I started using Datadog at Peloton in 2019. Prior to that, I had used a hodgepodge of observability tools including AWS CloudWatch, Loggly, and a bunch of internal Amazon tools (e.g. Timber). I continued to use Datadog at Better, Humane, and owned the architecture for deployment at GM. Since seeing Datadog for the first time, I have seen a dozen other tools - Observe, Coralogix, Chronosphere, Mezmo (LogDNA at the time), Grafana Labs, SigNoz, Splunk, Sentry, and a few homegrown ELK stacks. I cannot claim I have seen every tool, or even every popular tool, but I've gotten a very good breadth and a fair bit of depth in my career so far. I also cannot claim to have used every feature of Datadog, but I've definitely used the top 20 at multiple jobs. As another data point, I did pass two of the three available Datadog certifications at DASH in under 1 hour, combined.
Datadog is a cloud-hosted unified observability platform that offers tooling and integrations to collect signals or data from just about any source and index it into either a time series database or a search and analytics storage system. Datadog then provides tooling to visualize, extract automated or manual insights from, and monitor and alert on this data. Like many players in this space, you cannot locally host Datadog. The closest things you could self-host would be SigNoz or Grafana.
The obvious question is "Should I use Datadog?" and I tend to answer this question the same way I would if you said "Should I use AWS?" In both cases, the answer is "You probably should, but it is important to evaluate your specific needs." This is because like any PaaS or IaaS provider, Datadog works well for a large number of customers, but not every customer. I would estimate 85%-90% of companies could cost-effectively and efficiently integrate Datadog and be very pleased long-term. We will explore when you should do extra diligence in this guide.
If you get into the business of 3rd party observability, it won't take long for someone to tell you about how expensive Datadog is relative to other solutions in the space. I want to address this right up front so there's no confusion and proper expectations: Datadog CAN be expensive. This is due to a few different factors which I will talk about, and not even as a result of any nefarious motive on the part of Datadog. Much of this guide will keep costs in mind, and share suggestions for how to manage cost from day one, to avoid unwelcome surprises and leave a salty taste for the entirety of your relationship. With that in mind, let's get going!
Note: I will treat this guide somewhere between "you have never done this" and "you have a decent idea what you're doing" but maybe sprinkle in a few "expert-level" deep thoughts here and there. Note: I will mark each section that contains any AI-generated content, but expect that over time, there will be little to no AI-generated content.