[FunnyLookinHat]

I think you're looking at OTel from a strictly infrastructure perspective - which Cloudwatch does effectively solve without any added effort. But OTel really begins to shine when you instrument your backends. Some languages (Node.js) have a whole slew of auto-instrumentation, giving you rich traces with spans detailing each step of the http request, every SQL query, and even usage of AWS services. Making those traces even more valuable is that they're linked across services.

We've frequently seen a slowdown or error at the top of our stack, and the teams are able to immediately pinpoint the problem as a downstream service. Not only that, they can see the specific issue in the downstream service almost immediately!

Once you get to that level of detail, having your infrastructure metrics pulled into your Otel provider does start to make some sense. If you observe a slowdown in a service, being able to see that the DB CPU is pegged at the same time is meaningful, etc.

[Edit - Typo!]

[makeavish]

Agree with you on this. OTel agents allows exporting all host/k8s metrics correlated with your logs and traces. Though exporting AWS service specific metrics with OTel is not easy. To solve this SigNoz has 1-Click AWS Integrations: https://signoz.io/blog/native-aws-integrations-with-autodisc...

Also SigNoz has native correlation between different signals out of the box.

PS: I am SigNoz Maintainer

[reactordev]

Not confusing anything. Yes you can meter your own applications, generate your own metrics, but most organizations start their observability journey with the hardware and latency metrics.

Otel provides a means to sugar any metric with labels and attributes which is great (until you have high cardinality) but there are still things that are at the infrastructure level that only CloudWatch knows of (on AWS). If you’re running K8s on your own hardware - Otel would be my first choice.

[elza_1111]

FYI for anyone reading, OTel does have great auto-instrumentation for Python, Java and .NET also