Your GPU & AI Infrastructure fleet,
performing at its peak.
Kubex continuously monitors AI and ML workload behavior across your entire infrastructure, learning patterns of GPU usage and actively tuning container and node resources in real time. This includes GPU request optimization, GPU bin packing, and even advanced features like node pre-warming. The result: Your AI teams move faster.
Your GPUs work harder. Costs go down.
The Problem
AI infrastructure performance doesn’t manage itself.
AI infrastructure is expensive and unforgiving. When things aren’t right, jobs run slower than they should. And your platform team has no easy way to show what the investment is actually delivering.
Monitoring tools can surface the symptoms. Kubex addresses the root cause by continuously tuning resource allocations, scheduling policies, and utilization profiles to keep your AI workloads performing and your infrastructure earning its keep.
SOUND FAMILIAR?
“I need to keep GPUs busy, but without becoming the bad guy who says no to users.”
Kubex closes the loop. Instead of generating reports that require manual follow-up, it continuously optimizes GPU performance and utilization so your AI infrastructure delivers more, automatically.
How It Works
Continuous autonomous optimization, governed by your policies.
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Analyze
Ingests real-time and historical metrics across all namespaces, clusters, and workload types — building predictive behavioral models for every service. Models down to the container, enabling optimization of individual components even if they are launched from the same template.
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Optimize
Calculates optimal sizing, identifies bottlenecks, and uses advanced agents to predictively recommend tuning for schedulers, autoscalers, cloud scale groups and other components.
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Automate
Applies changes autonomously within policy guardrails, predictively adjusting requests, limits, and autoscaling parameters to keep things running safely. Automation controller can mutate or in-place resize at the individual container level, taking optimization to the next level.
Capabilities
What autonomous optimization covers.
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Precise GPU Request Sizing
Analyzes actual workload behavior to right-size GPU resource requests, eliminate over-allocation waste, and enable fractional GPU assignments so every sliver of GPU capacity is put to productive use.
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Intelligent AI Job Scheduling
Safely share a GPU across multiple containers by integrating with the KAI scheduler to automate fractional allocations in production, maximizing utilization without contention or compromise to workload performance.
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GPU Model Optimization
Select the best GPU model for each workload based on its specific compute requirements, and where supported, automatically determines the optimal MIG slice to maximize yield and efficiency on shared hardware.
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Predictive Node Scaling
Learns GPU workload patterns to proactively pre-warm nodes ahead of demand, eliminating startup latency, while continuously tuning autoscaler configurations, including scale-to-zero for idle GPU resources to match real demand rather than static guesses.
Results
What SRE teams achieve with Kubex.
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40-70%
Improvement in GPU utilization efficiency, sustained autonomously
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2-3X
Cost per token redux
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< 1 Day
To full GPU fleet visibility and performance baseline
Control & Governance
Autonomous doesn’t mean uncontrolled.
Human in the loop provision allows you to stay on top of automation and changes. Sensitive workloads can be placed in recommendation-only mode. Everything Kubex does is logged, auditable, and reversible.
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What you control
- Optimization scope by team, project, or GPU pool
- Min/max resource bounds per workload class
- Performance SLO targets per workload type
- Change velocity — how aggressively Kubex acts
- Rollback triggers and automatic revert conditions
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What Kubex handles autonomously
- Continuous GPU rightsizing for performance and efficiency
- KAI Scheduler tuning for throughput and latency targets
- GPU MIG Partition Allocations
- Cost and performance attribution across teams and workloads
- Change scheduling around job patterns and demand peaks
- Rollback if post-change performance metrics degrade
See your GPU and AI Infrastructure fleet performing at its peak.
See Kubex in action for yourself or talk to our team about your AI infrastructure environment. Most teams have full fleet visibility and autonomous optimization running within days of deployment.
