AI WORKLOAD INTELLIGENCE

Know what AI costs—and what to do about it

See OpenAI usage and spend by model and project, tie GPU and VM cost to AI workloads on AWS and Azure, and run automation and rightsizing from the same place you manage the rest of the estate—Smart Resource Management plus OptiCloud, applied to AI.

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Why CloudVectra for AI Workloads

Most platforms stop at AI cost dashboards. CloudVectra connects AI workload visibility directly to infrastructure intelligence and automation—turning insights into actions.

Instead of asking “What did AI cost me?”, CloudVectra answers “What should I optimize right now—and can it be automated?”

Capability	What you get
Unified AI + Infrastructure Intelligence Layer Built on Smart Resource Management to connect AI usage, cloud infrastructure, and cost into a single operational view.
Cross-Platform AI Workload Visibility Provides unified visibility across OpenAI workloads, GPU instances, and cloud-based compute environments.
Usage based Rightsizing for Compute & GPU Applies OptiCloud intelligence to recommend optimal sizing for GPU and VM workloads based on real usage patterns.
Action-Driven Optimization Engine Moves beyond dashboards by enabling automated actions like start, stop, and optimization workflows based on signals.
Closed-Loop Cost Optimization for AI Workloads Continuously monitors usage and cost, detects inefficiencies, and triggers optimization actions across AI and cloud resources.

Key Capabilities

How CloudVectra Optimizes AI Workloads

A unified intelligence layer for understanding and optimizing AI workloads across cloud infrastructure.

AI Cost & Usage Visibility

Gain clear visibility into how AI workloads consume tokens and generate cost across models, projects, and usage types. Understand real usage patterns—not just aggregated spend.

• OpenAI usage and cost analytics
• Token-level breakdown (input vs output)
• Cost by model, project, and usage type
• Daily and historical trend analysis

AI Workload Optimization

Identify underutilized GPU and compute resources used for AI workloads and take action automatically. CloudVectra extends Smart Resource Management to AI-driven infrastructure.

• Detect idle and underutilized GPU/VM resources
• Automated start/stop actions
• Workload-aware optimization signals
• Policy-driven automation

GPU Rightsizing & Efficiency

Optimize GPU and compute workloads using OptiCloud’s rightsizing intelligence. Align resource allocation with actual AI workload demand to reduce unnecessary spend.

• Rightsizing recommendations for GPU workloads
• Cost vs performance optimization insights
• Compute efficiency improvements

AI-Focused Cloud Cost Analysis

Filter AWS and Azure cost and usage to the resources that back AI training and inference. Instead of generic cost views, CloudVectra helps you isolate and understand AI-related spend across AWS and Azure.

• AI workload cost breakdown across cloud services
• GPU and compute cost attribution
• Filter and analyze AI-specific infrastructure usage
• Built on Advanced Cost Insights, focused for AI workloads

Available Today & What’s Next

CloudVectra already provides visibility and optimization for AI workloads, with continuous enhancements underway to expand control and governance.

Available Today

✔ AI cost & usage visibility (OpenAI)
✔ Token-level insights (input vs output)
✔ Cost breakdown by model, project, and usage (OpenAI)
✔ GPU / VM workload optimization
✔ Automated start/stop for idle GPU resources
✔ Rightsizing recommendations via OptiCloud

Expanding Next

→ AI budget controls (cost / token-based)
→ AI anomaly detection (usage & cost spikes)
→ Multi-model support across LLM providers (beyond OpenAI)
→ Advanced AI workload governance

What You Achieve

Lower AI Infra Waste

Eliminate idle and underutilized GPU/VM resources by combining visibility, rightsizing insights, and automated optimization actions.

Transparent API Spend

Understand how OpenAI workloads consume tokens and generate cost across models, projects, and infrastructure—with the ability to act on insights.

Aligned Capacity

Align GPU and compute resources with real workload demand using performance-aware recommendations and workload-driven optimization signals.

Governed automation

Reduce manual intervention while maintaining control by automating resource actions and identifying unusual usage patterns early.

Case Studies

AI Workloads — 75–80% GPU Cost Reduction

Problem: GPU instances supporting AI workloads were running continuously despite limited daily usage, leading to significant wasted compute spend.

Solution: CloudVectra applied automated scheduling using Smart Resource Management, aligning GPU usage with actual workload demand through policy-driven start/stop actions.

Result: Reduced GPU compute costs by 75–80% without impacting performance or production workflows.

OpenAI and infra cost in one attribution model

Problem: AI-related costs across models, projects, and cloud infrastructure were fragmented, making it difficult to identify cost drivers and control spend.

Solution: CloudVectra unified AI usage and infrastructure cost data, enabling detailed analysis across models, GPU resources, and usage patterns.

Result: Teams gained clear visibility into AI cost drivers and improved cost control with more predictable spending.

Inefficient AI/ML Workloads Driving High Costs

Problem: AI/ML workloads were over-provisioned or misconfigured, using expensive compute resources without matching actual performance needs.

Solution: OptiCloud identified inefficiencies, explained cost drivers, and recommended optimized resource configurations based on workload patterns.

Result: Reduced unnecessary spend while improving resource efficiency and preventing recurring cost issues.

Bring Intelligence to Your AI Workloads

Start optimizing AI cost, GPU usage, and cloud resources with a unified intelligence and automation platform.

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