Master Datadog with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Explore the key features that make Datadog powerful for data & analytics workflows.
Datadog monitors infrastructure (servers, containers, Kubernetes, cloud services), applications (via APM and distributed tracing), logs, real user sessions, synthetic tests, network flows, databases, security posture and threats, and AI/LLM workloads. All signals live in one platform and can be correlated together.
Datadog uses modular pricing: each product (Infrastructure, APM, Logs, RUM, Synthetics, Security, LLM Observability, etc.) is billed separately. Common units include per-host per-month, per ingested or indexed GB of logs, per million APM spans, and per session. Volume discounts and annual commitments are available, but many teams find costs grow quickly without active governance.
Yes. Datadog LLM Observability traces prompts, completions, tool calls, token usage, latency, and cost across LLM and agent pipelines, and integrates with providers like OpenAI, Anthropic, AWS Bedrock, and frameworks such as LangChain and LlamaIndex. It also offers evaluations for quality, safety, and hallucinations.
Open-source stacks (Prometheus, Grafana, Loki, OpenTelemetry, Jaeger) can match many of Datadog's features but require self-hosting, scaling, and integration work. Datadog trades higher cost for a fully managed, integrated experience with cross-signal correlation, enterprise security, and turnkey integrations. Datadog also natively ingests OpenTelemetry data.
Datadog has a free tier for basic infrastructure monitoring of up to five hosts, and startups can use the platform productively. However, pricing scales aggressively with hosts, log volume, and custom metrics, so small teams should monitor usage carefully or consider lighter-weight alternatives until scale justifies the cost.
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Tutorial updated March 2026