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CloudlyMELT

Unified AI-native Observability

Network → GPU → Application: The only platform that correlates all three layers to find root causes in seconds MTTR: Hours → Seconds
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Meet the Experts
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Industry Reality Check-2026

The $77B Problem You're Facing

  • AI GPU market: $77.15B by 2035 (22% CAGR)
  • Average GPU utilization: 15-25% in Kubernetes clusters
  • 60-70% of GPU budgets wasted on idle resources
  • Downtime cost: $5,600/minute + SLA penalties
  • Distributed training: 50% slower due to stragglers
  • Average MTTR: 4+ hours for GPU-related issues
  • Root cause unknown in 60% of incidents

Your Costs Today:

Single H100: $30-50/hour

The Silent Killers of Your ROI

The Idle GPU Epidemic

  • Problem: GPUs sitting at 5-10% utilization
  • Cost: $60/hour per idle H100 = $525,600/year
  • Why it happens: No visibility into actual usage patterns

The Straggler Bottleneck

  • Problem: 1 slow GPU bottlenecks 23 fast ones
  • Cost: Training 2-3x slower = 8 days wasted per job
  • Impact: $12,960 wasted per training run

The Blind Spot Problem

Can’t correlate network issues to GPU failures. Cost: Hours of debugging wrong layer. Impact: 4+ hour MTTR

The Silent Throttle

GPUs thermal throttling without alerts. Cost: 2-3x latency increase undetected. Impact: SLO breach before you know it

From “Blind to Brilliant” with 5 Killer Capabilities

What If You Could See Everything?

Cross-Layer Correlation

  • See GPU ↔ Pod ↔ Job ↔ Team connections
  • Instant root cause: "Which pod is using GPU 7?
  • Business Value: Reduce MTTR from hours to seconds

Predictive Intelligence

  • Predict GPU failures 48 hours in advance
  • Prevent $2M SLA penalties (real customer save)

LLM-Powered Root Cause

  • GPU-3 thermal throttling caused inference timeout in checkout-service

GPU-3 thermal throttling caused inference timeout in checkout-service

Error Budget Tracking

  • Know how much "failure allowance" remains before SLO breach

Service Dependency Map

  • Visual graph showing which services depend on which GPUs

CloudlyMELT Platform Overview

Not a pre-AI era platform, but an AI-native solution with building blocks for 100% customizable observability

Real-Time Monitoring

Cross-Layer Correlation

Cost Attribution

Multi-Tenant Fairness

AI-Powered Analytics

Predictive Failure Detection

Performance Optimization

Security & Compliance

Key Differentiators

  • Kubernetes-Native: Deep Pod-to-GPU correlation (most available solutions can't do this)
  • Multi-Mode: Local, remote, or hybrid GPU monitoring (no hardware lock-in, well abstracted)
  • ML-Powered: Predictive analytics, not just reactive dashboards (path to AI-native DevOps)
  • Cost-First: Every metric tied to dollars spent/saved
  • Open Standards: OpenTelemetry, Prometheus, DCGM (no vendor lock-in)
  • 3-Layer Correlation : Network + GPU + Application in one view
  • LLM Explanations : Root cause in plain English, not just metrics
  • 3-Layer Correlation : Network + GPU + Application in one view
  • LLM Explanations : Root cause in plain English, not just metrics

See the Cascade Before It Crashes

Unlike Datadog (expensive, generic) or Prometheus (DIY complexity), CloudlyMELT is purpose-built for AI infrastructure with GPU-first design spanning all of core infrastructure, network and AI applications.

Competitive Landscape - Why CloudlyMELT stands out

Feature CloudlyMELT Datadog GPU Prometheus + DCGM Run:ai NVIDIA Base Command
GPU Monitoring ✅ Native ⚠️ Add-on ✅ DIY ✅ Native ✅ Native
K8s Integration ✅ Deep ⚠️ Basic ❌ Manual ✅ Deep ⚠️ Basic
Cost Attribution ✅ Built-in ❌ Generic ❌ None ⚠️ Basic ❌ None
Failure Prediction ✅ ML-Powered ❌ Rules ❌ None ❌ None ⚠️ Basic
Multi-Tenant Fairness ✅ DRF / Jain ❌ None ❌ None ✅ Yes ⚠️ Basic
Straggler Detection ✅ Auto ❌ None ❌ Manual ❌ None ❌ None
LLM-Powered RCA ✅ Native ❌ None ❌ None ❌ None ❌ None
Network → GPU Correlation ✅ Automatic ⚠️ Manual ❌ None ❌ None ❌ None
Error Budget Tracking ✅ Built-in ⚠️ Add-on ❌ DIY ❌ None ❌ None