I like Grafana. I’ve built dashboards in it. Most ops engineers have. It’s free, it’s open source, the plugin ecosystem is massive, and Grafana Cloud has a generous free tier. 25 million users worldwide isn’t an accident.
But every time I get paged at 2am, the workflow is the same: open Grafana, look at the dashboard, see that something is red, try to figure out which panel matters, click through to the logs in Loki, grep around, then SSH into the server anyway because the logs in Loki don’t have enough context. Grafana showed me the problem. It didn’t tell me the cause.
That last mile is what I built SuperTerminal to handle. (We also sell a separate product, the BitSentry Dashboard, for continuous background investigation, but this comparison is about the on-demand workflow.)
What Grafana does
Grafana is the visualization and dashboarding layer of the LGTM stack (Loki for logs, Grafana for visualization, Tempo for traces, Mimir for metrics). You can also connect it to almost anything else: Prometheus, Elasticsearch, CloudWatch, Datadog, Postgres, InfluxDB.
Grafana Cloud adds hosted versions of these backends, alerting, SLO management, incident response (Grafana IRM), on-call management, and a newer AI assistant that can modify dashboards from natural language prompts.
The strength is composability. You pick your data sources, build your dashboards, set your alerts. If you want full control over your observability stack without vendor lock-in, Grafana is the default answer.
The free tier is substantial: 10k metrics series, 50GB of logs, 50GB of traces. For small-to-mid teams, you can run a real observability stack without paying anything.
What Grafana doesn’t do
Grafana doesn’t investigate. It presents data and lets you investigate yourself.
When an alert fires, Grafana shows you a panel that crossed a threshold. Maybe the dashboard has a link to related logs. Maybe you switch to Loki and search. But the reasoning, the “this metric spiked because of this log entry which was caused by this config change,” is still your job.
The AI assistant helps with dashboard management (building panels, writing queries). It doesn’t do incident investigation or root cause analysis from your log data.
What we built instead
SuperTerminal skips the dashboard. You describe the problem (“API latency spiked at midnight”), it SSHes into the server, pulls the relevant logs, checks service health, looks at resource usage, and the AI connects the dots.
The output isn’t a graph you have to interpret. It’s “the latency spike started when the database connection pool hit its limit. Active connections jumped to 50 at 23:58, matching the start of the latency increase. The pool max is configured at 50 in /etc/app/config.yaml.”
You go from “something is red on the dashboard” to “here’s what happened and where the config lives” without being the person who reads every log line.
The comparison
| Grafana (+ LGTM stack) | BitSentry | |
|---|---|---|
| Primary function | Data visualization and dashboarding | Incident investigation and diagnosis |
| You get | Dashboards, alerts, log search | Root cause reports with evidence |
| Investigation approach | You interpret the data | AI interprets the data |
| Log access | Through Loki/Elasticsearch (ingested) | Through SSH (in-place on the server) |
| Alerting | Yes (built-in) | No (uses alerts from existing tools) |
| Open source | Yes (massive ecosystem) | No (SuperTerminal is closed source, free beta) |
| Pricing | Free tier + consumption-based cloud | $7,200/year flat |
| Setup | Deploy LGTM stack + configure dashboards | SSH config + AI key, 5 minutes |
The real workflow difference
With Grafana: alert fires, open dashboard, look at panels, switch to Loki, search logs, maybe find something, SSH into server for more context, form a hypothesis, check another service, repeat until you find the cause.
With SuperTerminal: alert fires, open it up, type “API latency spiked at midnight on the payments service,” wait 2-3 minutes while it SSHes through the relevant servers, read the root cause report.
Both get you to the answer. One requires you to be the reasoning engine. The other automates the reasoning and presents the conclusion.
When Grafana is the right tool
You need observability. Dashboards, metrics, logs, traces, alerting. We don’t do any of this. You need something collecting and visualizing data, and Grafana (or Datadog, or whatever) fills that role.
You want open source control. Grafana’s ecosystem is open source and composable. You can self-host everything, swap out backends, and avoid vendor lock-in. If that matters to your team, Grafana is unmatched.
Your team is comfortable interpreting dashboards. Some engineers prefer staring at graphs and reasoning about patterns. They’re good at it. For them, Grafana is the right tool and SuperTerminal is unnecessary.
When to try SuperTerminal
You have Grafana (or any observability tool) and the bottleneck is the gap between “dashboard shows a problem” and “I know what caused it.” SuperTerminal is the investigation layer that sits on top of whatever you already use for monitoring.
Your logs in Loki don’t have the full picture. Maybe you’re throttling log ingestion for cost reasons, or the relevant logs are on a server that isn’t shipping to Loki. SuperTerminal reads logs in-place over SSH.
You’re tired of being the person who interprets the dashboard at 2am. That’s the honest version of this comparison. Grafana is great. Interpreting it while half-asleep is not.
Try SuperTerminal
Free while in beta. Uses your SSH config and your own AI keys. Set it up in 5 minutes.