Streamwake + Grafana.
Don't replace Grafana — we read the dashboard panels + Datasource scrape job + alert-rule signal and drive remediation, then push the typed fix back into the same Grafana Datasource / Annotation stream.
Don't replace Grafana — we read the dashboard panels + Datasource scrape job + alert-rule signal Grafana already fans-in from Prom / Loki / Tempo / CloudWatch, layer streaming-domain classification + a typed confidence on top, and push the remediation back as a streamwake.<datasource>.remediation.applied metric AND a Grafana streamwake.remediation.applied annotation — same panel, same Datasource, same alert rule. Loop closes on operator-owned infra end to end.
Grafana open visualization plane — dashboard panels + datasources + alert rules + annotations. Streamwake doesn't replace Grafana— we correlate its signals and drive remediation on the anomalies it already surfaces.
What Grafana owns. What Streamwake runs on top.
Honest framing of the surface the vendor owns and the surface Streamwake runs on top of it. Both layers run together in production — same signal bus, different obligations.
Visualization + Datasource connectivity
Recording rules + alert routing
Detect → Classify → Fix + bi-directional sync
Closed-loop on operator-owned Grafana
An incident closed
before your viewers notice.
Four steps from a Grafanaanomaly to a closed incident with a typed postmortem. Read top to bottom — the loop is closed end to end.
- 1
Ingest Grafana telemetry + Datasource signal
Dashboard panel evaluation metrics, recording-rule evaluation counters, alert-rule firing + no-data + resolve events, Datasource health probes / scrape-window stats / scrape-error counters, and annotation activity land on the Streamwake signal bus via a single REST POST. No new Grafana instance; the agent reads the panels + Datasources the operator already runs.
- 2
Correlate + streaming-classify
The agent joins Grafana signals (panel evaluation, recording-rule burn, alert-rule firing) against Datasource metrics (Prom scrape, Loki push, Tempo pull, CloudWatch scrape) on the same bus, then ranks root cause with a typed confidence score (dashboard_panel_anomaly_skew vs datasource_scrape_skew vs recording_rule_slo_burn_skew vs alert_rule_no_data_skew).
- 3
Recommend/run fix + sync back as Datasource metric + Grafana annotation
The agent picks the smallest safe remediation — re-anchor a dashboard panel threshold, refresh a Datasource scrape cadence, re-pin a recording-rule SLO window, fix a mis-configured alert-rule no-data window — verifies recovery, AND emits the same remediation as a streamwake.<datasource>.remediation.applied metric pushed into the Grafana Datasource AND a Grafana streamwake.remediation.applied annotation the panel the agent read can immediately re-render.
- 4
Write the postmortem
A structured writeup — what happened, what was tried, what changed — lands in the team's inbox AND as a typed streamwake.<datasource>.remediation.applied metric + a Grafana streamwake.remediation.applied annotation the moment the incident resolves. Both sides of the loop close inside the operator's own Grafana instance.
Four failure modes Grafana alerts alone miss.
Each one is something the Grafanasignal exposes but the agent loop names and acts on — so a chart becomes a closed incident rather than a triage queue.
Dashboard Panel Anomaly Skew
A dashboard panel's threshold drifted away from the real burn-rate — the alarm trip that used to require sustained breach now fires on a single roll-up that the recording-rule math already screens out. Grafana renders the chart; Streamwake catches the panel-vs-burn-rate skew the moment two consecutive evaluation windows disagree and re-anchors the threshold to the recording-rule's burn-rate curve before the next brown-out trips the wrong panel annotation.
Datasource Scrape Skew
A Grafana Datasource scrape cadence drifts from the upstream Prometheus / Loki / Tempo endpoint — the Datasource evaluation window outlasts the upstream scrape-cadence window, so panels back-fill late and alert-rule firing records miss the actual breach by one window. Streamwake catches the upstream-scrape-vs-Datasource-evaluation skew on the same bus, surfaces the scrape-lag hypothesis, and either retunes the Datasource evaluation window or staggers the scrape cadence so panels land in lockstep again.
Recording Rule Slo Burn Skew
A Grafana recording-rule SLO burn-rate window drifted from its real burn-rate — the recording rule tracks a 30d window while the underlying service burn now runs faster, so panels report "within budget" and the alert-rule stays silent until the alerting window trips the wrong rule. Streamwake catches the recording-rule-vs-burn-rate skew on the same signal and re-anchors the rule to the current burn-rate curve before the next brown-out trips the wrong panel annotation.
Alert Rule No Data Skew
A Grafana alert-rule no-data window masks an outage — the no-data window swallows a real breach because the upstream Datasource went silent (scrape stall, push endpoint backpressure, or network partition between Grafana and Prometheus). Streamwake catches the no-data-vs-firing skew the moment the alert-rule fires AND the no-data window reports "OK" on the same panel, surfaces a typed next-action (shrink the no-data window, fail over the Datasource, escalate), and prevents a silent outage from passing the alert-rule gate.
Run the Free Stream Check.
Next to Grafana.
Paste your Grafana dashboard or Datasource URL — /stream-check runs the same five checks (manifest, segments, bitrate ladder, CDN response, playback errors) in under a minute. No login. Pair it with the Book-a-demo block below for a guided walkthrough of the loop closure inside your own Grafana instance + Datasource topology.
Dashboard panel anomaly skew caught between the two Datasource windows the operator already polls.
grafana.dashboard_panel_anomaly.burn_rate_window_seconds series on the primetime CDN panel split landed two label-set variants from the two Prometheus Datasources Grafana fans-in-to — one with the streaming_service label, one without. The recording-rule aggregation joined at ~52% of the right-sized rate, while the matching grafana.datasource_scrape_errors_total counter on the affected Datasource → panel evaluation handoff climbed to 1,847 vs 142baseline — a regional CDN brownout hidden by Datasource scrape-window drift on the operator's own Grafana instance.
The agent classified it as dashboard_panel_anomaly_skew + datasource_scrape_skew at 87% confidence, re-anchored the panel evaluation label set across both Datasources (forcing the streaming_service label into both scrape jobs), rerouted egress off the brownout PoP, and emitted the typed remediation back as streamwake.prometheus.remediation.applied AND a Grafana streamwake.remediation.applied annotation the same panel could immediately re-render — the brownout cleared within 11 minutes. Both sides closed on the operator's own Grafana — no downstream SaaS in the middle.
Direct mapping to the Streamwake loop on the operator-owned Grafana surface — every dashboard panel the agent reads is the same panel the annotation updates, and every Datasource scrape job the agent reads is the same scrape job the agent re-anchors. The agent that closed the primetime brownout is the same one that catches a recording-rule SLO burn-rate skew the moment two consecutive evaluation windows disagree, drains a Datasource backpressure skew before live panels pile up, and re-pins an alert-rule no-data window to the real burn-rate curve before the next brown-out trips the wrong annotation.
Book a 20-minute walkthrough on your Grafana dashboards + Datasources + alerting.
We're happy to walk through how the dashboard-panel-anomaly-skew, datasource-scrape-skew, recording-rule-SLO-burn-skew, and alert-rule-no-data-skew probes close the loop on top of the Grafana dashboards + Datasources + alert rules the operator already runs. Drop your details below and we'll follow up within 1 business day.