Chamber

Chamber is an AIOps platform for Kubernetes GPU clusters used by machine learning teams. It addresses the operational burden of monitoring training workloads, diagnosing failures, reclaiming idle GPU capacity, and maintaining node health. Agents act autonomously when risk is low and escalate to humans when risk is high, allowing researchers to focus on model development rather than cluster operations.

Core capabilities include a Workload Explorer for searching and filtering jobs across clusters, AI Root Cause Analysis that correlates logs, metrics, events, and scheduling data, and Chambie, a conversational agent accessible via Slack, CLI, Python SDK, web console, and webhooks. Additional modules cover automatic dashboards, notifications, cost forecasting, and advanced orchestration with fair-share scheduling, budget-based governance, GPU fractioning, and cross-cloud workload routing. The platform deploys into the user's own Kubernetes environment via Helm, with data remaining in the user's infrastructure.

Chamber is intended for AI/ML teams and machine learning engineers managing GPU fleets. Pricing is tailored based on fleet size, infrastructure, and team requirements, with plans discussed directly with the company. The platform is SOC 2 Type I and Type II attested.