Datadog fires the chart.
Streamwake closes the incident before a chart turns red.
Streamwake vs. Datadog.
Different layers, not different scores.
Datadog fires the chart; Streamwake closes the streaming incident on the same signal bus.
Datadog passive observability, pager-fired alerts, and human-run playbooks. Streamwake doesn't replace Datadog — we add the detection → classification → remediation → postmortem loop on top of the signals it already produces.
Two different layers, end to end.
Honest framing of what the other tool does well, and where Streamwake compounds on top. Both layers run together in production.
Six rows,
same loop, different layer.
Read top to bottom — the left column is what a traditional monitoring / analytics platform does today; the right column is what happens when Streamwake is sitting on top of it.
Monitor
→ Monitor
Both watch the same signals — QoE, encoder health, CDN egress, DRM handshake latencies. Datadog fans metric + log + trace + event into the team-blessed alert tier; we read the same bus.
Same signals, same event bus — and Datadog fans metric + log + trace in; we consume that same stream and act on it.
Threshold trips + paging a human
→ Class-prioritized detect
Datadog's threshold trip fires an alert and a human triages it. Anomalies arrive as a flat pager list the on-call rotation has to walk through.
Every streaming anomaly is prioritized by class — segment-fetch variance, manifest drift, ABR overshoot, edge brown-out, DRM handshake bursts — so the noise is structured before any human reads it.
Engineer reads the chart
→ Agent correlates
The Datadog dashboard groups events by source and an operator reads between the lines — joining encoder, CDN, and DRM views by hand to find the root cause.
The agent joins the same Datadog metric + log + trace stream across encode, edge, and DRM into one incident — so the chart and the cause tell the same story.
Engineer names the failure
→ Agent classifies
A human investigates the chart, names the failure mode, and decides whether the threshold is even a streaming incident worth a page.
The agent names the failure mode itself, checks whether it is safe to act, and returns a typed next-action rather than a partial chart to triage.
Engineer runs the playbook
→ Agent remediates
A human writes the runbook and runs it through Datadog or a downstream tool — watching the recovery readback by eye.
The agent picks the smallest safe remediation — reroute egress, roll a flag, re-package the title, quarantine a node — and verifies recovery before the incident closes.
Engineer writes postmortem
→ Automatic postmortem
A human hand-writes the post-incident writeup when the dust settles. The Datadog event timeline is the record of record, but the writeup is hand-authored from it.
A structured postmortem — what happened, what was tried, what changed — lands in Slack, Linear, or PagerDuty the moment the incident closes, citing the Datadog event as the record.
Same signals.
Different obligation.
Datadog does monitoring and analytics well. Streamwake doesn't replace that — it adds the detection → classification → remediation → postmortem loop on top of the same signal bus.
The left column ends with a chart and an alert. The right column ends with the incident closed and a writeup in the team's inbox. That's the whole difference — and it doesn't ask you to throw away anything you already run.
Telemetry + analytics
Detect · classify · fix · write it up
Incident closed, not a chart
Questions teams ask
before adding another layer.
Anything specific to your Datadog + Streamwake rollout — write to us.
Bring us your last streaming incident —
we'll show you how Streamwake would have handled it.
Describe a recent outage — encoder, CDN, ISP, or DRM — and we'll come back with a no-cost diagnosis from the team. Operator-confidential, free, no sales pitch.