
Log Aggregation and Intelligent Sampling with CloudWatch and OpenTelemetry
Ingesting every debug log to CloudWatch is how observability becomes a FinOps incident. Tail sampling with ADOT, Logs Insights, and Firehose to S3 for the long tail.

Ingesting every debug log to CloudWatch is how observability becomes a FinOps incident. Tail sampling with ADOT, Logs Insights, and Firehose to S3 for the long tail.

The reflex to bolt Amazon Managed Prometheus + Grafana onto every workload is how observability bills quietly double. CloudWatch Application Signals now gives you an auto-discovered service map, SLOs, and traces with near-zero setup; AMP only earns its keep when you are PromQL-native or drowning in high-cardinality metrics — where ingestion (not retention) is the cost driver. Here is the decision matrix, an ADOT dual-export config, and the three levers that actually cut the AMP bill.

CloudWatch Logs Insights bills $0.005 per GB scanned and high-cardinality custom metrics multiply costs. Cardinality budgets, sampling rules, and FinOps fixes.

The AWS observability team built a chaos engineering game on top of the official OTel Demo. 44 injected failures. Three signals. One LLM judge. Here's everything inside it.

A 500ms latency spike in a distributed system could be a slow RDS query, a Lambda cold start, a downstream API timeout, or a CloudWatch Logs ingestion delay. Finding the cause requires correlated logs, traces, and metrics — not grep.
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