Observability

Observability tools collect metrics, logs and traces so teams can see how systems behave and find out why something broke. Monitoring is a large field of its own, so this short list covers only the platforms that platform teams most often pair with the incident and Kubernetes tools elsewhere on ClingCloud.

Datadog, Dynatrace, New Relic and Grafana Cloud all collect telemetry from cloud services, hosts, containers and applications, and all now include AI assistants. These let people ask questions in plain language instead of writing a query, summarize what changed before an incident, and point at the services most likely to be the cause.

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4 AI DevOps tools in observability

Datadog

Monitoring and observability platform for metrics, logs and traces, with Bits AI to help investigate incidents.

  • Free plan or free tier
  • Has an API
  • Works with AWS

New Relic

Observability platform with a free tier and an AI assistant for asking questions about your telemetry.

  • Free plan or free tier
  • Has an API

Grafana Cloud

by Grafana Labs

Hosted Grafana, Prometheus, Loki and Tempo for metrics, logs and traces, with AI features for queries and dashboards.

  • Free plan or free tier
  • Has an API

Dynatrace

Observability platform with automatic discovery and AI-driven root cause analysis for large environments.

  • Works with Kubernetes

The differences show up in pricing models, how data is collected, how long it is kept, and whether you can run parts of the stack yourself. Grafana Cloud is built on open-source projects such as Prometheus and Loki. Before you choose, estimate data volumes honestly, since observability bills grow with what you send, and check what telemetry is shared with the AI features.

For most teams the practical choice comes down to how much telemetry they produce and who will look at it. A small team may get by on one platform with sensible sampling and short retention. A larger organization may combine a commercial platform with open-source tools for high-volume data. Whatever you choose, agree on naming and tagging for services so metrics, logs and traces can be joined up during an incident, and connect the platform to your incident tool so alerts carry useful context.

Questions about observability

Why is observability in a DevOps tools directory?

Incident response, Kubernetes management and cost work all depend on good telemetry. This category lists the main platforms so the rest of the directory makes sense.

Do AI assistants in observability tools see all our data?

They work on the telemetry you send to the platform. Check the vendor's terms for how that data is used by AI features and whether you can turn them off.

How do we keep observability costs under control?

Send what you use, sample high-volume traces, set retention by data type, and review the biggest sources each month. Most platforms show which services send the most data.

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