It started on Reddit.
I waded into a thread comparing Grafana and Datadog—two observability tools. This post was specifically around dashboarding.
Clearly a lot of engineering teams are considering similar decisions, and have thoughts on dashboards.
Here's the original post, with my two cents below:
The Difference Between Grafana and Datadog
Grafana is to-the-moon customizable. (Just don’t ask me to craft you the PromQL query to get the visualization you want.)
Grafana is a "choose your own adventure" tool. Pull data from anywhere, shape it however you like, and build dashboards that match your exact needs.
Datadog is an out-of-the-box experience. You don’t have to ask for a dashboard—it’s already there.
It’s designed around answering the big operational questions quickly: Am I running out of memory? Is my app crashing? Do I have a bad package?
Dashboards and Monitoring
Grafana thrives on flexibility. It has a huge ecosystem of plug-and-play community dashboards, making it easier to get started.
But at its core, Grafana is first and foremost a visualization tool.
Datadog, on the other hand, is about infrastructure monitoring. It’s built to surface problems before you even go looking for them.
If all you need is unlimited customization, Grafana is your best bet.
If you want problem-solving insights delivered to you, Datadog is likely the better choice.
"If all you need is unlimited customization, Grafana is your best bet. If you want problem-solving insights delivered to you, Datadog is likely the better choice."
How We Handle It
Grafana is open source, which is why we bundle it into our Docker Compose setup for local development.
This means we can validate our dashboards before pushing code to production—an advantage for teams who want consistency between local and live environments.
Datadog, being a SaaS product, doesn’t offer that same flexibility.
You’re always operating within its ecosystem, which makes things easier but removes some control.
At Datable, we made this trade off—both for control of our data, and to stop overspending on observability vendors.
Data Model: Metrics vs. Problems
Grafana is built around metrics—it started as a visualization tool for Prometheus and has since expanded into logs and traces.
Datadog isn’t tied to a specific data type. Instead, it’s built around solving operational problems. Rather than asking, "What are my metrics?", Datadog answers, "Is my system healthy?"
This is a crucial distinction in how they’re designed to be used.
Which One Should You Use?
If deep customization, open-source flexibility, and dashboard fine-tuning matter to you, go with Grafana.
If you need an out-of-the-box monitoring experience that answers operational questions instantly, Datadog is the better choice.
At Datable, we see value in both tools.
We have praises to sing for each.
The real key here is control—control over your data, how you route it, transform it, and visualize it.
Do what works best for your team, and your product.
My Full Response:
Here's a screenshot of my full response on Reddit.
(I write in bullets.)
