Kllyroo — Infrastructure monitoring that explains itself

The Platform

Nine capabilities. One agent.

Everything below runs from a single lightweight agent — no sidecars, no separate collectors, no extra bill per capability.

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Root-Cause Signatures

223 detectors across web, app, database, and queue layers — each with a specific fix, not a generic score.

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⤳

Causal Correlation

Cascading failures collapse into one incident with a named root cause, not five disconnected pages.

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Container & Kubernetes Intelligence

Docker lifecycle, health, and history — plus node, pod, and deployment monitoring for your clusters. Same agent, same dashboard.

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Predictive Alerts

ETA-to-threshold forecasting and baseline anomaly detection on CPU, memory, and restart rate — a warning before the page fires, not after.

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✦

AI Monitoring

ETA prediction, anomaly detection, and RCA — the full AI capability matrix, mapped across every asset type.

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⛨

Security Detection

40 signatures catch exploit scans and probes in real time — RCE, SSRF, Log4Shell, webshells, and more.

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⛔

Automated Response

Block the offending IP or push an agent update in one click — every action human-approved, nothing runs unattended.

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Session Recording

Every SSH session recorded and searchable by command — audit trail built in, no separate tool required.

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AI Chat

Ask your infrastructure a question. Answers are grounded in live data via real tool calls, not guesses.

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Asset Types

Every capability, applied to what you actually run.

The nine capabilities above aren't asset-specific — here's how they come together for each thing Kllyroo watches.

Causal AI

Correlation, not coincidence.

Most tools show you every alert that fired. Kllyroo 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.

Deterministic cause → effect rules, not a black-box score you have to trust blindly
One incident, one root cause, one fix — instead of paging three different owners
Every correlation ships with the reasoning attached, so your team learns the pattern too

See how the correlation engine works →

Kllyroo predicted-critical alert showing root cause and recommended action

Kubernetes Monitoring

Your clusters, in the same dashboard.

No in-cluster operator to install or upgrade — the same lightweight agent that watches your hosts polls the cluster it runs alongside.

Nodes, deployments, and pods — alerted like everything else.

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.

Node, pod, and deployment status tracked continuously via the agent's own cluster access
Restart-rate anomaly detection on pods — baselined per pod, not a fixed global count
Cluster-wide "no data" alerting, so a dead connection pages you instead of going quiet

See the full Kubernetes alerting model →

Kllyroo Kubernetes view with nodes and deployments

Container Intelligence

Docker, watched the way a human would.

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.

Lifecycle detection: OOM-killed, crash-looped, image changed
Per-container history graphs with a date-range filter, same as hosts
Alerts on CPU, memory, "not running," and "unhealthy" — each with its own incident trail
Restart-rate anomaly detection — flags a container restarting more than its own normal cadence, not a fixed global threshold

See full container monitoring details →

Kllyroo container list with status, health, CPU and memory

Predictive Alerts

A warning before the page fires.

Kllyroo 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.

ETA-to-threshold prediction for CPU and memory, on hosts and containers
Self-learning baselines (EMA with outlier clipping) — no manual thresholds to tune
Every predictive alert ships with a plain-language reason, not just a number

See the prediction methodology →

Kllyroo AI Predictive Logs table of predicted alerts

AI Investigation

Ask your infrastructure a question.

Kllyroo'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.

"Which servers are down right now?" — answered from live status, not a stale snapshot
Unrecognized log lines get an LLM-generated explanation instead of being dropped silently

See what the assistant can and can't do →

Kllyroo AI assistant answering a question about infrastructure

Session Recording

Every SSH session, recorded and searchable.

Built-in terminal recording and command history for every SSH session through Kllyroo — the kind of audit trail teams usually pay for a separate tool (Teleport, StrongDM) to get.

Full-session video playback, indexed by server, user, and time
Command search across every recorded session — find "who ran that" in seconds

See recording & retention details →

Kllyroo terminal session recording playback

Security Detection

Catches the scan, not just the breach.

40 security signatures watch your web-facing logs for the exploit attempts that precede a real compromise.

Real exploit patterns, not keyword guessing.

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.

Block the source IP straight from the alert — queued for admin approval, applied by the agent on that server, never fully automatic

See all 40 signature categories → See how the human-approval queue works →

Fits Your Workflow

Alerts where your team already is.

Route by severity, by asset, or by integration — every notification carries the same root-cause explanation you see in the dashboard.

Slack

Microsoft Teams

PagerDuty

Opsgenie

Generic Webhook

Email & SMS

Kllyroo alert integrations for Slack and Teams

See routing rules & how notifications tie to incidents →

How It Compares

Not trying to out-feature Datadog.
Trying to out-explain it.

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	Kllyroo
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

Where We Stand

What we're good at today — and what we're not, yet.

A monitoring tool should be honest about its own blind spots. Here's ours, plainly.

Built & Production-Ready

223 root-cause signatures across web, app, DB, and queue layers
Cause → effect alert correlation, not just co-occurrence
Docker container monitoring — lifecycle, health, history, alerts
Kubernetes monitoring — nodes, pods, deployments, restart-rate & no-data alerting
Predictive ETA and baseline anomaly detection for CPU & memory
Human-approved automated response — IP blocking & agent updates, one click
Slack / Teams / PagerDuty / Opsgenie / webhook notifications
Built-in SSH session recording & searchable command history
AI chat grounded in live server/alert data via tool-calling

On The Roadmap

Predictive ETA & anomaly detection for Kubernetes pods (metrics collection is categorical today)
Code-level APM & distributed tracing
Active TLS certificate expiry checks
Cloud provider API integrations (AWS / Azure / GCP)

Get In Touch

Reach us directly.

Offices in India and the US — reach out and we'll get back to you.

Every incident hasa root cause.We find it first.

Nine capabilities. One agent.

Root-Cause Signatures

Causal Correlation

Container & Kubernetes Intelligence

Predictive Alerts

AI Monitoring

Security Detection

Automated Response

Session Recording

AI Chat

Every capability, applied to what you actually run.

Servers

Websites

Containers

Kubernetes

Correlation, not coincidence.

Your clusters, in the same dashboard.

Nodes, deployments, and pods — alerted like everything else.

Docker, watched the way a human would.

A warning before the page fires.

Ask your infrastructure a question.

Every SSH session, recorded and searchable.

Catches the scan, not just the breach.

Real exploit patterns, not keyword guessing.

Alerts where your team already is.

Not trying to out-feature Datadog.Trying to out-explain it.

What we're good at today — and what we're not, yet.

Built & Production-Ready

On The Roadmap

Reach us directly.

Every incident has
a root cause.
We find it first.

Not trying to out-feature Datadog.
Trying to out-explain it.