Master Datadog AI with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Sign up for Datadog free trial at datadoghq.com and install the Datadog Agent on your servers or containers Configure application performance monitoring by installing language
specific SDKs and enabling trace collection Set up log forwarding from your applications and infrastructure to begin centralized log management Create custom dashboards and enable AI
powered anomaly detection to start monitoring your system's behavioral baselines
💡 Quick Start: Follow these 3 steps in order to get up and running with Datadog AI quickly.
Explore the key features that make Datadog AI powerful for deployment & hosting workflows.
Continuously scans metrics, APM, and logs to surface anomalies, error rate spikes, and dependency regressions without manually configured thresholds. Includes root cause analysis linking anomalies to upstream deploys or infrastructure changes.
Generative AI co-pilot for incident response that answers natural-language questions over telemetry, summarizes incidents, drafts postmortems, and suggests remediation steps based on historical patterns.
Purpose-built tracing for GenAI applications capturing prompts, completions, tokens, latency, and cost across major model providers, plus quality evaluation pipelines for monitoring model output regressions.
ML-driven monitors that project time-series metrics into the future for capacity planning and identify single-host or single-pod outliers within larger fleet baselines.
Automatically groups similar log lines into patterns so engineers can spot the dominant error signatures in millions of lines without writing regex queries manually.
Clusters duplicate exceptions across services using ML, so a deploy regression appears as one issue with all impacted versions and users rather than thousands of individual stack traces.
Watchdog is Datadog's automated anomaly detection engine that continuously analyzes metrics, traces, and logs using machine learning to surface unusual behavior without requiring manually configured thresholds. Regular monitors fire when a metric crosses a static or dynamic threshold you define; Watchdog proactively finds anomalies you haven't anticipated.
Bits AI is Datadog's generative AI assistant that lets users ask natural-language questions about their telemetry, summarize incidents, draft postmortems, and get contextual remediation suggestions during on-call triage.
Yes. Datadog LLM Observability provides trace-level visibility into prompts, completions, latency, token usage, and cost across providers such as OpenAI, Anthropic, and Amazon Bedrock, with built-in quality evaluations and integration into APM traces.
Datadog uses usage-based pricing with separate SKUs per product (Infrastructure, APM, Logs, RUM, etc.). AI capabilities are typically tied to higher-tier plans or available as add-ons. Contact sales for Bits AI pricing.
Datadog AI is strongest when you want one platform spanning infra, APM, logs, RUM, security, and LLMs with ML built in. New Relic offers similar breadth with a consumption-based pricing model. PagerDuty AIOps focuses on alert correlation and incident routing rather than full-stack observability.
Now that you know how to use Datadog AI, it's time to put this knowledge into practice.
Sign up and follow the tutorial steps
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Follow our tutorial and master this powerful deployment & hosting tool in minutes.
Tutorial updated March 2026