Keep your stack. Add what it's missing.
Self-hosting the LGTM stack works fine operationally for teams that have invested in it. Randoli runs on the same backends. The difference isn't operational overhead. It's capability: cross-signal correlation, a built-in monitor catalog, AI-driven incident response, and a unified view instead of three separate data sources.
Where it actually differs
Cross-signal correlation
One unified view correlates logs, traces, metrics, and cost signals automatically. A spike in pod restart rate links to its corresponding trace latency increase without manual dashboard construction.
Three separate Grafana data sources (Loki, Tempo, Prometheus) in a single UI. Cross-signal correlation requires building and maintaining dashboards that join them by timestamp and label convention.
Monitor catalog
Built-in monitor catalog with pre-configured alerting rules for Kubernetes. Active monitoring from day one, with no need to write PromQL or LogQL for every baseline condition.
Alerts require writing PromQL and Loki LogQL expressions from scratch. The stack is maximally flexible, but no default monitor coverage ships. Every rule is bespoke.
AI incident response
Raiya reads the incident before you do, correlating signals across logs, traces, and metrics, running structured root-cause analysis, and drafting runbook steps automatically.
No built-in AI incident response layer. RCA is manual: switch between data sources, correlate signals by timestamp and label, form a hypothesis, validate against each backend separately.
AI agent access (MCP)
Raiya is accessible via MCP, letting any MCP-compatible AI tool query your observability data directly and ask questions about infrastructure state in natural language.
No MCP interface. Observability data is accessible through Grafana's UI and HTTP APIs, but not through the model-context-protocol standard.
Log monitoring
Logs stream through the same real-time pipeline as the rest of your signals, with automatic cross-signal correlation built in.
Loki serves logs on its own timeline. Correlating a log line with a trace or metric spike is a manual, cross-panel exercise.
Why teams add Randoli
Teams running self-hosted LGTM have already made the right architectural decisions: open-source backends, no vendor lock-in, data stays in their environment. That's worth acknowledging. The stack runs fine operationally for teams that have built expertise around it, and the Loki/Tempo/Prometheus community is large and well-supported.
What Randoli adds on top of those backends is a layer the raw stack doesn't provide: automatic cross-signal correlation, a built-in monitor catalog so you're not writing every alert from scratch, Raiya for AI-driven incident response, and MCP access so your AI tooling can query your observability data directly. Randoli runs on the same backends. It's not a replacement; it's what the stack is missing.