The Open-Source Observability Stack in 2026: Beyond the LGTM Tax
You don't need eleven components and a k8s cluster to see what your app is doing. A tour of the open-source observability landscape as it actually is, weights and warts included.
The LGTM stack is one option, not the definition
Open-source observability has quietly become synonymous with running Loki, Grafana, Tempo, and Mimir — four stateful systems, each with its own query language and failure modes. It's a great stack if you have a platform team. If you don't, you're doing distributed-systems operations as a side quest.
The alternative shape: one backend, OTel-native
The OpenTelemetry data model gave the industry a common wire format. That makes it possible to build a single backend that treats logs, traces, metrics, and exceptions as views over the same events — which is the bet TracePath makes on ClickHouse (or SQLite, at small scale).
TODO-content: TODO-content: survey the landscape honestly with a comparison of operational weight vs coverage: full LGTM, VictoriaMetrics+VL stack, SigNoz, Uptrace, TracePath single-binary, and hosted options. For each: what it does well, what running it actually costs (VM sizing, failure surface), and what happens when it breaks at 3am. Include the standard caveat about benchmark bias (we build TracePath) and link the SQLite benchmark + self-host vs cloud posts. End with a decision tree: k8s+platform-team -> LGTM; one VM -> single-binary; no-ops -> cloud.