Before OpenTelemetry (OTel), observability required installing proprietary agents for every APM vendor, meaning that switching vendors necessitated re-instrumenting the entire stack. OTel solved this by offering a standard way to generate traces, metrics, and logs once and send them anywhere. However, while the framework itself is free, running it effectively in production incurs significant labor costs.
Infrastructure Sprawl and Operational Burden
A typical self-managed OTel deployment requires operating collector instances per region or cluster. Teams must tune batch settings and memory limiters to prevent bottlenecks under load. This creates a new infrastructure layer that the team owns, patches, and scales—on top of the application systems they intended to monitor.
Storage decisions also become an ongoing responsibility rather than a vendor-managed feature. Teams must select and operate backend components: trace stores for distributed tracing, time-series databases (TSDB) for metrics history, and log indexes. Every version upgrade across this chain requires coordination effort that does not appear in standard headcount plans.
The Correlation Gap
Even with data arriving at three different systems—traces, logs, and metrics—an engineer cannot automatically jump from a slow span to the specific log line or query causing it. Building this correlation layer is continuous engineering work that must evolve as schemas change.
This gap impacts incident response significantly. A trace showing high latency in one service identifies where an issue exists but does not explain why, such as whether the cause was a missing database index, a downstream API timeout, or host resource constraints. Stitching this context across systems that were not designed to talk directly is exactly the work managed platforms typically handle.
When Self-Hosting Still Wins
This does not mean self-managed OTel is inherently bad; it depends on team capacity and requirements. Teams with mature platform engineering practices, specific compliance needs for owning storage layers, or deep Kubernetes native maturity may find a managed backend more constraining than their own stack.
In these scenarios, the community exporter ecosystem offers deeper customization options regarding sampling logic and retention policies compared to what single vendors expose natively. The decision ultimately becomes where engineering hours are best spent: on instrumentation strategy versus maintaining collectors and storage pipelines that act as a second job for observability teams.
What This Means For Practitioners
If you have already instrumented services with OTel, the critical question is whether your team has capacity to manage the ingestion pipeline or if it should be offloaded. A converged platform can accept OpenTelemetry data natively without requiring a separate collector fleet.
This allows teams to retain vendor-neutral instrumentation while handing over operational weight for storage tuning and correlation logic backends, freeing engineers from maintaining pipelines that deliver telemetry but do not directly fix application problems. For those evaluating this trade-off, consider whether the labor cost of self-hosting scales with your number of services or if a managed approach better aligns with current platform strategy.
For teams looking to explore how existing OTel instrumentation integrates into platforms that handle ingestion and root cause analysis without re-instrumentation, hands-on guides can demonstrate setup procedures within minutes of deployment.