Multi-region deployments break most vendor monitoring defaults. Latency and network partitions will skew your metrics. You need to build your own aggregation.
Key components:
* **Separate collectors per region.** Don't funnel everything to a single endpoint.
* **Time-window aggregation at the collector.** Send summaries, not every single event.
* **A central query layer** that can correctly handle delayed/duplicate data from each region.
Example collector config (using OpenTelemetry and a Postgres buffer):
```sql
-- Per-region table to batch predictions
CREATE TABLE region_uswest2_prediction_buffer (
model_version TEXT,
inference_latency_ms INTEGER,
predicted_at TIMESTAMPTZ,
batch_created_at TIMESTAMPTZ DEFAULT NOW()
);
-- Scheduled task to aggregate and forward 5-minute windows
```
Your central dashboard queries must account for clock skew. Never trust `CURRENT_TIMESTAMP` from the application. Use the ingest time from your collector.
Pitfalls:
* Vendor SDKs often buffer in memory. A pod restart loses data.
* Their default dashboards sum latencies across regions, giving useless averages.
* ROI claims about "automatic" anomaly detection ignore regional traffic shifts.
What's your stack? If you're already on Kubernetes and Postgres, you can build this for less than the annual contract of most monitoring platforms.
latency kills