Alternatives to Fabric.js logo

Alternatives to Fabric.js

Raphael, New Relic, Kibana, Grafana, and Sentry are the most popular alternatives and competitors to Fabric.js.
58
170
+ 1
0

What is Fabric.js and what are its top alternatives?

It provides interactive object model on top of canvas element. Fabric also has SVG-to-canvas (and canvas-to-SVG) parser. Using Fabric.js, you can create and populate objects on canvas; objects like simple geometrical shapes
Fabric.js is a tool in the Monitoring Tools category of a tech stack.
Fabric.js is an open source tool with 28.9K GitHub stars and 3.5K GitHub forks. Here’s a link to Fabric.js's open source repository on GitHub

Top Alternatives to Fabric.js

  • Raphael
    Raphael

    It is a cross-browser JavaScript library that draws Vector graphics for web sites. It will use SVG for most browsers, but will use VML for older versions of Internet Explorer. ...

  • 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. ...

  • Kibana
    Kibana

    Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch. ...

  • 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. ...

  • Sentry
    Sentry

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

  • Amazon CloudWatch
    Amazon CloudWatch

    It helps you gain system-wide visibility into resource utilization, application performance, and operational health. It retrieve your monitoring data, view graphs to help take automated action based on the state of your cloud environment. ...

  • Logstash
    Logstash

    Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching). If you store them in Elasticsearch, you can view and analyze them with Kibana. ...

  • Datadog
    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! ...

Fabric.js alternatives & related posts

Raphael logo

Raphael

344
35
0
JavaScript library that draws Vector graphics for web sites
344
35
+ 1
0
PROS OF RAPHAEL
    Be the first to leave a pro
    CONS OF RAPHAEL
      Be the first to leave a con

      related Raphael posts

      New Relic logo

      New Relic

      20.8K
      8.6K
      1.9K
      New Relic is the industry’s largest and most comprehensive cloud-based observability platform.
      20.8K
      8.6K
      + 1
      1.9K
      PROS OF NEW RELIC
      • 415
        Easy setup
      • 344
        Really powerful
      • 245
        Awesome visualization
      • 194
        Ease of use
      • 151
        Great ui
      • 106
        Free tier
      • 80
        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
      • 13
        Multiple language support
      • 11
        SQL Analysis
      • 11
        Server Resources Monitoring
      • 9
        Transaction Tracing
      • 8
        Apdex Scores
      • 8
        Azure Add-on
      • 7
        Analysis of CPU, Disk, Memory, and Network
      • 7
        Detailed reports
      • 6
        Performance of External Services
      • 6
        Error Analysis
      • 6
        Application Availability Monitoring and Alerting
      • 6
        Application Response Times
      • 5
        Most Time Consuming Transactions
      • 5
        JVM Performance Analyzer (Java)
      • 4
        Browser Transaction Tracing
      • 4
        Top Database Operations
      • 4
        Easy to use
      • 3
        Application Map
      • 3
        Weekly Performance Email
      • 3
        Pagoda Box integration
      • 3
        Custom Dashboards
      • 2
        Easy to setup
      • 2
        Background Jobs Transaction Analysis
      • 2
        App Speed Index
      • 1
        Super Expensive
      • 1
        Team Collaboration Tools
      • 1
        Metric Data Retention
      • 1
        Metric Data Resolution
      • 1
        Worst Transactions by User Dissatisfaction
      • 1
        Real User Monitoring Overview
      • 1
        Real User Monitoring Analysis and Breakdown
      • 1
        Time Comparisons
      • 1
        Access to Performance Data API
      • 1
        Incident Detection and Alerting
      • 1
        Best of the best, what more can you ask for
      • 1
        Best monitoring on the market
      • 1
        Rails integration
      • 1
        Free
      • 0
        Proce
      • 0
        Price
      • 0
        Exceptions
      • 0
        Cost
      CONS OF NEW RELIC
      • 20
        Pricing model doesn't suit microservices
      • 10
        UI isn't great
      • 7
        Expensive
      • 7
        Visualizations aren't very helpful
      • 5
        Hard to understand why things in your app are breaking

      related New Relic posts

      Farzeem Diamond Jiwani
      Software Engineer at IVP · | 8 upvotes · 1.4M 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
      Shared insights
      on
      New RelicNew RelicKibanaKibana

      I need to choose a monitoring tool for my project, but currently, my application doesn't have much load or many users. My application is not generating GBs of data. We don't want to send the user information to New Relic because it's a 3rd party tool. And we can deploy Kibana locally on our server. What should I use, Kibana or New Relic?

      See more
      Kibana logo

      Kibana

      20.4K
      16.2K
      262
      Visualize your Elasticsearch data and navigate the Elastic Stack
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      PROS OF KIBANA
      • 88
        Easy to setup
      • 65
        Free
      • 45
        Can search text
      • 21
        Has pie chart
      • 13
        X-axis is not restricted to timestamp
      • 9
        Easy queries and is a good way to view logs
      • 6
        Supports Plugins
      • 4
        Dev Tools
      • 3
        More "user-friendly"
      • 3
        Can build dashboards
      • 2
        Out-of-Box Dashboards/Analytics for Metrics/Heartbeat
      • 2
        Easy to drill-down
      • 1
        Up and running
      CONS OF KIBANA
      • 7
        Unintuituve
      • 4
        Works on top of elastic only
      • 4
        Elasticsearch is huge
      • 3
        Hardweight UI

      related Kibana posts

      Tymoteusz Paul
      Devops guy at X20X Development LTD · | 23 upvotes · 9.6M 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
      Tassanai Singprom

      This is my stack in Application & Data

      JavaScript PHP HTML5 jQuery Redis Amazon EC2 Ubuntu Sass Vue.js Firebase Laravel Lumen Amazon RDS GraphQL MariaDB

      My Utilities Tools

      Google Analytics Postman Elasticsearch

      My Devops Tools

      Git GitHub GitLab npm Visual Studio Code Kibana Sentry BrowserStack

      My Business Tools

      Slack

      See more
      Grafana logo

      Grafana

      17.8K
      14.3K
      415
      Open source Graphite & InfluxDB Dashboard and Graph Editor
      17.8K
      14.3K
      + 1
      415
      PROS OF GRAFANA
      • 89
        Beautiful
      • 68
        Graphs are interactive
      • 57
        Free
      • 56
        Easy
      • 34
        Nicer than the Graphite web interface
      • 26
        Many integrations
      • 18
        Can build dashboards
      • 10
        Easy to specify time window
      • 10
        Can collaborate on dashboards
      • 9
        Dashboards contain number tiles
      • 5
        Open Source
      • 5
        Integration with InfluxDB
      • 5
        Click and drag to zoom in
      • 4
        Authentification and users management
      • 4
        Threshold limits in graphs
      • 3
        Alerts
      • 3
        It is open to cloud watch and many database
      • 3
        Simple and native support to Prometheus
      • 2
        Great community support
      • 2
        You can use this for development to check memcache
      • 2
        You can visualize real time data to put alerts
      • 0
        Grapsh as code
      • 0
        Plugin visualizationa
      CONS OF GRAFANA
      • 1
        No interactive query builder

      related Grafana posts

      Matt Menzenski
      Senior Software Engineering Manager at PayIt · | 16 upvotes · 1M 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
      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 15 upvotes · 5M 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
      Sentry logo

      Sentry

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      • 121
        Email Notifications
      • 108
        Open source
      • 84
        Slack integration
      • 71
        Github integration
      • 49
        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
      • 11
        Self-hosted
      • 9
        Easy setup
      • 7
        Realiable
      • 6
        Provides context, and great stack trace
      • 4
        Feedback form on error pages
      • 4
        Love it baby
      • 3
        Gitlab integration
      • 3
        Filter by custom tags
      • 3
        Super user friendly
      • 3
        Captures local variables at each frame in backtraces
      • 3
        Easy Integration
      • 1
        Performance measurements
      CONS OF SENTRY
      • 12
        Confusing UI
      • 4
        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
      Tassanai Singprom

      This is my stack in Application & Data

      JavaScript PHP HTML5 jQuery Redis Amazon EC2 Ubuntu Sass Vue.js Firebase Laravel Lumen Amazon RDS GraphQL MariaDB

      My Utilities Tools

      Google Analytics Postman Elasticsearch

      My Devops Tools

      Git GitHub GitLab npm Visual Studio Code Kibana Sentry BrowserStack

      My Business Tools

      Slack

      See more
      Amazon CloudWatch logo

      Amazon CloudWatch

      11.5K
      8.1K
      214
      Monitor AWS resources and custom metrics generated by your applications and services
      11.5K
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      PROS OF AMAZON CLOUDWATCH
      • 76
        Monitor aws resources
      • 46
        Zero setup
      • 30
        Detailed Monitoring
      • 23
        Backed by Amazon
      • 19
        Auto Scaling groups
      • 11
        SNS and autoscaling integrations
      • 5
        Burstable instances metrics (t2 cpu credit balance)
      • 3
        HIPAA/PCI/SOC Compliance-friendly
      • 1
        Native tool for AWS so understand AWS out of the box
      CONS OF AMAZON CLOUDWATCH
      • 2
        Poor Search Capabilities

      related Amazon CloudWatch posts

      A huge part of our continuous deployment practices is to have granular alerting and monitoring across the platform. To do this, we run Sentry on-premise, inside our VPCs, for our event alerting, and we run an awesome observability and monitoring system consisting of StatsD, Graphite and Grafana. We have dashboards using this system to monitor our core subsystems so that we can know the health of any given subsystem at any moment. This system ties into our PagerDuty rotation, as well as alerts from some of our Amazon CloudWatch alarms (we’re looking to migrate all of these to our internal monitoring system soon).

      See more
      Bram Verdonck

      After looking for a way to monitor or at least get a better overview of our infrastructure, we found out that Grafana (which I previously only used in ELK stacks) has a plugin available to fully integrate with Amazon CloudWatch . Which makes it way better for our use-case than the offer of the different competitors (most of them are even paid). There is also a CloudFlare plugin available, the platform we use to serve our DNS requests. Although we are a big fan of https://smashing.github.io/ (previously dashing), for now we are starting with Grafana .

      See more
      Logstash logo

      Logstash

      11.3K
      8.7K
      103
      Collect, Parse, & Enrich Data
      11.3K
      8.7K
      + 1
      103
      PROS OF LOGSTASH
      • 69
        Free
      • 18
        Easy but powerful filtering
      • 12
        Scalable
      • 2
        Kibana provides machine learning based analytics to log
      • 1
        Great to meet GDPR goals
      • 1
        Well Documented
      CONS OF LOGSTASH
      • 4
        Memory-intensive
      • 1
        Documentation difficult to use

      related Logstash posts

      Tymoteusz Paul
      Devops guy at X20X Development LTD · | 23 upvotes · 9.6M 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

      Hi everyone. I'm trying to create my personal syslog monitoring.

      1. To get the logs, I have uncertainty to choose the way: 1.1 Use Logstash like a TCP server. 1.2 Implement a Go TCP server.

      2. To store and plot data. 2.1 Use Elasticsearch tools. 2.2 Use InfluxDB and Grafana.

      I would like to know... Which is a cheaper and scalable solution?

      Or even if there is a better way to do it.

      See more
      Datadog logo

      Datadog

      9.3K
      8K
      860
      Unify logs, metrics, and traces from across your distributed infrastructure.
      9.3K
      8K
      + 1
      860
      PROS OF DATADOG
      • 139
        Monitoring for many apps (databases, web servers, etc)
      • 107
        Easy setup
      • 87
        Powerful ui
      • 84
        Powerful integrations
      • 70
        Great value
      • 54
        Great visualization
      • 46
        Events + metrics = clarity
      • 41
        Notifications
      • 41
        Custom metrics
      • 39
        Flexibility
      • 19
        Free & paid plans
      • 16
        Great customer support
      • 15
        Makes my life easier
      • 10
        Adapts automatically as i scale up
      • 9
        Easy setup and plugins
      • 8
        Super easy and powerful
      • 7
        In-context collaboration
      • 7
        AWS support
      • 6
        Rich in features
      • 5
        Docker support
      • 4
        Cute logo
      • 4
        Source control and bug tracking
      • 4
        Monitor almost everything
      • 4
        Cost
      • 4
        Full visibility of applications
      • 4
        Simple, powerful, great for infra
      • 4
        Easy to Analyze
      • 4
        Best than others
      • 4
        Automation tools
      • 3
        Best in the field
      • 3
        Free setup
      • 3
        Good for Startups
      • 3
        Expensive
      • 2
        APM
      CONS OF DATADOG
      • 19
        Expensive
      • 4
        No errors exception tracking
      • 2
        External Network Goes Down You Wont Be Logging
      • 1
        Complicated

      related Datadog posts

      Robert Zuber

      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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      Farzeem Diamond Jiwani
      Software Engineer at IVP · | 8 upvotes · 1.4M 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!

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