Chamber

Autopiloting your AI infrastructure

Winter 2026ActiveB2BInfrastructureArtificial IntelligenceDeveloper ToolsB2BEnterprise SoftwareAISan Francisco, CA, USA

Company

https://www.usechamber.io

Chamber puts your AI infrastructure on autopilot. Our agentic AI enabled platform orchestrates, governs, and optimizes your AI infrastructure so teams can run ~50% more workloads on the same GPUs without manual intervention. It operates like an autonomous infrastructure team: continuously monitoring GPU clusters, forecasting demand, detecting unhealthy nodes, and reallocating resources in real time to where they create the most impact. Your ML teams move faster, infra waste drops, and GPU bottlenecks disappear.

Verdict

High Signal

Market Opportunity

GPU infrastructure observability and orchestration for enterprise AI/ML teams is a massive and fast-growing market — every company scaling AI workloads needs this, and GPU costs are enormous ($48k/mo waste metric cited on site). ICP is clear: ML research and engineering teams at mid-to-large enterprises running distributed training workloads. B2B SaaS with clear monetization path (pricing page exists), and the problem — GPU underutilization, failed runs, debugging overhead — is acute and well-understood.

High Signal

Founder Signal

Exceptionally strong team with directly relevant experience. Charles Ding (CEO) founded Bungee (acquired by ClearDemand), grew it to $3.5M ARR, then led Amazon Project Greenland — literally building GPU orchestration at Amazon — and AWS CloudWatch Application Signals. Shaocheng Wang (CTO) spent 9.5 years at AWS, including 2+ years on large-scale GPU infrastructure and AI workload platforms. Jason Ong shipped GPU efficiency tooling at Amazon (Principal Engineer Award). Andreas Bloomquist was Sr. PM-Technical at AWS managing central ML infrastructure platforms and launched CloudWatch Application Signals. All four founders built the exact product they're now selling, inside Amazon.

Medium Signal

Competition

Direct competitors include Run:ai (acquired by NVIDIA, dominant in GPU scheduling/orchestration), Determined AI (acquired by HPE), and MLflow/Weights & Biases for experiment tracking. SkyPilot and Volcano exist for Kubernetes-level GPU scheduling. Chamber's angle — agentic AI-native observability + root cause analysis + orchestration in one platform — is differentiated from pure schedulers, but Run:ai/NVIDIA integration is a real threat. The team's insider knowledge of how Amazon built this internally is a meaningful moat, but the space is getting crowded fast.

Medium Signal

Product

Product has a live UI demo, pricing page, docs, and ROI calculator — solid for an early-stage YC company. The website shows a detailed Workload Explorer with real-looking metrics (94.9% success rate, 198/256 GPUs active, cost data per job). However, no named customer logos, no published revenue figures, and no external testimonials — the 'built by engineers from Amazon/Meta/Microsoft/Flexport/Optimizely' framing is team pedigree, not customer logos.

OverallA Tier

Chamber is one of the strongest team-market fits in W26 — four founders who literally built GPU orchestration and observability infrastructure at Amazon are now selling it as a product to the market they know intimately. Charles Ding's prior exit (Bungee → ClearDemand, $3.5M ARR) plus deep domain expertise from Project Greenland makes this team unusually credible. The product is visually polished with real UI and a pricing page, though no named customer logos or revenue metrics are visible yet. The biggest risk is Run:ai (now backed by NVIDIA) and the broader ecosystem of AWS/GCP native tooling making this a feature rather than a standalone product. If they can close 5-10 paying enterprise customers quickly and prove the agentic orchestration layer isn't easily replicated by hyperscalers, this is a strong A.

Active Founders

Charles Ding

Founder & CEO

Second Time Founder with 1X Exit | Former Engineering Leader at Meta & Amazon | Ex-Microsoft. We are building to accelerate the world’s AI innovation through efficiency.

Andreas Bloomquist

Founder

I have a passion for distilling complex problems into elegant simple solutions for customers. Ex-Aamzon Product Manger with experience delivering observability and GPU efficiency solutions. I also have a passion for technical selling and GTM strategies. From my experience both Optimizely and Amazon, I am a believe in the power of experimentation to iterate quickly and deliver results.

Jason Ong

Founder

I’m a software engineer from Malaysia who moved to the U.S. in 2016. I’ve built high-impact systems at Amount, Avant, Flexport, and Amazon. I’ve worked across fintech, logistics, and GPU-related scheduling tooling, where I saw how hard distributed training is for many teams. I’m now co-founder of Chamber, focused on simplifying GPU orchestration for training workloads.

Shaocheng Wang

Founder

I am a cofounder of Chamber and a former Senior Software Engineer at Amazon. Over the past 9+ years, I’ve built and launched multiple 0→1 AWS products, with deep expertise in large-scale observability, distributed systems, and AI infrastructure efficiency. At Chamber, I’m applying this experience to build intelligent AI workload orchestration and observability software that helps companies run AI workloads much more efficiently.

Chamber

TierA Tier

BatchWinter 2026

Team Size4

StatusActive

LocationSan Francisco, CA, USA