Alternatives to AppDynamics logo

Alternatives to AppDynamics

New Relic, Nagios, Splunk, ELK, and Datadog are the most popular alternatives and competitors to AppDynamics.
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What is AppDynamics and what are its top alternatives?

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

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SaaS Application Performance Management for Ruby, PHP, .Net, Java, Python, and Node.js Apps.
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Sebastian Gębski
Sebastian Gębski
CTO at Shedul/Fresha · | 4 upvotes · 302.3K views
atFresha EngineeringFresha Engineering
CircleCI
CircleCI
Jenkins
Jenkins
Git
Git
GitHub
GitHub
New Relic
New Relic
AppSignal
AppSignal
Sentry
Sentry
Logentries
Logentries

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).

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Julien DeFrance
Julien DeFrance
Principal Software Engineer at Tophatter · | 3 upvotes · 103.9K views
atStessaStessa
New Relic
New Relic
Datadog
Datadog
#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.

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Nagios logo

Nagios

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Complete monitoring and alerting for servers, switches, applications, and services
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Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 10 upvotes · 894.3K views
atUber TechnologiesUber Technologies
Prometheus
Prometheus
Graphite
Graphite
Grafana
Grafana
Nagios
Nagios

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)

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Splunk logo

Splunk

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Search, monitor, analyze and visualize machine data
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    Splunk
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    AppDynamics

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    Kibana
    Kibana
    Splunk
    Splunk
    Grafana
    Grafana

    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.

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    ELK logo

    ELK

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    The acronym for three open source projects: Elasticsearch, Logstash, and Kibana
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      ELK logo
      ELK
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      Wallace Alves
      Wallace Alves
      Cyber Security Analyst · | 1 upvotes · 78.9K views
      Docker
      Docker
      Docker Compose
      Docker Compose
      Portainer
      Portainer
      ELK
      ELK
      Elasticsearch
      Elasticsearch
      Kibana
      Kibana
      Logstash
      Logstash
      nginx
      nginx

      Docker Docker Compose Portainer ELK Elasticsearch Kibana Logstash nginx

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      related Datadog posts

      Robert Zuber
      Robert Zuber
      CTO at CircleCI · | 8 upvotes · 221.5K views
      atCircleCICircleCI
      Datadog
      Datadog
      PagerDuty
      PagerDuty
      Honeycomb
      Honeycomb
      Rollbar
      Rollbar
      Segment
      Segment
      Amplitude
      Amplitude
      PostgreSQL
      PostgreSQL
      Looker
      Looker

      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.

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

      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.

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      Conor Myhrvold
      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 10 upvotes · 894.3K views
      atUber TechnologiesUber Technologies
      Prometheus
      Prometheus
      Graphite
      Graphite
      Grafana
      Grafana
      Nagios
      Nagios

      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
      Grafana
      Grafana
      Kibana
      Kibana

      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)
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      Azure Application Insights logo

      Azure Application Insights

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      It is an extensible Application Performance Management (APM) service for web developers
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        Azure Application Insights
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        Jaeger logo

        Jaeger

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        Distributed tracing system released as open source by Uber
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        ruxit logo

        ruxit

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        Full stack availability and performance monitoring powered by artificial intelligence
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        Librato logo

        Librato

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        Real-Time Cloud Monitoring
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        Jerome Dalbert
        Jerome Dalbert
        Senior Backend Engineer at StackShare · | 3 upvotes · 75.8K views
        atStackShareStackShare
        Heroku
        Heroku
        New Relic
        New Relic
        Skylight
        Skylight
        Rails
        Rails
        Pingdom
        Pingdom
        Slack
        Slack

        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.

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        phpMyAdmin logo

        phpMyAdmin

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        A free software, for MySQL and MariaDB
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        Sebastian Gębski
        Sebastian Gębski
        CTO at Shedul/Fresha · | 4 upvotes · 302.3K views
        atFresha EngineeringFresha Engineering
        CircleCI
        CircleCI
        Jenkins
        Jenkins
        Git
        Git
        GitHub
        GitHub
        New Relic
        New Relic
        AppSignal
        AppSignal
        Sentry
        Sentry
        Logentries
        Logentries

        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
        Blackfire.io logo

        Blackfire.io

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        Blackfire.io enables developers to continuously measure & improve their code performance in dev, test, staging and prod.
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        Kadira

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        Performance Monitoring for Meteor
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        Keymetrics

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        Full Stack monitoring and app management for Node.js