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BurnRateSurgeon vs Nobl9
Nobl9 reads more data sources than we do and reports how much budget burned. BurnRateSurgeon replays the alert rules that were supposed to catch the burn, and says which one missed.
The honest difference
Nobl9 is an SLO platform: declare an objective once, across Prometheus, Datadog, Dynatrace, CloudWatch and a long list of other sources, and it backfills an error budget from historical data and keeps score going forward. What it does not do is re-run your Alertmanager routing, PagerDuty escalation, or inhibit rules over that history — it tells you the budget burned, not which specific alert rule should have paged and did not. BurnRateSurgeon is narrower by design: one replay engine, evaluating the actual rule group — window boundaries, for-clauses, recording-rule cascades and inhibit chains included — against real time-series, and attributing every miss to a root cause.
Nobl9, capability by capability
Every row below is scored the same way it is on the full five-product table on the landing page — the same marks, the same notes, checked against Nobl9’s own documentation in September 2026.
| Capability | BurnRateSurgeonAlert forensics | Nobl9SLO platform |
|---|---|---|
| Turns an SLO spec into alert rulesMulti-window multi-burn-rate rules and their recording groups, generated for you. | PartialPrescribes a fix as a rule diff. It does not author your SLO spec — bring Sloth or Pyrra for that. | YesSLOs as code through sloctl and OpenSLO, across many data sources at once. |
| Replays your own history against the proposed rulesEvaluates the rules you are about to ship over real past time-series, respecting scrape lag, evaluation intervals, for-clauses and inhibit chains. | YesThe product. A deterministic replay over a TSDB snapshot, down to the scheduler. | PartialReplay backfills an error budget from historical data. Your alert routing is not re-run over it. |
| Says why an alert missed or stormedAttributes a root cause to each failure — window-boundary gap, evaluation lag, inhibit loop, cardinality explosion — and shows the evidence behind it. | YesEvery finding is classified and carries the incidents it was attributed from. | NoReports how much budget burned. Which rule let the incident past stays a manual question. |
| Fails a pull request on an alerting regressionBlocks the merge when a rule change turns an incident the old rules caught into one the new rules would miss. | YesThe PR gate replays the branch and comments the incidents the change would have lost. | Partialsloctl and the Terraform provider apply from CI, and the check they run is schema-level. |
| Cross-vendor corpus of alerting failure signaturesA catalogue of failure modes observed in real production alerting, indexed across vendors and readable by your own agents over MCP. | YesPrometheus, Grafana, Mimir, VictoriaMetrics and Datadog signatures, in one index. | NoReads many data sources, but keeps no catalogue of how alerting fails on them. |
| Runs offline, on data that stays in your networkWhether the analysis can happen without shipping your time-series to a vendor. | YesThe CLI reads a local snapshot. Nothing leaves the cluster unless you upload it. | NoAgents run in your network, and evaluation happens in the hosted platform. |
| Open-source coreHow much of the thing you are evaluating you can read, fork and run for free. | YesCLI and replay engine are Apache-2.0. The corpus and shadow-mode are the paid part. | Partialsloctl and the OpenSLO spec it helped start are open; the platform is not. |
Comparing more than one alternative? See all five side by side, or open an issue if something here reads as out of date or unfair.
When to choose which
Choose Nobl9 when
- You need one SLO platform across many data sources — Prometheus, Datadog, Dynatrace, CloudWatch and more — with a single reporting layer over all of them.
- You want an error budget backfilled from historical data without running replay infrastructure of your own.
- sloctl and an OpenSLO-based workflow already fit how your team ships SLOs as code.
Choose BurnRateSurgeon when
- You need the actual alert-routing path — Alertmanager, inhibit rules, silences — re-run over history, not just the budget backfilled.
- You want a root cause attached to each miss — window-boundary gap, inhibit loop, cardinality explosion — not only a number for how much budget burned.
- You want the replay to run entirely offline, on a local snapshot, before anything reaches a hosted platform.
Nobl9 and BurnRateSurgeon, in practice
- Does Nobl9’s historical backfill do what BurnRateSurgeon’s replay does?
- No. Nobl9 reconstructs an error budget from historical data for the SLO you declared. It does not re-run your alert rules or your Alertmanager routing over that history, so it cannot say a specific rule would have missed a specific incident — that is what the replay engine is built to answer.
- Is Nobl9 open source?
- sloctl and the OpenSLO spec Nobl9 helped start are open. The platform itself is not. BurnRateSurgeon’s CLI and replay engine are Apache-2.0.
- Can BurnRateSurgeon read every data source Nobl9 does?
- Not yet all of them. The replay engine currently reads Prometheus, Grafana Cloud, Mimir, VictoriaMetrics and Datadog composite monitors — fewer sources than Nobl9, by design: cross-vendor breadth is added only once each new adapter produces the same classified report through the same engine (PLAN-FASE-06).
- Does my data leave my network with either product?
- With BurnRateSurgeon’s CLI, no — replay runs locally against a TSDB snapshot. With Nobl9, agents run inside your network but evaluation happens on the hosted platform.
Comparing something else?
Stop debugging alerts after the incident
Validate your burn-rate rules against real history. The open-source CLI is free forever — the SaaS adds shadow-mode, PR gates, and a cross-vendor failure-signature corpus.
Not ready yet? Read one failure a week instead.