer ELK Elasticsearch Kibana Logstash nginx

      See more

      related Grafana posts

      Conor Myhrvold
      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 9 upvotes · 361.2K views
      atUber TechnologiesUber Technologies
      Nagios
      Nagios
      Grafana
      Grafana
      Graphite
      Graphite
      Prometheus
      Prometheus

      Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

      By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

      To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

      https://eng.uber.com/m3/

      (GitHub : https://github.com/m3db/m3)

      See more
      Gopalakrishna Palem
      Gopalakrishna Palem
      Kibana
      Kibana
      Grafana
      Grafana

      For our Predictive Analytics platform, we have used both Grafana and Kibana

      Kibana has predictions and ML algorithms support, so if you need them, you may be better off with Kibana . The multi-variate analysis features it provide are very unique (not available in Grafana).

      For everything else, definitely Grafana . Especially the number of supported data sources, and plugins clearly makes Grafana a winner (in just visualization and reporting sense). Creating your own plugin is also very easy. The top pros of Grafana (which it does better than Kibana ) are:

      • Creating and organizing visualization panels
      • Templating the panels on dashboards for repetetive tasks
      • Realtime monitoring, filtering of charts based on conditions and variables
      • Export / Import in JSON format (that allows you to version and save your dashboard as part of git)
      See more
      Azure Application Insights logo

      Azure Application Insights

      49
      24
      0
      49
      24
      + 1
      0
      It is an extensible Application Performance Management (APM) service for web developers
        Be the first to leave a pro
        Azure Application Insights logo
        VS
        AppDynamics logo
        Compare Azure Application Insights vs AppDynamics
        Azure Application Insights logo
        Azure Application Insights
        VS
        AppDynamics logo
        AppDynamics
        Jaeger logo

        Jaeger

        70
        49
        0
        70
        49
        + 1
        0
        Distributed tracing system released as open source by Uber
        Jaeger logo
        VS
        AppDynamics logo
        Compare Jaeger vs AppDynamics
        Jaeger logo
        Jaeger
        VS
        AppDynamics logo
        AppDynamics
        ruxit logo

        ruxit

        244
        13
        5
        244
        13
        + 1
        5
        Full stack availability and performance monitoring powered by artificial intelligence
        ruxit logo
        VS
        AppDynamics logo
        Compare ruxit vs AppDynamics
        ruxit logo
        ruxit
        VS
        AppDynamics logo
        AppDynamics
        Librato logo

        Librato

        93
        43
        31
        93
        43
        + 1
        31
        Real-Time Cloud Monitoring
        Librato logo
        VS
        AppDynamics logo
        Compare Librato vs AppDynamics
        Librato logo
        Librato
        VS
        AppDynamics logo
        AppDynamics

        related Skylight posts

        Jerome Dalbert
        Jerome Dalbert
        Senior Backend Engineer at StackShare · | 3 upvotes · 22.7K views
        atStackShareStackShare
        Slack
        Slack
        Pingdom
        Pingdom
        Rails
        Rails
        Skylight
        Skylight
        New Relic
        New Relic
        Heroku
        Heroku

        We currently monitor performance with the following tools:

        1. Heroku Metrics: our main app is Hosted on Heroku, so it is the best place to get quick server metrics like memory usage, load averages, or response times.
        2. Good old New Relic for detailed general metrics, including transaction times.
        3. Skylight for more specific Rails Controller#action transaction times. Navigating those timings is much better than with New Relic, as you get a clear full breakdown of everything that happens for a given request.

        Skylight offers better Rails performance insights, so why use New Relic? Because it does frontend monitoring, while Skylight doesn't. Now that we have a separate frontend app though, our frontend engineers are looking into more specialized frontend monitoring solutions.

        Finally, if one of our apps go down, Pingdom alerts us on Slack and texts some of us.

        See more

        related AppSignal posts

        Sebastian Gębski
        Sebastian Gębski
        CTO at Shedul/Fresha · | 4 upvotes · 206K views
        atFresha EngineeringFresha Engineering
        Logentries
        Logentries
        Sentry
        Sentry
        AppSignal
        AppSignal
        New Relic
        New Relic
        GitHub
        GitHub
        Git
        Git
        Jenkins
        Jenkins
        CircleCI
        CircleCI

        Regarding Continuous Integration - we've started with something very easy to set up - CircleCI , but with time we're adding more & more complex pipelines - we use Jenkins to configure & run those. It's much more effort, but at some point we had to pay for the flexibility we expected. Our source code version control is Git (which probably doesn't require a rationale these days) and we keep repos in GitHub - since the very beginning & we never considered moving out. Our primary monitoring these days is in New Relic (Ruby & SPA apps) and AppSignal (Elixir apps) - we're considering unifying it in New Relic , but this will require some improvements in Elixir app observability. For error reporting we use Sentry (a very popular choice in this class) & we collect our distributed logs using Logentries (to avoid semi-manual handling here).

        See more
        Scout logo

        Scout

        54
        43
        26
        54
        43
        + 1
        26
        Application Monitoring that Developers Love
        Scout logo
        VS
        AppDynamics logo
        Compare Scout vs AppDynamics
        Scout logo
        Scout
        VS
        AppDynamics logo
        AppDynamics
        phpMyAdmin logo

        phpMyAdmin

        50
        18
        0
        50
        18
        + 1
        0
        A free software, for MySQL and MariaDB
          Be the first to leave a pro
          phpMyAdmin logo
          VS
          AppDynamics logo
          Compare phpMyAdmin vs AppDynamics
          phpMyAdmin logo
          phpMyAdmin
          VS
          AppDynamics logo
          AppDynamics
          Kadira logo

          Kadira

          36
          29
          15
          36
          29
          + 1
          15
          Performance Monitoring for Meteor
          Kadira logo
          VS
          AppDynamics logo
          Compare Kadira vs AppDynamics
          Kadira logo
          Kadira
          VS
          AppDynamics logo
          AppDynamics
          Blackfire.io logo

          Blackfire.io

          32
          27
          9
          32
          27
          + 1
          9
          Blackfire Profiler automatically instruments your code to gather data about consumed server resources like memory, CPU time, and...
          Blackfire.io logo
          VS
          AppDynamics logo
          Compare Blackfire.io vs AppDynamics
          Blackfire.io logo
          Blackfire.io
          VS
          AppDynamics logo
          AppDynamics
          Server Density logo

          Server Density

          25
          14
          2
          25
          14
          + 1
          2
          Trusted monitoring built by experts.
          Server Density logo
          VS
          AppDynamics logo
          Compare Server Density vs AppDynamics
          Server Density logo
          Server Density
          VS
          AppDynamics logo
          AppDynamics
          Honeycomb logo

          Honeycomb

          16
          23
          0
          16
          23
          + 1
          0
          Observability for a distributed world--designed for high cardinality data and collaborative problem solving 🐝💖
            Be the first to leave a pro
            Honeycomb logo
            VS
            AppDynamics logo
            Compare Honeycomb vs AppDynamics
            Honeycomb logo
            Honeycomb
            VS
            AppDynamics logo
            AppDynamics

            related Honeycomb posts

            Robert Zuber
            Robert Zuber
            CTO at CircleCI · | 8 upvotes · 35.4K views
            atCircleCICircleCI
            Looker
            Looker
            PostgreSQL
            PostgreSQL
            Amplitude
            Amplitude
            Segment
            Segment
            Rollbar
            Rollbar
            Honeycomb
            Honeycomb
            PagerDuty
            PagerDuty
            Datadog
            Datadog

            Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

            We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.

            See more
            Wavefront logo

            Wavefront

            16
            15
            0
            16
            15
            + 1
            0
            Unified Cloud Monitoring with Real-Time Analytics
              Be the first to leave a pro
              Wavefront logo
              VS
              AppDynamics logo
              Compare Wavefront vs AppDynamics
              Wavefront logo
              Wavefront
              VS
              AppDynamics logo
              AppDynamics