Datadog Natural Language Queries Makes Metrics Accessible to All
Querying metrics used to be hard. Users needed deep familiarity with metric names, tag structures, and query syntax. Experienced teams spent time refining queries through trial and error. Newer users struggled just to get started. That friction slowed troubleshooting and left valuable data untouched.
Datadog has now fixed that. The company launched Datadog Natural Language Queries (NLQ) for metrics. Instead of constructing queries manually, users describe what they want in plain language and immediately generate a working query and visualization. No syntax. No guesswork.
The experience is simple and direct. Users type a request such as “Show average CPU usage per host” or “What’s the max number of failed checkouts in production, broken down by availability zone?” and instantly see a result. The tool shifts focus from building queries to simply asking questions.
That matters for teams beyond engineering. Cross-functional colleagues no longer depend on experts to construct queries or pass along institutional knowledge. Datadog Natural Language Queries opens metrics access to a much wider audience.
Behind the scenes, AI does the heavy lifting. NLQ converts plain-language input into a fully structured Datadog query. It identifies the relevant metric, applies filters like environment or service, selects the appropriate aggregation, and determines how to group the data. It also handles custom metrics that follow your own tagging conventions.
Iteration is equally smooth. After generating an initial result, users can refine queries with phrases like “Group by region,” “Only show production,” or “Last 30 days instead.” NLQ updates the existing query without requiring a rebuild from scratch.
And for advanced users, the generated query stays fully editable in the query editor, helping experienced users fine-tune results while showing newer users how queries are structured.
NLQ works directly from the Metrics Explorer, dashboards, notebook widgets, and the Quick Graphs editor. Teams can reach it wherever they already work.
Datadog has published the full NLQ documentation for teams ready to get started.
