Alternatives to AppSignal logo

Alternatives to AppSignal

New Relic, Scout, Sentry, Datadog, and ruxit are the most popular alternatives and competitors to AppSignal.
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What is AppSignal and what are its top alternatives?

AppSignal gives you and your team alerts and detailed metrics about your Ruby or Elixir application. Sensible pricing, no aggressive sales & support by developers.
AppSignal is a tool in the Performance Monitoring category of a tech stack.

AppSignal 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.
New Relic logo
New Relic
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AppSignal logo
AppSignal

related New Relic posts

Sebastian Gębski
Sebastian Gębski
CTO at Shedul/Fresha · | 4 upvotes · 431.5K 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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Jerome Dalbert
Jerome Dalbert
Senior Backend Engineer at StackShare · | 4 upvotes · 160.2K 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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Scout logo

Scout

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Application Monitoring that Developers Love
Scout logo
Scout
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AppSignal logo
AppSignal

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Johnny Bell
Johnny Bell
Senior Software Engineer at StackShare · | 17 upvotes · 341.6K views
React
React
JavaScript
JavaScript
LogRocket
LogRocket
Sentry
Sentry
Bugsnag
Bugsnag
Redux
Redux
#OpenSource
#Chrome
#OpenSorce
#ErrorBoundry

For my portfolio websites and my personal OpenSource projects I had started exclusively using React and JavaScript so I needed a way to track any errors that we're happening for my users that I didn't uncover during my personal UAT.

I had narrowed it down to two tools LogRocket and Sentry (I also tried Bugsnag but it did not make the final two). Before I get into this I want to say that both of these tools are amazing and whichever you choose will suit your needs well.

I firstly decided to go with LogRocket the fact that they had a recorded screen capture of what the user was doing when the bug happened was amazing... I could go back and rewatch what the user did to replicate that error, this was fantastic. It was also very easy to setup and get going. They had options for React and Redux.js so you can track all your Redux.js actions. I had a fairly large Redux.js store, this was ended up being a issue, it killed the processing power on my machine, Chrome ended up using 2-4gb of ram, so I quickly disabled the Redux.js option.

After using LogRocket for a month or so I decided to switch to Sentry. I noticed that Sentry was openSorce and everyone was talking about Sentry so I thought I may as well give it a test drive. Setting it up was so easy, I had everything up and running within seconds. It also gives you the option to wrap an errorBoundry in React so get more specific errors. The simplicity of Sentry was a breath of fresh air, it allowed me find the bug that was shown to the user and fix that very simply. The UI for Sentry is beautiful and just really clean to look at, and their emails are also just perfect.

I have decided to stick with Sentry for the long run, I tested pretty much all the JS error loggers and I find Sentry the best.

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related Datadog posts

Robert Zuber
Robert Zuber
CTO at CircleCI · | 8 upvotes · 371.3K 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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ruxit logo

ruxit

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Full stack availability and performance monitoring powered by artificial intelligence
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ruxit
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AppSignal logo
AppSignal
AppDynamics logo

AppDynamics

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Application management for the cloud generation
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AppDynamics
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AppSignal
Azure Application Insights logo

Azure Application Insights

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

Dynatrace

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Monitor, optimize, and scale every app, in any cloud
    Be the first to leave a pro
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    Dynatrace
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    AppSignal logo
    AppSignal