Atmosly Cost Intelligence – AI-Powered Kubernetes Cost Optimization

Optimization Opportunities

Areas across your cluster where you can reduce costs by rightsizing workloads

Resize overprovisioned workloads

Save $151.28/mo

Lower costs by scaling down overprovisioned containers based on historical usage trends and AI recommendations.

CPU Usage 33.1% Unused

Memory Usage 71.15% Unused

Affected Pods 15 workloads

Remove idle nodes (no pods scheduled)

Save $89.45/mo

Detect and decommission idle nodes with no active workloads to reduce compute overhead.

Idle Nodes 3 nodes

CPU Cores 24 cores unused

Memory 96GB unused

Delete Unused PVCs

Save $0.00/mo

Identify and remove unattached Persistent Volume Claims (PVCs) to free up storage and cut storage costs.

Unused PVCs 0 PVCs

Storage Space 0GB

Status Optimized

AI-Powered Right-sizing Analysis

Machine learning recommendations based on historical workload patterns and usage trends

AI Recommendations

15 Workloads Analyzed

$151.28 Monthly Savings

89% Confidence Score

frontend-app

default

Current CPU 2 cores → 0.5 cores

Current Memory 4GB → 1GB

Save $23.45/mo

api-service

backend

Current CPU 4 cores → 1.5 cores

Current Memory 8GB → 3GB

Save $45.67/mo

database-cache

data

Current CPU 1 core → 0.25 cores

Current Memory 2GB → 0.5GB

Save $12.34/mo

How you Save cost with Atmosly

Switch Off Env's when not required

Built-in automation to automatically shut down environments when not in use, saving costs during idle periods.

Smart Scheduling

Automatically detect idle environments and schedule shutdowns during off-hours, weekends, and holidays.

Custom Policies

Set custom policies for different environments - dev environments can be shut down nightly, while staging environments follow business hours.

Auto Start/Stop

Automatically restart environments when developers need them, ensuring no productivity loss while maximizing cost savings.

Scale down Nodes On-demand or Schedule scale down

Intelligent node scaling with automation to reduce compute costs by scaling down nodes when demand is low.

Automated Scaling

Automatically scale down nodes during low-usage periods and scale up when demand increases, optimizing compute costs.

Scheduled Scaling

Set up scheduled scaling policies to reduce node count during nights, weekends, and holidays when workloads are minimal.

Demand-Based Scaling

Scale nodes based on actual workload demand, ensuring you only pay for the compute resources you actually need.

Run Workloads on SPOT for better Price to performance

Leverage spot instances for non-critical workloads to achieve up to 90% cost savings compared to on-demand pricing.

Spot Instance Management

Automatically identify and migrate suitable workloads to spot instances while maintaining application reliability.

Fault Tolerance

Implement intelligent fallback mechanisms to ensure critical workloads remain available even if spot instances are terminated.

Cost Optimization

Achieve significant cost savings by running batch jobs, development environments, and non-critical services on spot instances.

Get AI cost recommendation for Kubernetes Workloads

AI-powered cost optimization recommendations based on historical usage patterns and workload analysis.

Machine Learning Analysis

Analyze historical workload patterns to provide intelligent recommendations for resource optimization and cost reduction.

Predictive Analytics

Predict future resource needs and cost trends to help you make informed decisions about capacity planning.

Smart Recommendations

Receive actionable recommendations for right-sizing, instance type selection, and workload placement optimization.

Right size application workloads with smart recommendations

Optimize resource allocation for your applications based on actual usage patterns and performance requirements.

Resource Optimization

Analyze CPU and memory usage patterns to recommend optimal resource requests and limits for each workload.

Performance Monitoring

Monitor application performance to ensure right-sizing recommendations don't impact service quality or reliability.

Cost Impact Analysis

Calculate potential savings for each right-sizing recommendation with detailed cost impact analysis.

Overall Cost Analysis and RI saving

Comprehensive cost analysis with Reserved Instance recommendations to maximize savings across your entire infrastructure.

Cost Breakdown

Detailed analysis of costs by service, region, instance type, and usage patterns to identify optimization opportunities.

RI Recommendations

Get intelligent recommendations for Reserved Instance purchases based on your usage patterns and commitment preferences.

Savings Tracking

Track realized savings from implemented optimizations and Reserved Instance purchases with detailed reporting.

Frequently Asked Questions

How accurate is the cost tracking in Atmosly Cost Intelligence?

Atmosly Cost Intelligence provides real-time cost tracking with 99.9% accuracy by directly integrating with cloud provider APIs. We track actual resource usage and costs, not estimates, ensuring you have precise visibility into your Kubernetes spending across all clusters and namespaces.

What cloud providers does Cost Intelligence support?

Cost Intelligence supports all major cloud providers including AWS, Google Cloud Platform, Microsoft Azure, and hybrid cloud environments. We provide unified cost tracking and optimization recommendations across all your cloud infrastructure regardless of provider.

How much can I expect to save with Cost Intelligence?

Most customers save 20-40% on their Kubernetes costs within the first 3 months. Savings come from identifying unused resources, right-sizing workloads, implementing auto-scaling, and following optimization best practices. The exact savings depend on your current infrastructure efficiency.

Can I set up cost alerts and budgets?

Yes! You can set up custom budget thresholds, spending alerts, and anomaly detection. Get notified via email, Slack, or webhooks when costs approach limits or when unusual spending patterns are detected. This helps prevent cost overruns and ensures proactive cost management.

How does the automated optimization work?

Automated optimization uses AI to analyze your resource usage patterns and automatically implements cost-saving measures like scaling down underutilized resources, cleaning up orphaned volumes, and right-sizing workloads. You can set policies to control what actions are taken automatically.

Can I generate cost reports for different teams?

Absolutely! You can create team-specific cost reports with custom filters, date ranges, and resource groupings. Set up scheduled reports that automatically distribute cost insights to different stakeholders, helping teams understand their spending and optimize accordingly.

Is there a learning curve for Cost Intelligence?

Cost Intelligence is designed to be intuitive and user-friendly. The dashboard provides clear visualizations, and optimization recommendations are explained in plain language. Most users can start seeing cost insights within minutes of setup, with no complex configuration required.

How does Cost Intelligence handle multi-tenant environments?

Cost Intelligence provides tenant-specific cost tracking and reporting, allowing you to allocate costs accurately across different teams, projects, or customers. You can set up cost allocation rules and generate separate reports for each tenant while maintaining overall cost visibility.

Ready to Optimize Your Kubernetes Costs?