What Devloom actually does under the hood.
Not a dashboard. A causal inference engine that happens to have a dashboard. Logs, metrics, and traces in — ranked root cause candidates and a remediation diff out.
System architecture
Data ingestion → correlation engine → root cause graph → remediation output.
The correlation engine: why symptom clustering is not root cause analysis.
Alert correlation shows you everything that changed at once. Root cause analysis tells you which one caused the others. Those are different problems — and most observability tools only solve the first one. Standard correlation reduces MTTD by surfacing related events; Devloom reduces MTTD by identifying the originating cause.
Devloom's causal inference model starts from the degradation signal and works backward through your dependency graph. Concrete example: a Redis connection pool config change (pool_size: 20 → 5) in deploy abc12f3 → connection wait times exceed 2000ms timeout → payments-service P99 latency spikes → checkout conversion drop. The model traces the causal chain, not the symptom cluster. Not for teams who want more dashboards. For teams who want the answer.
Standard correlation
847 alerts. No clear signal.
Causal filtering
Filtered to 51 correlated events.
Root cause output
3 root cause candidates. Redis timeout ranked #1 at 92%.
Every root cause answer includes a confidence score and an explanation chain.
SREs don't trust opaque black boxes — and they shouldn't. Devloom shows its work: each root cause candidate includes the causal chain that led to that confidence score, expressed in terms your team can verify against the actual trace waterfall and deploy diff. A 92% confidence score with a visible evidence trail is useful. A 95% score with no explanation is not. We show the chain of spans, log lines, and config deltas that support each candidate ranking.
Redis connection pool exhaustion (payments-service)
pool_size reduced from 20 to 5 in deploy abc12f3 (14 min ago) → connection wait time exceeded 2000ms timeout → downstream latency spike across checkout and billing services
Upstream Redis node memory pressure
redis-primary memory utilization at 87% for last 40 min → possible eviction pressure contributing to latency. Causal path weaker — no config change correlates.
Network latency between AZ-1 and AZ-2
P99 inter-AZ latency elevated by 8ms in the same window. Low causal confidence — baseline variation, no deployment correlation found.
From diagnosis to fix in one workflow.
We never apply fixes automatically. You review the diff, you confirm the rollback target, you decide when to apply. The workflow is designed so you can go from PagerDuty alert to applied fix in under 5 minutes — without switching to a second tool or running manual kubectl commands to find the bad deploy.
Confirm root cause — review the confidence score and explanation chain. Dismiss false candidates with one click.
Review rollback diff — Devloom shows the exact config delta between the current deploy and the last-known-good state.
Apply or copy to runbook — one-click apply triggers the rollback in your connected CI/CD system. Or copy the diff to your runbook for manual application.
Verify recovery — Devloom watches the impacted metrics for 5 minutes post-fix and confirms the degradation has resolved.
Rollback will be applied to payments-service via GitHub Actions. Review before confirming.
Data and retention.
Know exactly what we store, for how long, and where it lives before you connect your first service.
| Plan | Retention window | Data residency | Notes |
|---|---|---|---|
| Starter (free) | 7 days | US West (default) | Metrics, logs, traces. No PII stored. |
| Pro | 30 days | US West (default) / US East | All signals + remediation history. |
| Team | 90 days | US West / US East (selectable) | Full audit log. EU residency on roadmap. |
GDPR-ready data handling: we process only signals you explicitly connect. No cross-tenant data access. For details, see our security page.
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