Datadog vs Nagios: What are the differences?
What is Datadog? Unify logs, metrics, and traces from across your distributed infrastructure. 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 Nagios? Complete monitoring and alerting for servers, switches, applications, and services. Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License.
Datadog can be classified as a tool in the "Performance Monitoring" category, while Nagios is grouped under "Monitoring Tools".
Some of the features offered by Datadog are:
- 14-day Free Trial for an unlimited number of hosts
- 200+ turn-key integrations for data aggregation
- Clean graphs of StatsD and other integrations
On the other hand, Nagios provides the following key features:
- Monitor your entire IT infrastructure
- Spot problems before they occur
- Know immediately when problems arise
"Monitoring for many apps (databases, web servers, etc)" is the top reason why over 118 developers like Datadog, while over 49 developers mention "It just works" as the leading cause for choosing Nagios.
Nagios is an open source tool with 60 GitHub stars and 36 GitHub forks. Here's a link to Nagios's open source repository on GitHub.
Shopify, Salesforce, and Starbucks are some of the popular companies that use Datadog, whereas Nagios is used by Twitch, Vine Labs, and PedidosYa. Datadog has a broader approval, being mentioned in 532 company stacks & 213 developers stacks; compared to Nagios, which is listed in 176 company stacks and 39 developer stacks.
What is Datadog?
What is Nagios?
Need advice about which tool to choose?Ask the StackShare community!
Sign up to add, upvote and see more prosMake informed product decisions
What are the cons of using Nagios?
Sign up to get full access to all the companiesMake informed product decisions
Sign up to get full access to all the tool integrationsMake informed product decisions
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)
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.
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.
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.
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.
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.
- 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. Whilst you can wind the time bar back to see what happened last week you can't ask questions like "show me the peak period each day for the last x months". The "get data" API is also fairly weak. Neither are concerns at the moment, and I'm sure they're on the to-do list.
I've been a systems administrator most of my career. Everywhere I went, I'd have to rebuild the same monitoring + graphing system. And then make sure that every machine wrote to that system and every application handed up the proper metrics through whatever mechanism seemed good at the time.
Then, as CTO of SimpleReach, single-handedly managing over 200 servers in addition to everything else, I found Datadog. We were already using statsd to instrument our applications, now it was just a matter of getting that data to Datadog. We use Chef, so I installed the Datadog agent on every machine in about 10 minutes and we were up and running.
The best part was that we had a deploy problem the next day with one of our main applications and troubleshooting took minutes instead of hours (and Datadog immediately paid for itself). Now no new features go out without instrumentation and no machine gets created without being monitored.
Datadog just scales with us. Great service and I highly recommend it to anyone not looking to reinvent the wheel with monitoring and instrumentation.
Datadog makes running a service with 800,000 unique users a month possible as a single developer/maintainer. I bought a separate monitor just to keep my datadog dashboards always visible and rely on triggers to keep watch over 20+ servers.
We use datadog to monitor our servers and some application metrics. Easy to get started and scale to many servers. Datadog support engineers are always quick to respond to bugs and other challenges.
We use Nagios to monitor our stack and alert us when problems arise. Nagios allows us to monitor every aspect of each of our servers such as running processes, CPU usage, disk usage, and more. This means that as soon as problems arise, we can detect them and call out an engineer to resolve the issues as soon as possible.
We just started looking into Datadog, but from what we see, it's like New Relic meets Loggly. It's really easy to plugin different services (like the one on this list) and get detailed analysis of what is happening on your servers and services. It makes tracking down sparse and difficult to understand problems possible.
Monitoring day-to-day operations of multiple high-performance computing assets distributed across several networks. Monitoring vendor provided data and setting up alerts when things do not show up on time.
Datadog was used as an agent for monitoring and as for the statsd daemon included. This way we are able to have automated system stats and include whatever other metrics we want to track.
We use Nagios to monitor customer instances of Bridge and proactively alert us about issues like queue sizes, downed services, errors in logs, etc.
Datadog is used because it has a great free tier and it provides us with great insights and integrations into our infrastructure and tools.
Powerful all-in-one monitoring solution as a service. Good integration with AWS. Very affordable price for small-scale startups.
We use nagios based OpsView to monitor our server farm and keep everything running smoothly.