Chamber — Observability, Prompts & Evals Platform
Observability, Prompts & EvalsLayer 4UnknownFounded 2024|San Francisco, CA|2-10
Visit websiteWhat is Chamber?
Chamber built Chambie, an AI-powered AIOps agent that autonomously monitors, root-causes, and remediates GPU infrastructure issues across clouds. Part of YC W2026, the company was founded by four ex-Amazon engineers: Charles Ding (CEO, second-time founder with a .5M ARR exit), Andreas Bloomquist (launched AWS CloudWatch Application Signals), Jason Ong (GPU scheduling at Amazon), and Shaocheng Wang (9.5+ years at AWS).
Platform engineers currently spend half their time keeping GPU infrastructure running, while ML researchers lose hours when training runs fail because diagnosing failures means digging through Kubernetes events, node logs, and GPU metrics in separate tools. Chamber unifies all of this with a single Helm command deployment, auto-discovery of GPUs, workloads, and teams, AI root cause analysis in plain English, and autonomous remediation.
The platform supports cross-cloud management (AWS, GCP, Azure, on-prem), workload orchestration, experiment tracker integration (W&B), and cost analytics. Chamber is SOC 2 Type I certified and targets the K/month average GPU waste metric. Rated A-tier on YC Tier List as one of the strongest team-market fits in the W26 batch.
Key Features
- ✓ML infrastructure automation
- ✓AIOps agent
- ✓Debugging automation
- ✓Infrastructure optimization
- ✓ML pipeline monitoring
Pros & Cons
Pros
- +Exceptionally strong team-market fit — all 4 founders built GPU infrastructure at Amazon
- +CEO is a second-time founder with a successful .5M ARR exit
- +Production-ready with SOC 2 certification, Helm-based deploy, and SDK/API/CLI access
- +Large and fast-growing market in GPU infrastructure observability
- +Free GPU Intelligence Dashboard provides low-friction entry point
Cons
- -Run:ai/NVIDIA integration poses competitive threat from the hardware layer
- -Space getting crowded with Determined AI, SkyPilot, plus Datadog adding GPU features
- -No named customer logos disclosed publicly yet
- -Per-GPU pricing model still being refined with early customers
Chamber Pricing
Free trial available
FreeFree
- ✓GPU Intelligence Dashboard
Per-GPUPer-GPU-under-managementmonthly
- ✓Full AIOps agent
- ✓Cross-cloud management
- ✓Autonomous remediation
- ✓Cost analytics
- ✓Volume discounts
Common Use Cases
ML engineering teams managing AI infrastructure
- •ML ops automation
- •Infrastructure debugging
- •Pipeline optimization
- •ML team productivity
Using Chamber with Respan
Chamber manages GPU infrastructure for ML teams while Respan monitors the LLM inference workloads running on that infrastructure. Together they provide visibility into both the hardware layer and the AI application layer.
- ✓Monitor GPU health with Chamber while tracking LLM inference performance with Respan
- ✓Correlate GPU infrastructure issues with LLM latency spikes via Respan
- ✓Optimize both hardware costs (Chamber) and LLM costs (Respan) in a unified workflow
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Last verified: March 27, 2026