DeepAware AI

What do they actually do

DeepAware AI sells a data center infrastructure management (DCIM) platform built for GPU‑heavy AI data centers. The product combines real‑time monitoring with an AI scheduler that recommends or automates GPU workload placement, plus integrations to energy markets so operators can shift workloads to lower‑cost or lower‑carbon windows. Operators use a unified dashboard for alerts, policy tuning, and “what‑if” scenarios; an autonomous robotics module is listed as a future capability rather than generally available today (product).

The company is in early commercial stage and onboarding via pilots. DeepAware says it is piloting with a 30 MW+ operator with a six‑figure pilot agreement and recently joined YC Summer 2025 (YC company page, news). Reported outcomes to date are company‑stated figures from simulations and early pilots (e.g., a projected 15% energy savings in a 30 MW+ pilot and up to 30% energy waste reduction in simulations), not yet published as post‑pilot case studies (product, YC company page).

Who are their target customer(s)

• Large non‑hyperscaler data‑center operators running GPU halls: High power bills and stranded capacity from thermal/power limits force sub‑optimal GPU density; limited tools to shift heavy workloads to cheaper or lower‑carbon times increase costs and emissions.
• Enterprise or startup AI infrastructure teams with on‑prem GPU clusters: They need predictable performance for training/inference but lack reliable job placement/migration to avoid hotspots and power/thermal headroom issues, leading to lower throughput or costly overprovisioning.
• Colocation and GPU hosting providers selling rack/GPU time: They must meet uptime and cost expectations but often rely on manual throttling or reconfiguration to prevent failures, reducing billable utilization and increasing operational overhead.
• Data‑hall operations and floor technicians: They are flooded with alerts and repetitive tasks (inspections, cable swaps, manual job moves) without enough staff to run 24/7, increasing downtime risk; automation/robotics could reduce this burden.
• Energy procurement and sustainability teams at data centers: They want to lower electricity cost and carbon by using real‑time markets or demand‑response but lack automated, safety‑aware workload controls to shift GPU jobs without breaking SLAs.

How would they acquire their first 10, 50, and 100 customers

• First 10: Run paid, scoped pilots with large GPU halls and leading colo/GPU hosts using warm introductions (YC and existing pilot). Offer baseline measurement and discounts in exchange for a published case study/reference.
• First 50: Package a standard 6–12 week pilot, ROI calculator, and short procurement checklist. Use a small direct sales team for targeted outreach, technical demos, and white‑glove onboarding to create repeatable wins and references.
• First 100: Add channel partners (colo resellers, SIs, energy aggregators) and a low‑touch SaaS tier for smaller on‑prem clusters with packaged integrations. Automate onboarding, publish measured case studies and pricing tiers, and co‑sell with partners for regional reach.

What is the rough total addressable market

Top-down context:

The global DCIM market was estimated at about $3.2B in 2024 with long‑term growth expected through the decade (Precedence Research). As a proxy for where GPU‑heavy customers sit, global colocation revenue was roughly $69–72B in 2024, reflecting the scale of facilities that may adopt DCIM and optimization tools (Grand View Research, IMARC).

Bottom-up calculation:

If DeepAware targets ~500 large non‑hyperscaler GPU sites globally at an average $300k/site/year and ~1,500 smaller on‑prem/colo GPU clusters at $75k/site/year, the annual TAM for its current scope is roughly $225M + $112.5M ≈ $338M.

Assumptions:

• Population of ~500 large GPU sites (multi‑MW AI halls) and ~1,500 smaller GPU clusters globally in the near term.
• Average annual contract values of ~$300k for large sites and ~$75k for smaller clusters for DCIM + scheduler + market integrations.
• Focus is on GPU‑heavy environments; excludes hyperscalers building in‑house equivalents and excludes future robotics revenue.

Who are some of their notable competitors

• Schneider Electric — EcoStruxure IT: Broad, established DCIM suite for monitoring, capacity planning, and energy/PUE reporting used by enterprise and colocation operators; overlaps on facility monitoring and energy workflows but not focused on GPU workload scheduling or robotics.
• Nlyte: Traditional DCIM vendor with monitoring, capacity planning, and energy optimization; competes on telemetry/alerting/capacity recovery but lacks RL‑driven GPU scheduling and market‑aware workload shifting.
• Sunbird (dcTrack / Power IQ): Modern, SaaS‑friendly DCIM focused on rack‑level power management and energy analytics to reclaim stranded capacity; primarily DCIM/energy analytics rather than autonomous workload scheduling or robotics.
• Determined (determined.ai): On‑prem ML training platform and scheduler to maximize GPU utilization; overlaps on job placement/utilization but does not offer data‑hall DCIM or grid/energy‑market integrations.
• AutoGrid — AutoGrid Flex: Demand‑response and DERMS platform for grid‑scale flexibility and energy markets; overlaps on price/carbon shifting goals but not focused on rack‑level DCIM or GPU workload orchestration.