01
Actionable Unified AI + Infrastructure Intelligence Layer
Built on Smart Resource Management to connect AI usage, cloud infrastructure, and cost into a single operational view.
Included in CloudVectra
AI Workload Intelligence
Know what AI costs, where it runs, and what to optimize next.
Bring OpenAI usage, AI/ML service spend, GPU and VM cost, and exposure risk into one operational view. CloudVectra connects token-level insight with OptiCloud rightsizing and Smart Resource Management actions for AI workloads.
OpenAI tokens Azure Foundry + Bedrock Top AI instances Optimize + secure
AI operations view
Cost, usage, workload, risk
AWS
Azure
OpenAI
Token spend
$48.8K
Input 64%
Output 36%
Top AI cost
GPU spend
g5.12xlarge $8.4K
Azure NCas $5.9K
Bedrock apps $3.2K
Exposure risk
2 public
AI/ML endpoints and notebooks flagged for public access review.
Security review
OptiCloud recommendation
Rightsize expensive AI instances
Governed action
idle-gpu-train-04
Idle 18 hours
Automation ready 75% waste cut
Why CloudVectra
Built for AI workloads, not just AI cost reports.
Most tools stop at spend charts. CloudVectra connects AI usage, infrastructure cost, optimization actions, and exposure risk so teams can decide what to fix next.
02
Actionable Cross-Platform AI Workload Visibility
Provides unified visibility across OpenAI workloads, GPU instances, and cloud-based compute environments.
Included in CloudVectra
03
Actionable Usage based Rightsizing for Compute & GPU
Applies OptiCloud intelligence to recommend optimal sizing for GPU and VM workloads based on real usage patterns.
Included in CloudVectra
04
Actionable Action-Driven Optimization Engine
Moves beyond dashboards by enabling automated actions like start, stop, and optimization workflows based on signals.
Included in CloudVectra
05
Actionable Closed-Loop Cost Optimization for AI Workloads
Continuously monitors usage and cost, detects inefficiencies, and triggers optimization actions across AI and cloud resources.
Included in CloudVectra
06
Actionable AI/ML Security & Public Exposure Detection
Identifies publicly exposed AI/ML endpoints, notebooks, and workload resources so teams can review access risk alongside cost and optimization signals.
Included in CloudVectra
Key Capabilities
How CloudVectra Optimizes AI Workloads
A unified intelligence layer for understanding and optimizing AI workloads across cloud infrastructure and OpenAI.
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
AI/ML Security & Exposure Detection
Surface public exposure risks around AI/ML workloads before they become governance issues. CloudVectra helps teams review AI endpoints, notebooks, and supporting resources in the same context as cost and optimization.
- • Detect publicly exposed AI/ML endpoints and notebooks
- • Review risky access on AI workload resources
- • Connect exposure findings with workload ownership
- • Prioritize security review alongside cost optimization
AI/ML exposure review
Public access exposures
ml-notebook-prod
Notebook has public network access enabled.
ai-inference-api
Endpoint exposure needs owner review.
Next action
Assign security review to the workload owner with cost and resource context.
What You Achieve
01
Lower AI Infra Waste
Eliminate idle and underutilized GPU/VM resources by combining visibility, rightsizing insights, and automated optimization actions.
02
Transparent API Spend
Understand how OpenAI workloads consume tokens and generate cost across models, projects, and infrastructure—with the ability to act on insights.
03
AI Security Visibility
Identify exposed AI endpoints, notebooks, and resources early, link them to ownership, and prioritize fixes alongside cost and optimization insights.
04
Governed automation
Reduce manual intervention while maintaining control by automating resource actions and identifying unusual usage patterns early.
Case Studies
Real customer scenarios
Practical examples of how teams use CloudVectra to move from operational complexity to clearer decisions and measurable action.
Scenario
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.
CloudVectra
CloudVectra applied automated scheduling using Smart Resource Management, aligning GPU usage with actual workload demand through policy-driven start/stop actions.
Impact
Reduced GPU compute costs by 75–80% without impacting performance or production workflows.
Solution
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.
CloudVectra
CloudVectra unified AI usage and infrastructure cost data, enabling detailed analysis across models, GPU resources, and usage patterns.
Impact
Teams gained clear visibility into AI cost drivers and improved cost control with more predictable spending.
Impact
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.
CloudVectra
OptiCloud identified inefficiencies, explained cost drivers, and recommended optimized resource configurations based on workload patterns.
Impact
Reduced unnecessary spend while improving resource efficiency and preventing recurring cost issues.
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Bring Intelligence to Your AI Workloads
Start optimizing AI cost, GPU usage, and cloud resources with a unified intelligence and automation platform.