Predictive infrastructure monitoring
MonOps watches your servers from the inside and gives each one a single health score out of 100. When that score starts climbing, you get told — usually minutes before anything breaks, and always before the support tickets arrive.
0m 00s
typical warning before failure
0.0%
of one cpu core used
0s
between health checks
0 min
to install on a server
risk score
8
A single hot CPU is noise. Hot CPU plus memory pressure plus rising network retries is a story.
The prediction ledger checks itself every 60 seconds and reports its own recall and median lead time in your dashboard.
A missed prediction lands in the ledger next to the calls it got right. No silent deletes.
Every cadence below is the real interval from the running system. Nothing here is a roadmap item.
01
A single hot CPU is noise. Hot CPU plus memory pressure plus rising network retries is a story. The risk engine fuses all nine into one 0–100 score, and carries an estimate of how long you have.
02
Every warning it raises gets checked against what actually happened, then reported in your own dashboard — including the calls it got wrong. Private to your team. Most vendors cannot show you this number at all.
03
When a rack goes, everything on it screams at once. The storm engine clusters correlated failures across a 30-minute window by machine, then namespace, then region, and pages you once.
04
Normal, watching, armed, cooldown. It runs on the server itself and raises its own sampling rate the moment behaviour drifts, so the evidence is already dense before anything breaks.
05
A per-endpoint baseline that learns what normal looks like, then opens an incident when response times drift outside it. The regression a green status check will never report.
06
A four-week behavioural baseline per machine, plus surveillance on every scheduled job. Finds the backup that has silently not run since Tuesday.
Risk crosses a floor, or an endpoint check fails twice in a row. Either way the state machine moves with nobody watching.
A fingerprint of the failure signature dedupes the alert, and flap suppression holds the line so a bouncing machine never becomes a page storm.
No acknowledgement inside the timeout and the policy climbs a level by itself, routing to the next rotation on call.
The flight recorder window is flushed, the guilty process tree resolved, and cluster events from the same minutes joined to the incident automatically.
Recovery has to hold across successive sweeps before the incident closes, so a half-healed machine never gets marked green.
A timeline assembled from the evidence, carrying the verdict on the prediction itself: called or missed, and by how much warning.
Inside the machine, the machine itself, your cluster, the network edge and your application — all on the same clock, all attached to the incident before anyone opens it.
Read through native operating-system interfaces rather than by polling your application, so the cost is a rounding error: 0.4% of one CPU core and about 45 MB of memory, on a machine under real load. If one measurement cannot be taken on a given kernel, that one is skipped and the rest carry on. Monitoring degrades, it does not die.
Redacted on the agent
Sensitive fields are stripped before anything reaches the wire, not after it lands in a database.
Survives the partition
A disk-backed outbox replays on reconnect. A severed link costs you latency, never evidence.
Refuses unknown peers
Enrollment tokens gate the fleet. An agent that cannot prove its org gets nothing.
Audit log with no user FK
Deliberate. The record outlives the account that created it.
Every machine keeps a rolling five-minute recording of its own state, second by second. When something breaks, that recording is written to disk with the guilty process already identified — while it is still alive to name.
Most tools sample every 15, 30 or 60 seconds. When a machine dies, the last data point you have is from before anything went wrong, and the interesting part fell between samples. You are left guessing from an average.
MonOps records continuously and keeps the last five minutes in memory on the machine itself. The moment risk crosses a threshold, that window is flushed to disk — 300 frames, no gaps, plus the process tree responsible, resolved by sustained CPU up to 32 levels of parentage.
the rolling recording
1 bar = 1 second
5 minutes agothe crash
Every process running on the machine in the final seconds, ranked by sustained CPU. The top row is the one MonOps blames — and because the list is captured while the processes are still alive, a crash cannot erase who caused it.
Not feature-list padding. Each one is a decision competitors would have to rebuild their agent to match.
One file. No runtime to install, nothing to keep patched.
The agent is a single self-contained binary that reads from native operating-system interfaces instead of polling your application. On a machine under real production load it costs 0.4% of one CPU core and about 45 MB of memory — small enough that nobody will ask you to turn it off during a busy quarter.
Evidence is written locally first, then replayed when the link returns.
Most agents stream to a cloud endpoint and drop whatever they cannot send. Cut the link during an incident and you lose exactly the data you needed. MonOps queues to disk on the machine and replays the backlog in order on reconnect, so a severed network costs you some latency and no evidence at all.
Correlated failures collapse into a single incident before anyone is woken.
When a rack or a shared volume goes, everything above it fails within seconds and most tools page for each one. The storm engine groups correlated failures across a rolling 30-minute window by machine, then namespace, then region. Forty pieces of evidence, one incident, one phone call.
Measured accuracy, including the misses. Ask a competitor for theirs.
Every prediction MonOps makes is recorded and then checked against what actually happened. You see recall — the share of real incidents it called in advance — and median lead time, in your own dashboard. Predictions that led to nothing sit in the same table as the ones that landed. Your numbers stay yours.
Compared against public documentation and pricing as of August 2026. A dash means it exists, but needs another product, another agent, or an enterprise plan to reach.
swipe the table sideways →
| Capability | MonOps | Competitor | Competitor | Competitor | Competitor |
|---|---|---|---|---|---|
| Prediction | |||||
| Pre-incident risk score per host (0–100)Fused kernel and Kubernetes signals with an estimated horizon | yes | no | no | no | no |
| Self-graded prediction accuracyRecall and median lead time, published to you | yes | no | no | no | no |
| On-agent predictor raising its own sample rate | yes | partial | no | no | no |
| Slow-but-OK latency regression incidents | yes | partial | partial | partial | no |
| Evidence | |||||
| Rolling flight recorder on every hostFive-minute per-second ring, flushed on trigger | yes | no | no | no | no |
| Culprit process tree resolved at flush time | yes | no | no | no | no |
| Operating-system level events as incident evidence | yes | partial | partial | partial | no |
| Syscall latency histogram (p50–p99) | yes | partial | no | no | no |
| Kubernetes events joined to the outage window | yes | partial | partial | partial | no |
| Raw unmounted device I/O (Ceph OSDs, bare volumes) | yes | no | no | no | no |
| Incident lifecycle | |||||
| Auto-open with flap suppression and dedupe | yes | partial | partial | partial | partial |
| Auto-escalation L1 → L3 without a second product | yes | no | no | no | partial |
| Storm clustering by node, namespace, region | yes | partial | no | no | no |
| Post-mortem timeline with prediction verdict | yes | no | no | no | no |
| On-call, status pages and audit log included | yes | no | no | partial | partial |
| Maintenance windows that record but never page | yes | yes | yes | partial | yes |
| Footprint | |||||
| Single static binary, no sidecar or runtime | yes | no | no | partial | no |
| Agent CPU on a busy node | 0.4% | 2–6% | 2–8% | 1–4% | n/a |
| Survives packet loss without stalling | yes | partial | partial | partial | partial |
| Survives a network partition without data lossDisk-backed outbox replayed on reconnect | yes | partial | partial | partial | no |
| Signed self-update the fleet verifies itself | yes | partial | partial | partial | n/a |
| Cost and control | |||||
| Log ingest billed by volume | no | yes | yes | yes | yes |
| Per-host pricing multiplier | no | yes | yes | yes | partial |
| Entry price | €10 / mo | $15 / host | $49 / user | $29 / mo | $29 / mo |
| EU-hosted with no third-party processors | yes | partial | partial | partial | partial |
| Custom quotas set by an operator, not sales | yes | no | no | no | no |
Quotas gate growth only. Nothing is deleted when you downgrade, nothing is metered by log volume, and no line item appears that you did not choose.
Starter
€10/ month
External truth for the endpoints your customers actually touch.
Pro
full platform€80/ month
The whole thing: agents, deep evidence, every engine, full lifecycle.
Enterprise
Talk
Fleets, sub-second probes, SSO, and quotas set by an operator.
