Your .NET 7 web API uses Application Insights SDK on each microservice. During traffic spikes, ingestion costs rise rapidly and your team struggles to preserve end-to-end transaction diagnostics across services. You want telemetry volume to automatically scale down during spikes without changing Azure portal rules, while keeping request/exception counts accurate and maintaining distributed tracing correlation. What should you implement?

Adaptive sampling automatically adjusts the sampling rate based on telemetry volume, reducing data ingestion during high-traffic periods while preserving a representative sample. It's implemented in the SDK, so it scales down before data leaves your services, and crucially, it ensures that related telemetry items (like requests and their dependencies) are kept or discarded together, maintaining end-to-end transaction diagnostics. Fixed-rate sampling is less dynamic and might still ingest too much during spikes or too little during lulls. Ingestion sampling happens after data reaches Azure, so it doesn't reduce network egress or initial processing costs. Disabling sampling would lead to uncontrolled costs during spikes.