Why Now: AI Data Centers Are Hitting an Operational Ceiling
Power, density, and AI workload variability are creating a new software layer opportunity. Uptime Institute reports that operators are facing rising costs, worsening power constraints, and AI-driven density requirements. Roughly one-third of surveyed operators are already running AI training or inference workloads.
New Relic reports that high-impact outages cost a median $2M per hour and that full-stack observability can materially reduce that hit. Infrastructure software has to catch up.
Thermal Hotspots
Thermal throttling during expensive jobs causes GPU degradation and reduces utilization to 70%
Wasted Capacity
Allocated vs actual GPU/CPU usage gaps leave stranded and idle infrastructure
Bad Nodes
Reliability drift, ECC/XID events, and node-specific degradations are hard to detect
Slow Root Cause
Downtime incidents costing $300K-$1M per hour with slow root cause identification
The InfraPulse Solution
InfraPulse turns telemetry into operational decisions that recover capacity before customers buy more hardware. We become the system that tells operators what is wasting compute and what to do next.
See the Thermal Wall
Correlate rack, server, GPU, and workload heat signatures. Detect throttling and cooling inefficiency before jobs fail.
Find Wasted Compute
Separate allocated from actually used GPU and CPU capacity. Highlight stranded and idle infrastructure.
Isolate Bad Nodes Faster
Detect recurring reliability drift, ECC/XID style events, and node-specific degradations with historical context.
Recommend Action, Not Noise
Explain what to move, drain, rebalance, or investigate before adding scheduler automation.
Reduce Downtime
Simplified matrix dependencies and unified monitoring with intelligent alerting and RCA acceleration.
AI-Driven Capabilities
InfraPulse leverages advanced AI to transform raw telemetry data into actionable intelligence, enabling proactive operations rather than reactive firefighting.
AI-Driven Inferences
Automatically correlate signals across thermal, power, GPU, and workload data to surface insights
Troubleshooting Chatbot
Interactive AI assistant to help diagnose issues and recommend remediation steps
Dashboard Creation
Intelligent dashboard generation tailored to your infrastructure and KPIs
Schema Definition
Automated schema discovery and normalization for incoming telemetry data
Organization Map Integration
Manage multiple data center locations with unified visibility and policy control
Product Roadmap
Start with thermal visibility, expand into full compute operations intelligence.
Thermal + Visibility
- Rack and node hotspot detection
- GPU and CPU telemetry ingestion
- Power / temperature / throttling monitoring
- Kubernetes and Slurm read-only context
Efficiency + Reliability
- Allocated vs actual utilization analysis
- Bottleneck diagnosis
- Bad node detection
- Fleet health scoring
Recommendation Engine
- Drain / rebalance suggestions
- Workload placement guidance
- Cooling-aware policy rules
- Capacity planning workflows
Closed Loop Optimization
- Kubernetes scheduler hints and APIs
- Policy automation
- What-if simulation
- Autonomous remediation
Target Customers
Primary buyer is the team accountable for utilization, reliability, and capacity decisions on shared compute infrastructure.
Enterprise AI Platform Teams
Internal GPU clusters for model training, fine-tuning, and inference. Need visibility into thermal constraints and utilization gaps.
Managed GPU Clouds
Need differentiation and margin protection through higher utilization and lower incident cost.
Research / HPC Operators
Shared clusters with high density, long jobs, and heterogeneous workloads requiring thermal and capacity management.
Ideal Customer Profile
- 50-5,000 GPUs and 200-20,000 servers
- Hybrid, on-prem, or co-located AI data centers
- Runs Kubernetes, Slurm, or both
- Feels power, cooling, and scheduling pain weekly
- Budget owner cares about utilization and incident cost
- Looking to delay capex by reclaiming hidden capacity
Integration Ecosystem
InfraPulse integrates with your existing infrastructure stack without requiring you to replace Kubernetes, Slurm, or DCGM on day one.
Recover 10-20% of Hidden Compute Capacity
Shorten root cause analysis, delay unnecessary capex, and optimize your AI infrastructure operations.
Why InfraPulse?
- Built on DataStelio Platform: Proven data ingestion and AI capabilities at scale
- Cross-Layer Visibility: Bridge facility data (power, cooling) and workload data in one plane
- Read-Only Start: Begin with visibility and recommendations, add automation when ready
- Non-Invasive Integration: Works alongside your existing tools, not a rip-and-replace
- ROI-Focused: Recover capacity, reduce incidents, delay hardware purchases