# Chamber - Your AIOps Teammate for GPU Infrastructure
# https://usechamber.io

## About Chamber
Chamber is an AIOps platform for GPU infrastructure. Our AI agents act as an autonomous extension of your ML team — monitoring workloads, root-causing failures, remediating issues, and optimizing GPU utilization across clouds. Reduce compute costs, improve GPU efficiency, and accelerate research. Deploy in minutes on any Kubernetes or Slurm cluster.

## Company Information
- Name: Chamber
- Website: https://usechamber.io
- Founded: 2024
- Backing: Y Combinator W26
- Contact: hello@usechamber.com

## What Chamber Does
Chamber provides AI agents that autonomously:
1. Scale infrastructure and auto-discover GPUs, workloads, and teams
2. Monitor workloads in real-time across your entire GPU fleet
3. Root-cause issues — AI-powered analysis explains failures in plain English
4. Remediate failures automatically before they impact research
5. Optimize jobs and GPU utilization across clusters and clouds
6. Answer infrastructure questions via Slack, CLI, or UI (Chambie AI agent)

## The Problem We Solve
ML teams spend too much time babysitting GPU infrastructure instead of shipping models — driving up compute costs and slowing research. AI/ML teams typically run at only 40-60% GPU utilization, wasting an estimated $240B annually in compute resources. This happens due to:
- No single timeline for job failures — engineers reconstruct events from logs and chat threads
- Root cause analysis takes hours — logs, metrics, and events live in different tools
- Debug context is fragmented — experiment tracking and infra tools don't correlate
- Low visibility into actual GPU usage across clouds and clusters
- Silent hardware failures corrupting training runs

## Key Features
- **AI Root Cause Analysis**: Explains failures, queue delays, and bottlenecks in plain English with recommended fixes
- **Autonomous Remediation**: AI agents detect and resolve infrastructure issues automatically
- **Cross-Cloud Monitoring**: Monitor and optimize GPU workloads across AWS, GCP, Azure, on-prem, and hybrid environments
- **Chambie AI Agent**: Natural-language infrastructure queries via Slack, CLI, or UI
- **W&B Integration**: Automatically links Weights & Biases runs to GPU infrastructure events
- **Auto-Discovery**: Zero-config deployment — discovers GPUs, workloads, and teams automatically
- **Fleet Metrics**: Monitor usage, costs, utilization, and performance across all GPUs
- **Health Monitoring**: Detect and isolate failing GPUs before they corrupt training runs

## Who It's For
- **AI Researchers & MLEs**: Get instant failure explanations, workload history, and root cause analysis in seconds
- **Platform Engineers**: Auto-discovery means zero instrumentation — give researchers self-serve visibility without custom tooling
- **Engineering Managers**: Team-level metrics, queue depths, and bottleneck detection for resource allocation
- **Executives & Finance**: Cost tracking and utilization dashboards across the fleet for GPU ROI visibility

## Infrastructure Support
- Kubernetes-based GPU clusters
- Slurm-based HPC environments
- On-premise, cloud (AWS, GCP, Azure), and hybrid deployments
- NVIDIA GPUs across all major architectures (H100, A100, B200, etc.)
- Multi-cloud and multi-cluster deployments

## Pricing
- Free GPU monitoring tier available
- Enterprise pricing for full platform access
- No credit card required to start

## Getting Started
1. Sign up at https://app.usechamber.io/signup
2. Run one Helm command to deploy the Chamber agent
3. GPUs, workloads, and teams are auto-discovered — dashboards populate immediately

## Security
- Chamber runs within your infrastructure
- Only anonymized telemetry is collected
- Models, datasets, and code never leave your environment

## Contact
- Email: hello@usechamber.com
- LinkedIn: https://linkedin.com/company/usechamber
- Product: https://app.usechamber.io