KubeWatch is a monitoring platform built specifically for Docker and Kubernetes: one agent, one dashboard, and enough context on every alert that the person paged at 3am doesn't have to start from a blank terminal.
Most monitoring stacks are assembled, not designed: a metrics tool here, a log shipper there, an alerting system bolted on afterward, each with its own agent and its own dashboard. Getting from “an alert fired” to “here’s what actually changed” usually means opening four tools and cross-referencing timestamps by hand.
KubeWatch exists to collapse that into one place: metrics, logs, alerts, incidents, cost, and a root-cause suggestion generated from the actual signal that fired, not a generic runbook. It should take minutes to install and give a real answer, not just a graph, when something breaks.
Each of these is a real, shipped part of the platform, not a roadmap slide.
Every container and node streams CPU, memory, network, and request throughput to VictoriaMetrics-backed time series. Zoom from the last minute to months back without hitting a sampling gap.
Threshold and anomaly rules route to Slack or email. A firing alert can escalate straight into an incident with on-call paging, a timeline, and a postmortem, without switching tools.
When something fires, KubeWatch can correlate the alert with recent deploys and log output, then propose a root cause and, where confidence is high enough, a fix to review.
Cloud cost breakdowns sit next to token-level spend for OpenAI, Anthropic, and self-hosted model calls, plus GPU utilization for vLLM, Triton, and KServe inference servers.
Run it as a managed service, or self-host the entire platform on your own Docker or Kubernetes cluster under a perpetual license. The feature set is the same either way.
One agent per cluster or host collects metrics, logs, and events over an encrypted channel. No sidecars to inject, no cluster-wide operator to babysit.
From a single Docker host to a fleet of Kubernetes clusters across multiple clouds.
A small team running one production cluster gets the same real-time visibility and AI-assisted diagnosis as a platform team operating dozens of clusters across regions, without needing a dedicated observability engineer to keep the monitoring stack itself running.
Deploy the agent in about a minute, no cloud credentials required.