Chamber
Product & Competitive Intelligence
Reduces the $240B in annual GPU waste by automating infrastructure optimization for ML teams.
Company Overview
Builds an AI-powered AIOps platform with AI agents that autonomously monitor, root-cause, and remediate GPU infrastructure issues across clouds, reducing the estimated $240B in annual GPU waste.
Competitive Advantage & Moat
Product Roadmap & Public Announcements
AI agent "Chambie" for autonomous GPU monitoring and debugging. Full GPU workload observability with automatic performance insights and root cause analysis. Cross-cloud scheduling (AWS, GCP, Azure, Slurm, Kubernetes). Transparent preemption, topology-aware scheduling, MIG time slicing, SM occupancy tracking. SOC 2 Type I certified.
Signals & Private Analysis
Active development on S3 backend, KMS encryption, CLI versioning. Charles Ding previously led Amazon Project Greenland (GPU orchestration) and AWS CloudWatch Application Signals. Biggest risk is Run:ai (NVIDIA-backed) and hyperscaler native tooling. Agentic AI orchestration for autonomous GPU fleet management likely.
Product Roadmap Priorities
AI-powered real-time monitoring and root cause analysis of GPU workloads to detect anomalies, identify bottlenecks, and surface actionable insights automatically.
Chamber watches every GPU in your fleet like a hawk and instantly tells you why something broke before your engineers even notice.
It's like having a mechanic who can hear your car engine from a mile away and text you exactly which spark plug is about to fail before you're stranded on the highway.
ML-driven intelligent scheduling that automatically places and migrates GPU workloads across AWS, GCP, Azure, and on-premises clusters to maximize utilization and minimize cost.
Chamber figures out the cheapest and fastest place to run your AI training job across every cloud you use, then moves it there automatically.
It's like having a travel agent who automatically rebooks your flights across every airline in real time to always get you the fastest route at the lowest price without you lifting a finger.
Automated linking of ML experiment metrics to infrastructure performance data, enabling AI-driven optimization of resource allocation for training runs.
Chamber connects your ML experiment results directly to the GPUs running them so it can automatically figure out the perfect hardware setup for every training run.
It's like a chef who remembers exactly which oven temperature and pan size made each recipe turn out perfectly, then automatically preheats everything before you even start cooking.
Company Overview
Key Team Members
- Charles Ding, Co-Founder & CEO
- Shaocheng Wang, Co-Founder & CTO
- Jason Ong, Co-Founder
- Andreas Bloomquist, Co-Founder
Founder Force Multiplier
Charles Ding is a second-time founder (Bungee acquired by ClearDemand, grew to $3.5M ARR), then led Amazon Project Greenland (GPU orchestration) and AWS CloudWatch Application Signals. Ex-Meta and ex-Microsoft. Shaocheng Wang spent 9.5 years at AWS including 2+ years on large-scale GPU infrastructure. Jason Ong shipped GPU efficiency tooling at Amazon (Principal Engineer Award). Andreas Bloomquist was Sr. PM-Technical at AWS managing central ML infrastructure platforms. All four founders built the exact product they're now selling, inside Amazon.
Funding History
- 2026 | Charles Ding, Shaocheng Wang, Jason Ong, and Andreas Bloomquist co-found Chamber.
- 2026 | Accepted into Y Combinator W26 batch.
- 2026 | SOC 2 Type I achieved.
Competitors
- GPU Cloud: CoreWeave, Lambda Labs, RunPod.
- Orchestration: Run:ai (NVIDIA), Determined AI (HPE), Anyscale.
- Observability: DCGM, Weights & Biases, Neptune.ai.
- Kubernetes GPU: Volcano, Kueue, Yunikorn.
- Enterprise AIOps: Datadog, Dynatrace.
