Datadog

Open the Service Map to view dependency health.

Select the service with high latency metrics.

Drill down into specific Traces to find the database query or API call causing the delay.

Check associated Logs for error stack traces.

Install Datadog Agent on source and destination hosts.

Create a dashboard comparing CPU/Memory/IOPS across both environments.

Monitor the migration cutover in real-time.

Verify application response times post-migration using APM.

Enable the LLM Observability integration.

Tag LLM calls with 'team' or 'project' metadata.

Monitor the 'Token Usage' dashboard to find high-cost prompts.

Set alerts on budget thresholds for specific AI models.

Configure a Monitor for 5xx error rates.

Integrate Datadog with PagerDuty and Slack.

Set up a 'Workflow Automation' to restart a Kubernetes deployment if specific error patterns emerge.

Post-incident, use Bits AI to generate a summary report.

Use Synthetic Monitoring to simulate user traffic before the event.

Auto-scale Kubernetes clusters based on Datadog metrics.

Monitor Real User Monitoring (RUM) to ensure page load times remain low globally.

Use Watchdog to catch anomalies that auto-scaling might miss.

Define 'Sensitive Data Scanner' rules to mask credit card numbers.

Configure Log Archives to S3 for long-term (7-year) storage.

Use Log Rehydration to bring back old logs for specific audits.

Generate compliance reports using the 'Audit Trail' feature.

Deploy the Datadog Mobile SDK.

Filter RUM data by 'OS Version' and 'Device'.

View Error Tracking to see the exact line of code where the crash occurred.

Correlate the mobile crash with backend API latency in one view.