What is Datadog and what are its top alternatives?
Datadog is a popular monitoring and analytics platform that offers features such as real-time data visualization, alerting, log management, and APM. However, Datadog can be expensive for small to medium-sized businesses and lacks some advanced customization options.
- New Relic: New Relic is a comprehensive observability platform that provides monitoring solutions for applications, infrastructure, and logs. It offers real-time insights, advanced visualizations, and customizable alerting. Pros include a user-friendly interface and detailed performance metrics, but cons include high pricing for larger organizations.
- Splunk: Splunk is a data analytics platform that offers monitoring, investigation, and visualization of machine data in real-time. It provides powerful search capabilities, machine learning features, and customizable dashboards. Pros include scalability and flexibility, while cons include a steep learning curve and high costs.
- Dynatrace: Dynatrace is an application performance management platform that uses AI and automation to provide insights and analytics on application performance. It offers end-to-end monitoring, root cause analysis, and automatic problem detection. Pros include AI-driven insights and automatic instrumentation, but cons include resource-intensive deployment.
- Prometheus: Prometheus is an open-source monitoring and alerting toolkit designed for reliability and scalability. It gathers metrics from targets like containers and databases and stores them for visualization and alerting. Pros include a flexible query language and integrations with various tools, while cons include a steeper learning curve compared to Datadog.
- Grafana: Grafana is an open-source analytics and monitoring platform that allows users to query, visualize, and alert on data. It supports various data sources, including Datadog, Prometheus, and InfluxDB. Pros include a wide range of integrations and a large community, but cons include a lack of built-in data collection agents.
- Zabbix: Zabbix is an open-source monitoring software that offers monitoring of servers, networks, and applications. It provides real-time monitoring, alerting, and reporting capabilities. Pros include a low cost of ownership and extensive monitoring capabilities, but cons include a steep learning curve and limited support for cloud environments.
- Sysdig: Sysdig is a container security and monitoring platform that provides visibility into containers, Kubernetes, and cloud services. It offers deep container inspection, real-time alerts, and troubleshooting tools. Pros include strong container-focused capabilities and cloud-native monitoring, but cons include a complex setup process.
- AppDynamics: AppDynamics is an application performance monitoring solution that provides real-time insights into the performance of business-critical applications. It offers end-user monitoring, database visibility, and business transaction tracking. Pros include deep application insights and business impact analysis, while cons include high costs for full-feature access.
- Icinga: Icinga is an open-source monitoring tool that checks the availability of network resources, notifies users of outages, and generates performance data. It offers customizable alerts, reporting, and REST APIs for integration. Pros include extensibility through plugins and a vibrant community, but cons include a potentially steep learning curve for beginners.
- Observium: Observium is a network monitoring platform that collects and displays data from routers, switches, servers, and other network devices. It offers autodiscovery, alerting, and historical data storage. Pros include ease of setup and customization, but cons include limited support for custom devices and less flexibility compared to Datadog.
Top Alternatives to Datadog
- 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
It provides the leading platform for Operational Intelligence. Customers use it 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’s Application Monitoring platform helps developers see performance issues, fix errors faster, and optimize their code health. ...
- 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 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
- Easy setup415
- Really powerful344
- Awesome visualization245
- Ease of use194
- Great ui151
- Free tier106
- Great tool for insights80
- Heroku Integration66
- Market leader55
- Peace of mind49
- Push notifications21
- Email notifications20
- Heroku Add-on17
- Error Detection and Alerting16
- Multiple language support13
- SQL Analysis11
- Server Resources Monitoring11
- Transaction Tracing9
- Apdex Scores8
- Azure Add-on8
- Analysis of CPU, Disk, Memory, and Network7
- Detailed reports7
- Performance of External Services6
- Error Analysis6
- Application Availability Monitoring and Alerting6
- Application Response Times6
- Most Time Consuming Transactions5
- JVM Performance Analyzer (Java)5
- Browser Transaction Tracing4
- Top Database Operations4
- Easy to use4
- Application Map3
- Weekly Performance Email3
- Pagoda Box integration3
- Custom Dashboards3
- Easy to setup2
- Background Jobs Transaction Analysis2
- App Speed Index2
- Super Expensive1
- Team Collaboration Tools1
- Metric Data Retention1
- Metric Data Resolution1
- Worst Transactions by User Dissatisfaction1
- Real User Monitoring Overview1
- Real User Monitoring Analysis and Breakdown1
- Time Comparisons1
- Access to Performance Data API1
- Incident Detection and Alerting1
- Best of the best, what more can you ask for1
- Best monitoring on the market1
- Rails integration1
- Free1
- Proce0
- Price0
- Exceptions0
- Cost0
- Pricing model doesn't suit microservices20
- UI isn't great10
- Expensive7
- Visualizations aren't very helpful7
- Hard to understand why things in your app are breaking5
related New Relic posts
I've used more and more of New Relic Insights here in my work at Kong. New Relic Insights is a "time series event database as a service" with a super-easy API for inserting custom events, and a flexible query language for building visualization widgets and dashboards.
I'm a big fan of New Relic Insights when I have data I know I need to analyze, but perhaps I'm not exactly sure how I want to analyze it in the future. For example, at Kong we recently wanted to get some understanding of our open source community's activity on our GitHub repos. I was able to quickly configure GitHub to send webhooks to Zapier , which in turn posted the JSON to New Relic Insights.
Insights is schema-less and configuration-less - just start posting JSON key value pairs, then start querying your data.
Within minutes, data was flowing from GitHub to Insights, and I was building widgets on my Insights dashboard to help my colleagues visualize the activity of our open source community.
#GitHubAnalytics #OpenSourceCommunityAnalytics #CommunityAnalytics #RepoAnalytics
Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.
I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.
For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.
Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.
Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.
Future improvements / technology decisions included:
Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic
As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.
One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.
- API for searching logs, running reports3
- Alert system based on custom query results3
- Splunk language supports string, date manip, math, etc2
- Dashboarding on any log contents2
- Custom log parsing as well as automatic parsing2
- Query engine supports joining, aggregation, stats, etc2
- Rich GUI for searching live logs2
- Ability to style search results into reports2
- Granular scheduling and time window support1
- Query any log as key-value pairs1
- Splunk query language rich so lots to learn1
related Splunk posts
I am designing a Django application for my organization which will be used as an internal tool. The infra team said that I will not be having SSH access to the production server and I will have to log all my backend application messages to Splunk. I have no knowledge of Splunk so the following are the approaches I am considering: Approach 1: Create an hourly cron job that uploads the server log file to some Splunk storage for later analysis. - Is this possible? Approach 2: Is it possible just to stream the logs to some splunk endpoint? (If yes, I feel network usage and communication overhead will be a pain-point for my application)
Is there any better or standard approach? Thanks in advance.
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.
Prometheus
- Powerful easy to use monitoring47
- Flexible query language38
- Dimensional data model32
- Alerts27
- Active and responsive community23
- Extensive integrations22
- Easy to setup19
- Beautiful Model and Query language12
- Easy to extend7
- Nice6
- Written in Go3
- Good for experimentation2
- Easy for monitoring1
- Just for metrics12
- Bad UI6
- Needs monitoring to access metrics endpoints6
- Not easy to configure and use4
- Supports only active agents3
- Written in Go2
- TLS is quite difficult to understand2
- Requires multiple applications and tools2
- Single point of failure1
related Prometheus posts
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.
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...
(GitHub : https://github.com/m3db/m3)
- Beautiful89
- Graphs are interactive68
- Free57
- Easy56
- Nicer than the Graphite web interface34
- Many integrations26
- Can build dashboards18
- Easy to specify time window10
- Can collaborate on dashboards10
- Dashboards contain number tiles9
- Open Source5
- Integration with InfluxDB5
- Click and drag to zoom in5
- Authentification and users management4
- Threshold limits in graphs4
- Alerts3
- It is open to cloud watch and many database3
- Simple and native support to Prometheus3
- Great community support2
- You can use this for development to check memcache2
- You can visualize real time data to put alerts2
- Grapsh as code0
- Plugin visualizationa0
- No interactive query builder1
related Grafana posts
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.
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...
(GitHub : https://github.com/m3db/m3)
- Deep code visibility21
- Powerful13
- Real-Time Visibility8
- Great visualization7
- Easy Setup6
- Comprehensive Coverage of Programming Languages6
- Deep DB Troubleshooting4
- Excellent Customer Support3
- Expensive5
- Poor to non-existent integration with aws services2
related AppDynamics posts
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!
We are evaluating an APM tool and would like to select between AppDynamics or Datadog. Our applications are largely hosted on Microsoft Azure but we would keep the option to move to AWS or Google Cloud Platform in the future.
In addition to core Azure services, we will be hosting other components - including MongoDB, Keycloak, PagerDuty, etc. Our applications are largely C# and React-based using frontend for Backend patterns and Azure API gateway. In addition, there are close to 50+ external services integrated using both REST and SOAP.
Sentry
- Consolidates similar errors and makes resolution easy237
- Email Notifications121
- Open source108
- Slack integration84
- Github integration71
- Easy49
- User-friendly interface44
- The most important tool we use in production28
- Hipchat integration18
- Heroku Integration17
- Good documentation15
- Free tier14
- Self-hosted11
- Easy setup9
- Realiable7
- Provides context, and great stack trace6
- Feedback form on error pages4
- Love it baby4
- Gitlab integration3
- Filter by custom tags3
- Super user friendly3
- Captures local variables at each frame in backtraces3
- Easy Integration3
- Performance measurements1
- Confusing UI12
- Bundle size4
related Sentry posts
Sentry has been essential to our development approach. Nobody likes errors or apps that crash. We use Sentry heavily during Node.js and React development. Our developers are able to see error reports, crashes, user's browsers, and more, all in one place. Sentry also seamlessly integrates with Asana, Slack, and GitHub.
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.
- Powerful api328
- Great search engine315
- Open source231
- Restful214
- Near real-time search200
- Free98
- Search everything85
- Easy to get started54
- Analytics45
- Distributed26
- Fast search6
- More than a search engine5
- Great docs4
- Awesome, great tool4
- Highly Available3
- Easy to scale3
- Potato2
- Document Store2
- Great customer support2
- Intuitive API2
- Nosql DB2
- Great piece of software2
- Reliable2
- Fast2
- Easy setup2
- Open1
- Easy to get hot data1
- Github1
- Elaticsearch1
- Actively developing1
- Responsive maintainers on GitHub1
- Ecosystem1
- Not stable1
- Scalability1
- Community0
- Resource hungry7
- Diffecult to get started6
- Expensive5
- Hard to keep stable at large scale4
related Elasticsearch posts
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.
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.
LogicMonitor
- Auto discovery5
- Fast deployment5
- Agentless3
- Awesome support3
- Very extensible3
- Strong Performance1