For DevOps teams

Make reliability a metric leadership actually tracks.

The reliability intelligence layer that sits on top of the stack you already operate. Quantify MTTR, attribute root cause automatically, end the alert fatigue, and give the executive team a single defensible number for engineering health.

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terminal

$ curl -fsSL https://get.pulsewise.io/install.sh | sudo bash -s -- --token pw_live_...[✓] Agent installed (v1.4.2)[✓] Configuration written to /etc/pulsewise/config.yml[✓] Service started: pulsewise-agent[✓] Connected to workspace: ops-team[✓] Metrics streaming — first data in ~10 seconds$ sudo pulsewise statusAgent: v1.4.2 | Status: connected | Workspace: ops-teamUptime: 47d 12h | CPU: 0.3% | Memory: 11MBMonitoring: nginx, postgresql, redis, docker (8 containers)Last metric push: 2s ago

Where ops teams are losing the hour.

More dashboards isn’t the answer. Faster, attributable signal is.

MTTR is dominated by triage, not fix

Most of the first hour of any incident is reconstruction — pulling up dashboards, scrolling logs, paging the right person. The actual remediation is usually fast once the picture is in front of you.

The page channel has lost authority

Too many low-quality alerts and the team starts muting. By the time something genuinely critical fires, the channel has been silenced for so long that nobody is watching it.

Cross-host events look like unrelated incidents

When the disk on host A fills up and the database on host B drops to read-only, you need those events joined. Most stacks treat them as two separate problems and let the operator do the correlation.

The Prometheus stack is its own product

Two hundred metrics, three Grafana dashboards, a hand-tuned Alertmanager config, and a pipeline that needs its own on-call. Sometimes the team just needs the signals and the diagnosis, not a second platform to maintain.

Capabilities that hold up at 03:00.

The features that matter when something is on fire and there is one tab open.

Causal diagnosis on every incident

Each incident arrives with metrics, service state, process-level snapshots, and recent system events joined into a single causal chain — written up in the prose an SRE would actually use.

Ten-second sampling on the Scale tier

Capture the thirty-second saturation events that trigger cascading failures. The CPU spike that lower-frequency polling smooths out is the spike that takes the database offline.

Deploy and config attribution

The diagnosis surfaces recent commits, package upgrades, cron windows, and service restarts that overlap the failure timestamp. Cause and effect are joined automatically.

Ninety-day recurrence analysis

See which hosts dominate the postmortem queue, which services flap on a weekly cycle, and which workloads predictably blow memory budgets every Tuesday at 03:00.

Webhook-first delivery into your existing stack

Signed JSON deliveries to PagerDuty, Opsgenie, your CI pipeline, or whatever already sits between alerts and on-call. At-least-once semantics, exponential backoff.

Workspaces, ownership tags, and RBAC

Per-host ownership routing, per-environment access scopes, read-only roles for tier-1 engineers. The access model matches how the team is actually structured.

Pulsewise against a self-hosted observability stack

	Pulsewise	Prometheus + Grafana
Time to first metric	Under sixty seconds	Hours of exporter and collector setup
Agent footprint	<15MB RSS, <1% CPU	500MB+ across exporters and collectors
Diagnosis on incident	Built in, written in prose	Operator-built dashboards or external ML pipeline
Rule configuration surface	Dashboard UI plus REST API	YAML in version control
Sampling resolution	Ten seconds on Scale	Fifteen to sixty seconds typical
Cost at fifty hosts	$450 CAD flat per month	$500–2,000+ CAD/month, plus operator time

Outbound channels

Slack
Discord
Email
PagerDuty (via signed webhook)
Opsgenie (via signed webhook)
Custom HTTP webhooks
REST API and event stream