Grafana vs Opstral
The Grafana AIOps Alternative for Autonomous Resolution
Written by
Dilip Namdev
Published
Reading time
4 min read
Grafana is the open standard for dashboards and visualization, with a full open observability stack behind it. If your goal is to resolve incidents rather than visualize and alert on them, here is an honest comparison with Opstral.
Why teams evaluate an alternative to Grafana
Grafana is the de-facto open standard for dashboards, and with the LGTM stack, Loki for logs, Grafana for visualization, Tempo for traces and Mimir for metrics, it is a full, open, self-hostable observability toolkit. If your priority is flexible visualization you own and compose yourself, Grafana is very hard to beat, and it is genuinely open. Teams look for an alternative when they want autonomous resolution and governance rather than dashboards and alerts: Grafana shows you what is wrong and can alert, but a human still acts on it, and stitching correlation, remediation and validation together is left to you.
Where Grafana still earns its place
Grafana's strength is visualization as a first-class citizen, and the composability that comes from being genuinely open. Any data source with a plugin (and there are hundreds) becomes a panel; any panel becomes a dashboard; any dashboard becomes shareable across teams. The LGTM stack extends the same openness to the entire pipeline: Loki, Tempo and Mimir are the same tools SaaS observability vendors charge for, running under your control. Grafana OnCall and Grafana IRM (formerly Incident) added incident response on top. For teams that want an open, ownable observability stack with best-in-class visualization, Grafana remains the reference product, and adopting a commercial resolution layer does not change that.
| Dimension | Grafana | Opstral |
|---|---|---|
| Primary focus | Open dashboards, visualization and an open observability stack | Autonomous, governed resolution across enterprise operations |
| Detection vs resolution | Visualizes and alerts on your data; remediation is left to a human or external automation | Closes the loop: ProcBot executes the fix, Sherlock validates it before the incident is closed |
| Governance of actions | Alerting is native; governed remediation is not part of the visualization layer | Every action runs as a reversible, audited Action Ticket, with approval gates where you want them |
| Operational breadth | Visualization and alerting across the metrics, logs and traces you feed it | Ten modular pillars spanning telemetry, service, infrastructure, security, data, cost, process, DevSec Ops, agent ops and the managed estate |
| Openness & hosting | Open-source, self-hostable, or Grafana Cloud | Commercial platform; SaaS, on-premises or fully air-gapped |
| Best for | Teams that want open, composable dashboards they own | Enterprises that want incidents resolved with governance across ten domains |
Signals teams give when they outgrow Grafana
- The dashboards are excellent; nobody watches them. The visualization is best-in-class, but incident response still starts with a pager and ends with an engineer manually correlating what the dashboard would have shown had someone been looking.
- Grafana Alerting works; automation is DIY. Alerts route via webhooks; the remediation is a script somebody wrote, running on infrastructure somebody else maintains, with audit signals in a third place.
- The LGTM stack is a maintenance investment. Loki, Tempo and Mimir at scale each need cluster expertise. The team maintaining observability starts to compete with the team using it.
- Cross-domain incidents. Grafana visualises the data you feed it. Correlation with ITSM, deployment pipelines, security events and cost anomalies requires assembling data sources, panels and alert rules by hand.
- Governance for automated action. Ad-hoc Grafana webhooks running ad-hoc scripts lack the audit trail, approval gates and rollback behaviour that a change board can accept uniformly.
Where Opstral is different
- It resolves, not just visualizesGrafana shows you the problem. Sentinel AI acts on it. When a Grafana Alerting rule fires (SLO burn, saturation, error rate spike), Sentinel investigates root cause across metrics, logs, traces, deployments and configuration in one pass, then ProcBot executes the fix. Sherlock verifies recovery against the same PromQL and LogQL queries that power your Grafana panels, before the incident is closed.
- Governed autonomy, out of the boxActions run as reversible, audited Action Tickets with approval gates and blast-radius scoping. Correlation, remediation and validation come as one governed loop, rather than being assembled from dashboards, alert rules and external scripts. Every automated action leaves an audit trail your reviewers can quote back at you.
- Ten domains, one intelligenceOne layer across telemetry, service, infrastructure, security, data, cost, process and the managed estate. Deployable as SaaS, on-premises, or fully air-gapped with LLM inference on your hardware. Teams that chose Grafana for its self-hosted openness can keep that posture with Opstral.
Migration path: adopting Opstral alongside Grafana
Do not rip out Grafana. Opstral consumes Prometheus-compatible metrics and OpenTelemetry telemetry natively, and connects to Grafana Alerting as an alert receiver. Dashboards, panels, PromQL and LogQL queries continue to run in Grafana. Sentinel reads the alerts, correlates them with signals from every other source (logs, traces, deployments, CMDB, ITSM, security events), and produces investigated incidents that ProcBot resolves under governance.
The usual sequence: two to four weeks of Sentinel in observation mode consuming Grafana alerts, so the team can see what would have been resolved autonomously. Then ProcBot enabled for low-risk automations (pod restart, HPA scale, config revert) behind approval gates. Then autonomy widens as trust accrues. Grafana remains the visualization system of record; Opstral becomes the resolution system of record. The two together give you the open dashboards you already wanted plus the audited autonomous action layer they never had.
Frequently asked questions
Does Opstral replace Grafana?
No, and it does not try to. Many teams keep Grafana for open dashboards and visualization and use Opstral for autonomous resolution on top, connecting through Integration Connectors. Telemetry Ops can also feed and complement an open observability stack.
What does Opstral add over Grafana?
Autonomous execution of the fix through governed, reversible Action Tickets, validation via Sherlock, and a closed correlation-to-resolution loop, rather than dashboards and alerts that still require a human to act.
Is Opstral open-source like Grafana?
No. Opstral is a commercial platform. Its advantage is a governed, autonomous resolution loop across ten domains, deployable on-premises or air-gapped; Grafana's advantage is open, composable visualization you fully own.
How does Opstral handle Grafana Alerting rules?
Grafana Alerting can route to Opstral as a contact point (webhook). Sentinel consumes the alert like any other signal source and treats it as one input into an investigation. Existing rules, silences and notification policies keep working; the difference is what happens after the alert fires.
Do we still need Loki, Tempo or Mimir if we adopt Opstral?
That depends on how deeply the team uses them for ad-hoc querying and long-term retention. If Loki and Tempo are the daily investigation tools, keep them; Opstral consumes their outputs. If they exist mostly to feed Grafana panels and support incident response, Opstral's Telemetry Ops pillar covers that natively and can reduce the operational surface you maintain.