A payments processor had APM enabled broadly but traces routinely ended at the boundary between newer and older services. AceMQ assessed instrumentation coverage across the estate to identify exactly where context propagation broke.
Newer services used auto-instrumentation while several older ones had been instrumented by hand years earlier against a much older library version. Where a request crossed between them, trace context headers were dropped, so a single logical transaction appeared as several unrelated traces. Asynchronous hops through messaging brokers lost context entirely. The net effect was that latency in the parts of the system the business cared most about was invisible, while well-instrumented services looked comprehensively covered.
Hybrid estate with Kubernetes services and legacy virtual-machine-hosted applications in Java and Python, messaging between tiers.
The assessment inventories tracer library versions and instrumentation method per service, then traces real production request paths end to end to find where context is actually lost. Because AceMQ does not operate the vendor platform, all findings are about the customer's code, libraries, and propagation configuration — the parts they can change.
End-to-end trace completeness on the core payment path went from fragmentary to near-total, which surfaced a latency contributor in a legacy service that had never appeared in any dashboard. The remediation backlog is ordered by business impact rather than by which team volunteered.
Consulting engagement to govern custom metric cardinality, log ingest, and trace volume so observability spend tracks value instead of accident.
Ongoing support for a Datadog monitor estate producing more alerts than the on-call rotation could meaningfully act on.
Whether you need architecture advisory, 24/7 support, or full managed services, AceMQ has the expertise to help.