Causal inference for production incidents

Root cause in seconds, not hours

Devloom pinpoints exactly which deploy, config change, or upstream dependency triggered the alert — and suggests the fix before your on-call opens a terminal.

Incident trace — checkout degradation
api-gateway
2341ms
checkout-service
2289ms
payments-service
2133ms
redis-cache
2041ms
db-primary
412ms
Caused by: deploy abc12f3 payments-service v2.4.1 — 14 min ago. Redis timeout introduced in connection pool config.
45 min average triage time per incident without root cause visibility
3.2 deploys average rollback attempts before finding the bad one
$9,400 median cost per production hour down for a 50-engineer team

How Devloom finds the cause while others find the symptoms.

01 — Ingest

Connect your existing pipeline

Connect your existing Prometheus, Loki, Jaeger, or OTEL pipeline in under 10 minutes. No agent rewrite. No pipeline migration.

02 — Correlate

AI maps the blast radius

The causal inference model traces the degradation signal backward through your dependency graph — identifying which service degraded, which upstream dependency shifted, and which config change or deploy diff correlates with symptom onset. Spans, log lines, and metric anomalies are scored for causal weight, not just temporal proximity.

03 — Resolve

One-click remediation diff

The rollback diff is shown inline — exact config delta between the current deploy and the last-known-good state. Apply from the UI via your connected CI/CD, or copy to your runbook. Devloom never applies fixes automatically. You review the diff, you decide when to apply.

Three things that separate diagnosis from guessing

Root cause

Root cause, not root noise.

We discard 94% of correlated alerts by default. The remaining 6% are causally linked to the incident, ranked by confidence score. This isn't alert grouping — it's causal filtering. No noise, no alert fatigue, no 45-minute MTTD. Just the one event that actually matters.

Signal reduction
847 events ingested
51 correlated events
3 root cause candidates
94% noise removed
Blast radius

Blast radius mapping.

See which downstream services are degraded before they page you — before MTTR starts climbing across three teams at once. Dependency graph with weighted impact arrows shows the full incident scope: which services are critically affected, which are degraded but stable, and which are unaffected.

Dependency graph
Remediation

Remediation diff inline.

We show the exact config or deployment delta that correlates with symptom onset — not a summary, a diff. Apply from the UI or copy to your runbook in one click. This is not automated remediation. Devloom never modifies your production systems without your explicit confirmation.

Diff preview
payments-service/config.yaml
redis:
- pool_size: 5
+ pool_size: 20
timeout: 30s

Plugs into what you already run

Prometheus Grafana Loki Jaeger OpenTelemetry PagerDuty Slack Datadog import

What on-call engineers say

"We had a P1 that would have taken 2 hours of LogQL queries to debug. Devloom traced it to a Redis timeout introduced in the previous deploy in under 3 minutes. MTTD went from 90 to 4 minutes on that incident."

"The remediation diff is the thing. I don't want a summary, I want to see exactly which config line changed between the last-good deploy and the bad one. Devloom shows the diff. I verify it, apply it, done."

Pricing — no per-seat tax
Free 3 services
$49/mo 25 services
$199/mo 100+ services
See full pricing →

Ship on Friday.
Sleep on Saturday.

No per-seat pricing. No agent rewrite. Connect in 10 minutes.

$ devloom connect --provider prometheus --endpoint http://metrics.internal:9090
[OK] 847 metrics streaming
[OK] root-cause engine active
[OK] blast radius model loaded
$ _