Prioritized
Know what matters first
Rank by savings, confidence, risk, and effort instead of sorting through generic cloud noise.
OptiCloud
Intelligent cloud optimization that actually gets implemented
OptiCloud cuts through noisy AWS and Azure signals to show what to fix first, why it matters, expected savings, and the next implementation step so teams optimize costs instead of just watching dashboards.
Ranked recommendations Savings context Implementation steps Workflow tracking
Optimization
Top priority recommendation
AWS EC2 - production
Rightsize overprovisioned compute group
Monthly savings
$18.4K
Risk
Low
Effort
2 hrs
$
Projected impact
$42K annualized savings after policy and resize recommendations.
Plain-language next step
Resize first, then review autoscaling floor.
Deep, Actionable Recommendations
A complete path from detection to savings.
Recommendations go beyond suggestions—each insight shows what was found, why it matters, how to resolve it, and the expected savings before it’s turned into an assigned task.
Recommendation flow
Rightsize compute safely
1
Detected
Dev compute group is oversized for observed CPU and memory trends.
2
Explained
Utilization stayed below target for 12 days and commitment coverage can absorb the smaller footprint.
3
Guided
Create a change task, resize during the approved window, then monitor health for seven days.
$
Savings
$2.4K monthly savings with low operational risk and tracked approval status.
Actionable
Move directly into work
Turn the recommendation into a ServiceNow, Jira, or internal task with owner-ready context.
Measurable
Validate the outcome
Track approvals, expected savings, and post-change health in the same optimization path.
Key Capabilities
More ways OptiCloud drives optimization
Rightsizing & Cost Optimization
Compare current and recommended resource sizes with savings, utilization, and risk context so teams can reduce spend without guessing.
Rightsizing decision
Same workload, cleaner resource size
Current t3.xlarge
$121.47/mo
CPU 12%
RAM 18%
Overprovisioned
Recommended t3.medium
$30.37/mo
CPU 78%
RAM 82%
Right-sized
75% cost savings Save $91.10/mo on this resource
Centralized Multi-Cloud Visibility
Bring AWS and Azure optimization opportunities into one prioritized view so teams can compare savings, ownership, and action status across clouds.
Multi-cloud visibility
One prioritized queue across providers
AWS 17 $75.9K
Azure 52 $2.2M
01 Overprovisioned compute
$18.4K/mo 02 Unused storage volumes
$1.8K/mo 03 Idle app service plan
$3.1K/mo Task Management & Workflow
Assign, track, and resolve recommendations using the built-in task system, or integrate with ServiceNow/Jira.
Notifications & Alerts
Automated alerts delivered via email, Slack, and Microsoft Teams help teams quickly act on optimization insights and reduce cloud waste.
Proven Results
Measured impact teams can see
Practical outcomes from turning cloud optimization into governed, repeatable operating workflows.
Up to 75%
Reduction in cloud costs through rightsizing and optimization
High adoption
Most optimization recommendations are acted on through guided workflows
Faster action
Improved adoption of optimization recommendations across teams
*Based on early customer usage data
Case Studies
Real customer scenarios
Practical examples of how teams use CloudVectra to move from operational complexity to clearer decisions and measurable action.
Scenario
Overprovisioned Azure App Service Plans
Problem
Azure App Service Plans were provisioned at higher tiers than required, resulting in consistently underutilized compute capacity and inflated monthly costs.
CloudVectra
OptiCloud analyzed actual application load and recommended downgrading App Service Plan tiers to match real usage patterns.
Impact
Optimized App Service costs while maintaining application performance and stability.
Scenario
Reliability & Performance Optimization Gaps
Problem
Workloads experienced inefficient scaling behavior and periodic performance degradation due to misconfigured resource allocation.
CloudVectra
OptiCloud identified configuration inefficiencies and recommended optimized autoscaling and resource allocation strategies.
Impact
Improved workload stability, reduced unnecessary cost overhead, and strengthened governance visibility.
Scenario
Misconfigured AI/ML GPU Workload
Problem
High-cost GPU instances were provisioned incorrectly or used without proper workload alignment, leading to unexpected significant cost spikes.
CloudVectra
OptiCloud identified the misconfiguration, analyzed resource usage patterns, and recommended appropriate instance types and sizing adjustments.
Impact
GPU spend was brought under control and deployment practices were improved to prevent future misconfigurations.
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