Metrics, alerting, incident management, AI observability, autoscaling, and more, from a single Docker host to a multi-cluster Kubernetes fleet.
Every container and node streams CPU, memory, network, and request throughput to high-resolution time-series charts. Zoom from the last minute to months back without sampling gaps.
Define threshold rules on any metric, route them to Slack or email, and let intelligent silencing cut the noise. A full audit trail shows every fire and resolution.
Escalate a firing alert into an incident, and KubeWatch pages whoever is currently on call while your team works it, timeline, responders, and related past incidents all on one page. Resolve it, then capture a postmortem with tracked action items.
Track every model call: tokens, latency, error rate, and spend, with a built-in price table for OpenAI, Anthropic, and more. Watch GPU utilization and scrape vLLM, Triton, and KServe inference servers from the same agent.
Report application API requests to see throughput, error rate, and p95 latency per route. Synthetic probes measure latency to your services, nodes, and the agent itself, with uptime tracking.
Point any OpenTelemetry SDK or Collector straight at KubeWatch. We ingest traces, metrics, and logs over OTLP/HTTP, decoding both Protobuf and JSON, so your existing instrumentation works with no rewrites and no vendor lock-in.
Set a per-workload policy and KubeWatch acts on the same metrics you already see. On Kubernetes it writes native HorizontalPodAutoscaler and Karpenter objects and lets the cluster execute them. On standalone Docker, where there is no HPA, KubeWatch is the orchestrator: it picks placement, scales containers, and routes traffic through a managed load balancer.
Your containers are only half the picture. Connect the databases, caches, message queues, CI/CD, and observability tools around them, and KubeWatch tracks their health, latency, and uptime, then pulls deep per-service metrics like connection pools, cache hit rates, and replication lag.
Pick a test type from the dropdown, point it at any URL, and KubeWatch runs it and reports the numbers that matter for that scenario, everyday latency percentiles for a load test, the breaking point and recovery time for a stress test, latency drift and uptime for a long-running soak, or how the target handles a sudden burst in a spike test.
Point the agent at your Kafka cluster's admin API and KubeWatch tracks topic size and consumer group lag right on the Overview page. Filter by cluster, topic, or consumer group to drill into exactly the partition that's falling behind.
Full visibility across Docker and Kubernetes, from a single node to multi-cluster fleets.
Model calls, GPUs, and inference servers are just another part of the stack KubeWatch already watches, not a separate product bolted on.
The first four are on the Pro plan; Self-Hosted AI Stack Observability and Autonomous Remediation are Enterprise. AI Log Diagnostics ships free on every plan and every self-hosted install, using a small model bundled in, with no API key and no per-token cost; bring your own OpenAI, Anthropic, or self-hosted key instead if you want a higher-accuracy provider for harder cases.
See how each one worksDeploy the agent in 60 seconds and every capability above starts working on your own containers, no separate setup per feature.