Monitoring makes a running Next.js application observable during debugging and after deployment. Logging records state changes, component behavior, and important request or response events; performance tools show where time or memory is being spent. Together, they help distinguish an application error from a slow AI-provider interaction. section 8.1.3 section 8.1.4
Choose a signal that matches the problem:
- Unexpected UI behavior: log state changes and component behavior to locate when a mutation or transition occurs. Keep AI logic separate from UI components so state flow remains easier to trace. section 8.1.3
- Slow responses: inspect browser network activity for API calls and data-fetching bottlenecks. Correlate important network events with structured logs, including request or response data and status. section 8.1.4
- Increasing memory use: use browser memory profiling to look for leaks, especially around streaming, long message histories, timers, and callbacks. Profile server-side memory when the problem is there instead. section 8.1.4
Measure an AI operation
An AI performance profiler can mark the start and end of an operation, then log the resulting duration and streaming metrics. Compare those measurements with network activity when an asynchronous provider request feels slow: retries and errors can add to the wait experienced by the user. section 8.1.4
Production monitoring should match expected traffic. For a small application, establish uptime and response-time monitoring, alerting, and basic error tracking; a hosting provider such as Vercel may supply the initial tools. After deployment, use its monitoring features to follow application performance and problems. When a deployment fails, inspect detailed error logs through the deployment dashboard or the `vercel logs` CLI command. section 9.6.2 section 9.6.3
Treat logs as sensitive operational data. Audit access to chat histories, and log impersonation actions so unauthorized access can be investigated. Avoid logging personally identifiable information or sending sensitive details to an LLM whose policies do not match your privacy requirements. Keep API calls behind a server intermediary when usage oversight matters; provider usage dashboards can help track API and model consumption. section 9.5 section 9.4.3
