Everything below runs from a single lightweight agent — no sidecars, no separate collectors, no extra bill per capability.
223 detectors across web, app, database, and queue layers — each with a specific fix, not a generic score.
Explore →Cascading failures collapse into one incident with a named root cause, not five disconnected pages.
Explore →Docker lifecycle, health, and history — plus node, pod, and deployment monitoring for your clusters. Same agent, same dashboard.
Explore →ETA-to-threshold forecasting and baseline anomaly detection on CPU, memory, and restart rate — a warning before the page fires, not after.
Explore →40 signatures catch exploit scans and probes in real time — RCE, SSRF, Log4Shell, webshells, and more.
Explore →Block the offending IP or push an agent update in one click — every action human-approved, nothing runs unattended.
Explore →Every SSH session recorded and searchable by command — audit trail built in, no separate tool required.
Explore →Ask your infrastructure a question. Answers are grounded in live data via real tool calls, not guesses.
Explore →Most tools show you every alert that fired. Klyroo asks a harder question first: which of these actually caused the others? A hand-curated causal model — database exhaustion cascading to application timeouts cascading to gateway errors — turns a pile of alerts into one clear incident.
No in-cluster operator to install or upgrade — the same lightweight agent that watches your hosts polls the cluster it runs alongside.
Kubernetes gets the same alerting and incident model as every other asset type: not-ready nodes, degraded deployments, pod restarts, and "no data" detection when the agent stops hearing from the cluster at all — each with its own incident trail, not a silent gap.
| Resource | Kind | Status |
|---|---|---|
| node-2 | Node | Ready |
| checkout-api | Deployment | Degraded 2/3 |
| checkout-api-7f9c-x2p | Pod | CrashLoopBackOff |
Every container's status, health check, CPU, and memory — tracked over time, alertable, and correlated with its own log stream. No separate agent, no extra install.
| Container | Status | Health | CPU | Mem |
|---|---|---|---|---|
| api-gateway | running | healthy | 12% | 340 MB |
| worker-queue | running | unhealthy | 96% | 1.8 GB |
| postgres-1 | running | healthy | 34% | 512 MB |
Klyroo fits a trend line to your own recent history, then forecasts when CPU or memory will cross its threshold — a short-range, "about to tip over" warning, not a guess. Every metric also carries its own baseline, so a spike gets judged against what's normal for that specific server or container, not a fixed number.
Klyroo's chat assistant calls real tools against your live data — server status, alert history, log search — instead of guessing from a prompt. Ask in plain language, get an answer grounded in what's actually happening right now.
Built-in terminal recording and command history for every SSH session through Klyroo — the kind of audit trail teams usually pay for a separate tool (Teleport, StrongDM) to get.
40 security signatures watch your web-facing logs for the exploit attempts that precede a real compromise.
Path traversal, SQL injection, SSRF against cloud metadata endpoints, Log4Shell and Spring4Shell probes, webshell uploads, exposed .env/.git/composer.json files — detected from the request pattern itself, with the CVE and the fix.
Route by severity, by asset, or by integration — every notification carries the same root-cause explanation you see in the dashboard.
The enterprise APM giants have a decade of integrations we don't. What we built instead: deeper root-cause explanations per alert, and a few things they don't do at all.
| Capability | Datadog / New Relic | Zabbix / Nagios | Klyroo |
|---|---|---|---|
| Root-cause signature per alert | Generic anomaly score | Threshold only | 223 specific signatures |
| Cause → effect alert correlation | Yes | No | Yes |
| Built-in SSH session recording | No | No | Yes |
| AI chat grounded in live data | Recent, generic | No | Yes |
| Kubernetes monitoring | Yes, in-cluster | Limited | Yes, agent-based |
| Predictive ETA / anomaly alerts | Yes | No | Yes |
| Code-level APM / tracing | Yes | No | Not yet |
| Pricing model | Per-host / per-GB, scales fast | Free / self-managed | Simple, self-hosted-friendly |
A monitoring tool should be honest about its own blind spots. Here's ours, plainly.
Pricing scales with the number of servers you monitor, not the volume of data you send us — the opposite of the per-host-and-per-GB model that makes Datadog bills unpredictable.
Point one agent at a handful of your servers and watch the first correlated incident happen. No slide deck required.
Book a 20-minute demo