OpenTelemetry-native · built for SRE teams

See everything.
Miss nothing.

M0n1t0r unifies metrics, traces and logs from a single OTLP pipeline — then puts an AI incident engineer on call, so you find root cause in seconds, not war-rooms.

OTLP drop-in Deploy in 5 min Your data, your tenant
app.m0n1t0r.net / overview LIVE
req / s
12,480
▲ 3.2%
error rate
0.18%
nominal
p95 latency
128ms
▼ 5ms
services
42/42
all healthy
throughput · requests/sec
service map · live dependencies
frontend24k · 0.2% · 96ms checkout8.1k · 0.3% · 128ms cart6.4k · 0.1% · 42ms payment3.2k · 0.2% · 110ms products9.7k · 0.0% · 21ms postgres12k · 0.0% · 8ms
HEALTHYAll SLOs within budget · payment error-budget 98.6% remaining
◆ AI Investigation idle
Standing by. I watch every signal and open an investigation the moment an SLO starts to burn.
detect correlate root cause remediate
0B
spans ingested / day
0ms
median query latency
0s
mean time to root cause
0%
open standards · OTLP
Get started
Live in three steps.

No agents to roll out, no proprietary SDK to adopt. If your services already speak OpenTelemetry, you are minutes from your first dashboard.

01
1

Point your collector

Aim any OTLP/HTTP exporter at your workspace endpoint with a bearer token. Keep the instrumentation you already ship.

02
2

Watch it build itself

Service maps, RED metrics, SLOs and error budgets assemble from your spans automatically — nothing to configure first.

03
3

Put AI on call

Turn on the investigator and workflows. When an SLO burns, you get root cause and an action — not just another red chart.

One pipeline
Metrics, traces & logs — correlated, not siloed.

Point your OpenTelemetry collector at M0n1t0r and every signal lands in one tenant-isolated store. RED metrics, service maps and SLOs build themselves from your spans — no agents, no lock-in.

  • Drop-in OTLP — keep the SDK you already ship
  • Span-derived RED metrics & error budgets, automatically
  • ClickHouse-backed — sub-50ms queries at billions of rows
requests by service · last 15m
throughput
12.4k/s
error rate
0.18%
p99
240ms
trace 7f3a…c1 · 214ms
POST /checkout
214ms
cart.validate
24ms
payment.charge
150ms
db.query
86ms
email.enqueue
22ms
Distributed tracing
Follow one request across forty services.

Every span, waterfalled and searchable. Jump from a slow endpoint to the exact database call that stole your latency budget — and the logs emitted inside that same span, one click away.

  • Full-fidelity waterfalls with attribute search
  • Trace ↔ log correlation as a single indexed lookup
  • Live dependency graph derived from real traffic
AI incident engineer
A senior on-call you can tap in — any hour.

When an SLO burns, M0n1t0r's investigator queries your live telemetry itself — metrics, traces, raw logs — reasons over what it finds, and hands you the root cause with the evidence. Then you can chat with it like a teammate.

  • Reads real telemetry with read-only tools — never guesses
  • Root cause with the queries and figures that prove it
  • Persisted per problem — reopen without re-spending tokens
◆ Investigation · payment latency
› queried p95 by service …
› scanned 1,204 error logs …
Root cause. payment-service DB pool exhausted (100/100) after a deploy raised query time 4×. Checkout p95 rose 128 → 512ms.
✓ Fix: raise pool to 200 & add index on txn(created_at).
workflow · on alert fired
RUN QUERY Payment error rate BRANCH err > 5% ? NOTIFY Page on-call LOG Within budget
Workflows
Turn a firing alert into an action.

Draw automations on a canvas: a trigger fans out into steps — query telemetry, branch on a condition, page the right human, hit a webhook. When an alert fires, the workflow runs itself and lands in the run history.

  • Visual builder — no YAML to hand-write
  • Fires on alerts, schedules, or on demand
  • Every run recorded, step-by-step
The whole platform
Everything the on-call loop needs — in one place.

Beyond traces and AI: the reliability tooling your team already depends on, unified on the same OTLP data and per-tenant model. No bolt-on products, no second bill.

SLOs & error budgets

Define a target once; multi-window burn-rate alerts warn you while there's still budget to spend — long before you breach.

Reliability

Anomaly detection

Per-series baselines learn what normal looks like and flag deviations that no single static threshold would ever catch.

Adaptive

Synthetic monitoring

Probe critical endpoints from the outside on a schedule and alert the moment latency climbs or an uptime check fails.

Uptime

Alerting & routing

Route by service, severity or tag to Slack, PagerDuty, email or webhooks — with silences so the right human hears it once.

On-call

Dashboards

Compose views from any metric with tabular, tabbed and time-series panels — then share them across the whole team.

Visibility

Notebooks

Investigate in prose interleaved with live queries and charts — and keep the finished notebook as the postmortem.

Analysis

Granular RBAC

Fine-grained permissions and custom roles go well past admin-or-viewer — grant exactly the access each teammate needs.

Access

Multi-tenant isolation

Every workspace is its own tenant. Telemetry, dashboards and secrets never mix — masked by default, revealed on click.

Isolation

Cost & retention controls

Per-tenant quotas, rate limits and retention policies keep ingest volume — and the bill — predictable as you scale.

Governance

Your stack is already talking.
Start listening in minutes.

Spin up a tenant, point your OTLP endpoint at it, and watch the dashboards fill with your own traffic — free to start.