Alternatives to Datadog logo

Alternatives to Datadog

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

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!
Datadog is a tool in the Performance Monitoring category of a tech stack.

Top Alternatives to Datadog

  • New Relic
    New Relic

    The world’s best software and DevOps teams rely on New Relic to move faster, make better decisions and create best-in-class digital experiences. If you run software, you need to run New Relic. More than 50% of the Fortune 100 do too. ...

  • Splunk
    Splunk

    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data. ...

  • Prometheus
    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

    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

    AppDynamics develops application performance management (APM) solutions that deliver problem resolution for highly distributed applications through transaction flow monitoring and deep diagnostics. ...

  • Sentry
    Sentry

    Sentry’s Application Monitoring platform helps developers see performance issues, fix errors faster, and optimize their code health. ...

  • Elasticsearch
    Elasticsearch

    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack). ...

  • LogicMonitor
    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. ...

Datadog alternatives & related posts

New Relic logo

New Relic

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New Relic is the industry’s largest and most comprehensive cloud-based observability platform.
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PROS OF NEW RELIC
  • 415
    Easy setup
  • 344
    Really powerful
  • 244
    Awesome visualization
  • 194
    Ease of use
  • 151
    Great ui
  • 107
    Free tier
  • 81
    Great tool for insights
  • 66
    Heroku Integration
  • 55
    Market leader
  • 49
    Peace of mind
  • 21
    Push notifications
  • 20
    Email notifications
  • 17
    Heroku Add-on
  • 16
    Error Detection and Alerting
  • 12
    Multiple language support
  • 11
    Server Resources Monitoring
  • 11
    SQL Analysis
  • 9
    Transaction Tracing
  • 8
    Apdex Scores
  • 8
    Azure Add-on
  • 7
    Analysis of CPU, Disk, Memory, and Network
  • 6
    Detailed reports
  • 6
    Performance of External Services
  • 6
    Error Analysis
  • 6
    Application Availability Monitoring and Alerting
  • 6
    Application Response Times
  • 5
    JVM Performance Analyzer (Java)
  • 5
    Most Time Consuming Transactions
  • 4
    Easy to use
  • 4
    Top Database Operations
  • 4
    Browser Transaction Tracing
  • 3
    Application Map
  • 3
    Pagoda Box integration
  • 3
    Custom Dashboards
  • 3
    Weekly Performance Email
  • 2
    Easy visibility
  • 2
    App Speed Index
  • 2
    Easy to setup
  • 2
    Background Jobs Transaction Analysis
  • 1
    Incident Detection and Alerting
  • 1
    Worst Transactions by User Dissatisfaction
  • 1
    Metric Data Resolution
  • 1
    Metric Data Retention
  • 1
    Team Collaboration Tools
  • 1
    Rails integration
  • 1
    Super Expensive
  • 1
    Access to Performance Data API
  • 1
    Real User Monitoring Overview
  • 1
    Real User Monitoring Analysis and Breakdown
  • 1
    Free
  • 1
    Best of the best, what more can you ask for
  • 1
    Best monitoring on the market
  • 1
    Time Comparisons
  • 0
    Exceptions
  • 0
    Ddd
CONS OF NEW RELIC
  • 19
    Pricing model doesn't suit microservices
  • 10
    UI isn't great
  • 7
    Visualizations aren't very helpful
  • 7
    Expensive
  • 5
    Hard to understand why things in your app are breaking

related New Relic posts

Farzeem Diamond Jiwani
Software Engineer at IVP · | 5 upvotes · 797.1K views

Hey there! We are looking at Datadog, Dynatrace, AppDynamics, and New Relic as options for our web application monitoring.

Current Environment: .NET Core Web app hosted on Microsoft IIS

Future Environment: Web app will be hosted on Microsoft Azure

Tech Stacks: IIS, RabbitMQ, Redis, Microsoft SQL Server

Requirement: Infra Monitoring, APM, Real - User Monitoring (User activity monitoring i.e., time spent on a page, most active page, etc.), Service Tracing, Root Cause Analysis, and Centralized Log Management.

Please advise on the above. Thanks!

See more
Sebastian Gębski

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
Splunk logo

Splunk

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815
13
Search, monitor, analyze and visualize machine data
500
815
+ 1
13
PROS OF SPLUNK
  • 2
    Alert system based on custom query results
  • 2
    API for searching logs, running reports
  • 2
    Query engine supports joining, aggregation, stats, etc
  • 1
    Ability to style search results into reports
  • 1
    Query any log as key-value pairs
  • 1
    Splunk language supports string, date manip, math, etc
  • 1
    Granular scheduling and time window support
  • 1
    Custom log parsing as well as automatic parsing
  • 1
    Dashboarding on any log contents
  • 1
    Rich GUI for searching live logs
CONS OF SPLUNK
  • 1
    Splunk query language rich so lots to learn

related Splunk posts

Shared insights
on
KibanaKibanaSplunkSplunkGrafanaGrafana

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
Prometheus logo

Prometheus

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An open-source service monitoring system and time series database, developed by SoundCloud
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PROS OF PROMETHEUS
  • 46
    Powerful easy to use monitoring
  • 38
    Flexible query language
  • 32
    Dimensional data model
  • 27
    Alerts
  • 23
    Active and responsive community
  • 22
    Extensive integrations
  • 19
    Easy to setup
  • 12
    Beautiful Model and Query language
  • 7
    Easy to extend
  • 6
    Nice
  • 3
    Written in Go
  • 2
    Good for experimentation
  • 1
    Easy for monitoring
CONS OF PROMETHEUS
  • 12
    Just for metrics
  • 6
    Bad UI
  • 6
    Needs monitoring to access metrics endpoints
  • 4
    Not easy to configure and use
  • 3
    Supports only active agents
  • 2
    Written in Go
  • 2
    TLS is quite difficult to understand
  • 2
    Requires multiple applications and tools
  • 1
    Single point of failure

related Prometheus posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 3.1M views

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
Matt Menzenski
Senior Software Engineering Manager at PayIt · | 14 upvotes · 203K views

Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.

See more
Grafana logo

Grafana

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Open source Graphite & InfluxDB Dashboard and Graph Editor
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PROS OF GRAFANA
  • 84
    Beautiful
  • 67
    Graphs are interactive
  • 57
    Free
  • 56
    Easy
  • 33
    Nicer than the Graphite web interface
  • 24
    Many integrations
  • 17
    Can build dashboards
  • 10
    Easy to specify time window
  • 9
    Dashboards contain number tiles
  • 8
    Can collaborate on dashboards
  • 5
    Open Source
  • 5
    Click and drag to zoom in
  • 5
    Integration with InfluxDB
  • 4
    Authentification and users management
  • 4
    Threshold limits in graphs
  • 3
    It is open to cloud watch and many database
  • 3
    Simple and native support to Prometheus
  • 2
    Great community support
  • 2
    Alerts
  • 2
    You can visualize real time data to put alerts
  • 2
    You can use this for development to check memcache
  • 0
    Grapsh as code
  • 0
    Plugin visualizationa
CONS OF GRAFANA
  • 1
    No interactive query builder

related Grafana posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 3.1M views

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
Matt Menzenski
Senior Software Engineering Manager at PayIt · | 14 upvotes · 203K views

Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.

See more
AppDynamics logo

AppDynamics

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Application management for the cloud generation
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+ 1
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PROS OF APPDYNAMICS
  • 18
    Deep code visibility
  • 11
    Powerful
  • 7
    Great visualization
  • 7
    Real-Time Visibility
  • 6
    Easy Setup
  • 5
    Comprehensive Coverage of Programming Languages
  • 3
    Deep DB Troubleshooting
  • 2
    Excellent Customer Support
CONS OF APPDYNAMICS
  • 5
    Expensive
  • 2
    Poor to non-existent integration with aws services

related AppDynamics posts

Farzeem Diamond Jiwani
Software Engineer at IVP · | 5 upvotes · 797.1K views

Hey there! We are looking at Datadog, Dynatrace, AppDynamics, and New Relic as options for our web application monitoring.

Current Environment: .NET Core Web app hosted on Microsoft IIS

Future Environment: Web app will be hosted on Microsoft Azure

Tech Stacks: IIS, RabbitMQ, Redis, Microsoft SQL Server

Requirement: Infra Monitoring, APM, Real - User Monitoring (User activity monitoring i.e., time spent on a page, most active page, etc.), Service Tracing, Root Cause Analysis, and Centralized Log Management.

Please advise on the above. Thanks!

See more

Hi Folks,

I am trying to evaluate Site24x7 against AppDynamics, Dynatrace, and New Relic. Has anyone used Site24X7? If so, what are your opinions on the tool? I know that the license costs are very low compared to other tools in the market. Other than that, are there any major issues anyone has encountered using the tool itself?

See more
Sentry logo

Sentry

11.6K
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See performance issues, fix errors faster, and optimize code health.
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PROS OF SENTRY
  • 235
    Consolidates similar errors and makes resolution easy
  • 121
    Email Notifications
  • 108
    Open source
  • 84
    Slack integration
  • 71
    Github integration
  • 48
    Easy
  • 44
    User-friendly interface
  • 28
    The most important tool we use in production
  • 18
    Hipchat integration
  • 17
    Heroku Integration
  • 15
    Good documentation
  • 14
    Free tier
  • 9
    Easy setup
  • 9
    Self-hosted
  • 7
    Realiable
  • 6
    Provides context, and great stack trace
  • 4
    Feedback form on error pages
  • 4
    Love it baby
  • 3
    Easy Integration
  • 3
    Gitlab integration
  • 3
    Filter by custom tags
  • 3
    Super user friendly
  • 3
    Captures local variables at each frame in backtraces
  • 1
    Performance measurements
CONS OF SENTRY
  • 12
    Confusing UI
  • 2
    Bundle size

related Sentry posts

Johnny Bell

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.

See more
Elasticsearch logo

Elasticsearch

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Open Source, Distributed, RESTful Search Engine
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PROS OF ELASTICSEARCH
  • 322
    Powerful api
  • 313
    Great search engine
  • 230
    Open source
  • 214
    Restful
  • 199
    Near real-time search
  • 96
    Free
  • 83
    Search everything
  • 54
    Easy to get started
  • 45
    Analytics
  • 26
    Distributed
  • 6
    Fast search
  • 5
    More than a search engine
  • 3
    Easy to scale
  • 3
    Awesome, great tool
  • 3
    Great docs
  • 2
    Potato
  • 2
    Document Store
  • 2
    Great customer support
  • 2
    Intuitive API
  • 2
    Reliable
  • 2
    Nosql DB
  • 2
    Fast
  • 2
    Easy setup
  • 2
    Highly Available
  • 2
    Great piece of software
  • 1
    Ecosystem
  • 1
    Scalability
  • 1
    Not stable
  • 1
    Github
  • 1
    Elaticsearch
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
    Easy to get hot data
  • 1
    Open
  • 0
    Community
CONS OF ELASTICSEARCH
  • 7
    Resource hungry
  • 6
    Diffecult to get started
  • 5
    Expensive
  • 4
    Hard to keep stable at large scale

related Elasticsearch posts

Tim Abbott

We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

I can't recommend it highly enough.

See more
Tymoteusz Paul
Devops guy at X20X Development LTD · | 23 upvotes · 5.1M views

Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

See more
LogicMonitor logo

LogicMonitor

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81
20
SaaS-based, automated IT performance monitoring platform for On-Premise, Hybrid, and Cloud infrastructures.
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81
+ 1
20
PROS OF LOGICMONITOR
  • 5
    Auto discovery
  • 5
    Fast deployment
  • 3
    Agentless
  • 3
    Awesome support
  • 3
    Very extensible
  • 1
    Strong Performance
CONS OF LOGICMONITOR
    Be the first to leave a con

    related LogicMonitor posts