Datadog vs LogicMonitor vs New Relic

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Datadog
Datadog

2.2K
1.5K
+ 1
696
LogicMonitor
LogicMonitor

16
25
+ 1
12
New Relic
New Relic

14.5K
3K
+ 1
1.9K
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- No public GitHub repository available -
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What is Datadog?

Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog!

What is LogicMonitor?

LogicMonitor provides the end-to-end visibility needed to maintain the performance and availability of business applications. It leverages automation and built-in intelligence to monitor today's complex and distributed infrastructures.

What is New Relic?

New Relic is the all-in-one web application performance tool that lets you see performance from the end user experience, through servers, and down to the line of application code.
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Why do developers choose Datadog?
Why do developers choose LogicMonitor?
Why do developers choose New Relic?

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    What companies use Datadog?
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    What tools integrate with Datadog?
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    What are some alternatives to Datadog, LogicMonitor, and New Relic?
    Splunk
    Splunk Inc. provides the leading platform for Operational Intelligence. Customers use Splunk to search, monitor, analyze and visualize machine data.
    Prometheus
    Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.
    Grafana
    Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins.
    AppDynamics
    AppDynamics develops application performance management (APM) solutions that deliver problem resolution for highly distributed applications through transaction flow monitoring and deep diagnostics.
    Sentry
    Sentry is an open-source platform for workflow productivity, aggregating errors from across the stack in real time. 500K developers use Sentry to get the code-level context they need to resolve issues at every stage of the app lifecycle.
    See all alternatives
    Decisions about Datadog, LogicMonitor, and New Relic
    Julien DeFrance
    Julien DeFrance
    Principal Software Engineer at Tophatter · | 3 upvotes · 72.5K 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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    Jerome Dalbert
    Jerome Dalbert
    Senior Backend Engineer at StackShare · | 3 upvotes · 45.7K 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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    Sebastian Gębski
    Sebastian Gębski
    CTO at Shedul/Fresha · | 4 upvotes · 255.9K 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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    Robert Zuber
    Robert Zuber
    CTO at CircleCI · | 8 upvotes · 135.2K 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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    Interest over time
    Reviews of Datadog, LogicMonitor, and New Relic
    Review ofDatadogDatadog

    Background

    We're a real-time financial services messaging company, so being able to monitor our servers and applications in real-time is important to us. We also like a good deal, so $15/server seemed a bargain.

    What were we looking for?

    We wanted to monitor our MS infrastructure (servers, SQL) and apps (C#) to understand performance issues and be able to rectify. We also want to be able to do long-term trending. And we wanted to go from nothing to live in a short time.

    Experience

    Installing the Datadog agent on the servers was a breeze and enabling the integrations for SQL and Windows trivial.

    Using the StatsD based API was also very easy - no worrying about JSON or UDP calls. The ability to add tags to all metrics is also a key benefit. We run multiple (100+) instances of a single application and being able to distinguish events from each one via tagging, or to see aggregates, is extremely useful.

    In all it took 2 days R&D to instrument our key applications sufficiently for production deployment. Deploying the agent to our production servers took 30 mins, giving our Ops team complete visibility for the 1st time.

    What have we learned

    Since we've been live Datadog has given us numerous insights into the way our system behaves, from uneven server loadings and sporadic memory usage to performance tuning a key application that resulted in a 50% increase in throughput. Knowing what's taking the time has been a boon.

    Continuous evolution

    The other nice surprise has been the evolving nature of Datadog. It seems like every couple of weeks there's a new feature on the site.

    Other points

    • I like the transparent pricing. Services that won't show me the price without having to talk to a sales person are really annoying.
    • Support has been good. We've contacted them several times with questions and always had a quick response (time zone considered...we're in London) and a helpful answer.

    So What's bad?

    Probably the weakest aspect at the moment is the long term trending of data.