Which layer are you actually missing?
Observability failures are usually gaps rather than quality problems, so name the gap before comparing tools.
You do not know whether servers are healthy: Netdata, with per-second metrics, auto-discovery and ML anomaly detection, free and self-hosted.
You do not know why a request was slow: Elastic APM or Uptrace, both giving distributed tracing across services with metrics and log correlation.
You do not know when something breaks: Better Stack for uptime, incident management and on-call, or Checkly for synthetic checks that exercise real user journeys. You do not know what is failing in the browser or the backend: GlitchTip for error tracking.
Why does per-second collection matter?
Because most incidents are shorter than a collection interval, and a metric averaged over a minute makes them invisible.
Netdata collects at one-second resolution, so a 20-second CPU saturation, a brief disk queue or a momentary connection pool exhaustion appears as a spike rather than being flattened into an unremarkable average. Those short events are frequently the cause of the user-visible latency that nobody could later reproduce.
Its zero-configuration auto-discovery is the second half of the value: Netdata finds services, containers and applications on a node and starts collecting relevant metrics without a configuration file, which means monitoring exists on day one rather than after a project.
Machine-learning anomaly detection trains on each metric's own history so alerts fire on deviation from that system's normal rather than on thresholds someone guessed, and AI-assisted troubleshooting helps narrow which of thousands of metrics moved together. It is free and open source, with Netdata Cloud from €15 per month, and metrics stay on your own nodes.
What does OpenTelemetry-native actually buy you?
The ability to change your mind later without redoing the instrumentation, which is the expensive part of observability.
Uptrace is built on OpenTelemetry rather than supporting it as an import path, so traces, metrics and logs arrive in the open standard and the instrumentation in your code belongs to the standard rather than to the vendor. Moving to a different backend becomes a configuration change instead of a re-instrumentation project across every service.
That matters because vendor-specific agents are how observability lock-in actually happens: the cost of leaving is not the contract, it is the work of re-instrumenting fifty services.
Uptrace covers distributed tracing, metrics monitoring and log management in one platform with alerting and dashboards, across the common languages and frameworks. It is open source and self-hostable, with cloud from €20 per month, based in Berlin. It is younger and smaller than Elastic, with a correspondingly smaller ecosystem.
Why does monitoring belong where the logs are?
Because the moment that matters is when a trace shows a slow span and you need the log lines from inside it.
Elastic APM is built on the Elastic Stack, so traces, metrics, logs and infrastructure data live in the same store and correlate directly — clicking from a slow transaction to the exact log entries for that request, rather than switching tools and reconstructing a timeline by timestamp.
Around that sit distributed tracing across services, APM agents for the common languages, error and exception tracking, real user monitoring measuring what browsers actually experienced, and machine-learning anomaly detection over the metrics.
Elastic N.V. is based in Amsterdam. Deployment can be self-managed on your own infrastructure or Elastic Cloud in EU regions, which is the deciding capability for anyone whose logs contain personal data — and most do. The trade-offs: the stack is heavy to run well, licensing has changed over the years and warrants checking for your use, and pricing above the free tier is custom.
What catches the failure before a user reports it?
Synthetic monitoring, and Checkly's approach is the one that stays accurate as the application changes.
Checkly runs browser checks written as Playwright tests — the same framework teams already use for end-to-end testing — so a check exercises a real user journey rather than pinging a URL, and the same scripts serve both purposes instead of drifting apart. API monitoring covers multi-step checks with assertions across chained requests.
Monitoring as code through the Checkly CLI is the structural part: checks live in your repository, are reviewed like code and deploy through CI/CD, so monitoring changes alongside the feature rather than being updated by whoever remembers. That shift-left integration means a broken journey is caught in the pipeline rather than in production.
Better Stack covers the complementary ground: uptime monitoring verified from multiple locations to avoid false alarms from a single vantage point, log management with structured data, incident management with on-call scheduling, and status pages — replacing both a monitoring tool and PagerDuty. Free tier, from €24 per month, based in Prague. Checkly is Berlin-based with a free tier and paid plans from €30 per month.
