Alternatives to PagerDuty logo

Alternatives to PagerDuty

OpsGenie, VictorOps, New Relic, Datadog, and Bigpanda are the most popular alternatives and competitors to PagerDuty.
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What is PagerDuty and what are its top alternatives?

PagerDuty is an alarm aggregation and dispatching service for system administrators and support teams. It collects alerts from your monitoring tools, gives you an overall view of all of your monitoring alarms, and alerts an on duty engineer if there's a problem.
PagerDuty is a tool in the Monitoring Aggregation category of a tech stack.

PagerDuty alternatives & related posts

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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 · 257.1K 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 · 73.8K 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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Robert Zuber
Robert Zuber
CTO at CircleCI · | 8 upvotes · 137.8K 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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Healthchecks.io

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Cron Monitoring. Get Notified When Your Cron Jobs Fail.
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Cronitor

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Monitor cron jobs, daemons and almost anything else
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Squadcast

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The end-to-end SRE tool for efficient incident resolution
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