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Your EKS Cluster. Your Autoscaler. Fully Optimized.
Right-size your containers dynamically
Automatically detect and fix overprovisioned pods with multidimensional pod autoscaling
Optimize your cluster config automatically
Let EKS Copilot choose the most cost-efficient instance types — Spot, On-Demand, or commitments — in real time
Intelligent Instance Selection
Karpenter or Cluster Autoscaler with no lock-in. Automatically choose resource-efficient instance types for your workload
EKS Automation Features
Automate Your EKS Infrastructure Management — No Vendor Lock-in
nOps manages $2B in annual AWS spend for innovative brands, from startups to enterprises, saving them 50%+ autonomously.
Automated Container Rightsizing & EKS Visibility
Multi-Dimensional Pod Autoscaling & Dynamic Container Rightsizing
- Full lifecycle management: metrics → thresholds → action
- Automatically scales pods & container resources horizontally and vertically for optimal resource efficiency (works with your existing HPA)
- Tailor thresholds to your goals (cost vs. performance)
- One-click apply for jobs, deployments, daemonsets, statefulsets
Total EKS Visibility
- Benchmarking for cluster cost and performance
- All the metrics you need: costs, purchase types, container & node efficiency, potential savings
Dive Deeper Into Clusters
- Hourly visibility into node-level usage to identify underutilization
- Manage and troubleshoot complex workloads at scale in a single streamlined UI
- Check cluster status, click into nodes, explore, & take action
Optimize Your Existing Autoscaler
- Supports Karpenter and Cluster Autoscaler — no vendor lock-in
- Realtime workload reconsideration & continuous tuning
- Get discounts on all of your compute with an optimal blend of On-Demand, Spot & Commitments
Spark Workload Optimization
- Automatically detect & right-size Spark Workloads (driver and executor) pods at startup minimizing the disruption
- Reduce overprovisioning and cut Spark cost on EKS
- Align Spark runtime settings with container allocations for consistent efficiency of containers
Schedule Rightsizing
- Automated workload optimization and container rightsizing with precise scheduling to control performance impact and stability
- Run rightsizing during off-peak or defined maintenance windows to mimimize disruption and cost
- Create and test schedules to observe behavior across workloads before production rollout
Maximize Price Efficiency
Optimize across all compute purchase models — On-Demand, Reserved Instances, Savings Plans & Spot — automatically.
Maximize Resource Efficiency
Continuously optimize pod sizes, replicas, and node utilization to eliminate waste and ensure every resource delivers maximum performance per dollar.
100% Commitment Utilization Guarantee
End-to-end savings, risk free, with zero oversight
Spot Orchestration with ML
Workloads are continually shifted across diverse Spot using ML analysis of historical data, current pricing & availability, for max savings AND stability.
How EKS Automation Works
Get your EKS clusters up and running in minutes with our automated platform.
STEP 1 - Install Agent
Intstall our lightweight agent in your EKS cluster (instructions for Cluster Autoscaler and Karpenter)
STEP 2 - Visibility & Recommendations
Gain visibility into real-time and historical efficiency, with recommendations for config, node & container rightsizing, and pricing
STEP 3 - Enable Automation
Optimize continuously, saving time and money without changing your existing tools, infrastructure and processs
How nOps Compares
See why leading companies choose nOps for their cloud cost optimization needs.
| Capability | nOps | Traditional Tools |
|---|---|---|
| Works with Karpenter & Cluster Autoscaler |  Yes |
 Often requires specific setup or replacement |
| Unified Cluster, Node & Container Visibility | Yes |
Limited or siloed views |
| Hourly Node Utilization Metrics | Yes |
Daily or aggregated only |
| Dynamic Container Rightsizing | Fully automated |
Manual, with limited context |
| Spark Workload Optimization | Fully automated |
Does not exist |
| Multidimensional Pod Autoscaling | Fully automated |
Does not exist |
| Intelligent Instance Selection | ML-based Spot selection |
Manual or static config |
| Real-Time Workload Rebalancing | Automated |
Requires manual intervention |
| Spot Orchestration & Diversification | Yes |
Basic Spot support or none |
| 100% Commitment Utilization (RI/SP) | Guaranteed |
Manual management, no SLAs |
| Lightweight Agent, No Infra Change | Yes |
Complex setup or changes |
| Vendor Lock-in | None |
Often requires buy-in |
The Result
60-75% Cost Savings
100% of Usage Discounted
<1% Spot Terminations
Time Saved for Engineers
Frequently Asked Questions
Q: How does nOps handle container rightsizing?
nOps dynamically analyzes CPU, memory, and I/O utilization to automatically adjust container resource requests and limits, preventing waste while maintaining performance.
Q: Can I control how aggressively my containers are right-sized?
Yes. nOps will automatically suggest thresholds, but you can also define thresholds and policies to prioritize on cost, performance, or both — with full visibility and one-click approval for changes.
Q: What is Multidimensional Pod Autoscaling?
Multidimensional Pod Autoscaling combines the best of Kubernetes Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA) logic — adjusting both pod count and resource requests dynamically based on multiple performance metrics (CPU, memory, and custom signals). This ensures your workloads scale efficiently in every dimension.
Q: How is this different from standard HPA or VPA?
Standard Kubernetes autoscalers act independently — HPA adjusts the number of pods, while VPA adjusts pod resource limits. nOps intelligently coordinates both to prevent overprovisioning and underutilization, using real-time and historical data for smarter scaling decisions.
Q: Does nOps support Spark workload optimization on EKS?
Yes. nOps automatically tunes Spark executors and driver configurations to improve performance and cost efficiency — ideal for dynamic, compute-intensive data workloads.
Q: What benefits does Spark optimization provide?
Customers running Spark on EKS see significant reductions in job runtime and compute costs through automated resource tuning and instance selection.
Q: How accurate is nOps EKS cost data?
nOps cost data is 100% CUR-matched to your AWS billing data, ensuring precise chargeback, showback, and forecasting.
Q: Will this affect cluster performance or stability?
No — all optimizations are safe, validated, and applied gradually, ensuring zero disruption to running workloads.
Q: How does nOps handle Spot interruptions?
nOps continuously analyzes AWS Spot market signals (capacity trends, interruption rates, and pricing) to forecast risk. It rebalances workloads in real time across diverse Spot pools and On-Demand fallbacks to ensure availability. This ML-driven orchestration achieves sub-1% termination rates while maintaining performance consistency.
Q: What kind of visibility does nOps provide into EKS clusters?
You get real-time and historical insight into costs, utilization, and efficiency down to the pod, container, and namespace level.
Q: Can I allocate EKS costs to different teams or applications?
Yes — costs can be automatically mapped by team, environment, or product without manual tagging, supporting chargeback and showback models.
Q: Does nOps provide forecasting or anomaly detection for EKS costs?
It includes built-in forecasting and anomaly detection so you can spot cost trends, budget risks, and unexpected usage early.
Q: Does nOps require installing anything in my cluster?
Yes. nOps deploys a lightweight Kubernetes agent that securely collects utilization and cost data. It runs with minimal overhead and a minimal permissions footprint, so it won’t interfere with your workloads or control plane.
Q: When will I start seeing optimization insights?
Because nOps aligns all usage data with your AWS Cost and Usage Report (CUR) for precise cost attribution, initial recommendations appear within 1.5 – 3 days after agent installation. Once populated, dashboards and insights update continuously.
Q: How often does nOps analyze and update optimization data?
Metrics are collected on a one-minute basis. This enables nOps to make optimization recommendations and scaling decisions accounting for even short-lifetime workloads and brief load spikes in your most current usage patterns.
Q: Can I preview or approve changes before they’re applied?
Absolutely. nOps provides full visibility into every recommendation on our comprehensive EKS dashboards so you can approve actions manually or let our automation tools handle them automatically.
Q: How does nOps support multi-cluster or multi-account environments?
nOps offers a single, consolidated view of all your EKS clusters and AWS accounts. You can see utilization, cost, and efficiency trends across your entire organization — and optimize consistently with shared policies.
Q: Does nOps store any sensitive data from my workloads?
No. nOps only processes operational metadata such as container resources, node utilization, and AWS billing data. All information is encrypted in transit and at rest, ensuring your application data remains private and secure.
Q: Can I use nOps with Infrastructure-as-Code workflows?
Yes. nOps EKS optimization can be configured, enabled, and disabled through our robust infrastructure-as-code features.
Q: How does nOps handle container rightsizing?
nOps dynamically analyzes CPU, memory, and I/O utilization to automatically adjust container resource requests and limits, preventing waste while maintaining performance.
Q: Can I control how aggressively my containers are right-sized?
Yes. nOps will automatically suggest thresholds, but you can also define thresholds and policies to prioritize on cost, performance, or both — with full visibility and one-click approval for changes.
Q: What is Multidimensional Pod Autoscaling?
Multidimensional Pod Autoscaling combines the best of Kubernetes Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA) logic — adjusting both pod count and resource requests dynamically based on multiple performance metrics (CPU, memory, and custom signals). This ensures your workloads scale efficiently in every dimension.
Q: How is this different from standard HPA or VPA?
Standard Kubernetes autoscalers act independently — HPA adjusts the number of pods, while VPA adjusts pod resource limits. nOps intelligently coordinates both to prevent overprovisioning and underutilization, using real-time and historical data for smarter scaling decisions.
Q: Does nOps support Spark workload optimization on EKS?
Yes. nOps automatically tunes Spark executors and driver configurations to improve performance and cost efficiency — ideal for dynamic, compute-intensive data workloads.
Q: What benefits does Spark optimization provide?
Customers running Spark on EKS see significant reductions in job runtime and compute costs through automated resource tuning and instance selection.
Q: How accurate is nOps EKS cost data?
nOps cost data is 100% CUR-matched to your AWS billing data, ensuring precise chargeback, showback, and forecasting.
Q: Will this affect cluster performance or stability?
No — all optimizations are safe, validated, and applied gradually, ensuring zero disruption to running workloads.
Q: How does nOps handle Spot interruptions?
nOps continuously analyzes AWS Spot market signals (capacity trends, interruption rates, and pricing) to forecast risk. It rebalances workloads in real time across diverse Spot pools and On-Demand fallbacks to ensure availability. This ML-driven orchestration achieves sub-1% termination rates while maintaining performance consistency.
Q: What kind of visibility does nOps provide into EKS clusters?
You get real-time and historical insight into costs, utilization, and efficiency down to the pod, container, and namespace level.
Q: Can I allocate EKS costs to different teams or applications?
Yes — costs can be automatically mapped by team, environment, or product without manual tagging, supporting chargeback and showback models.
Q: Does nOps provide forecasting or anomaly detection for EKS costs?
It includes built-in forecasting and anomaly detection so you can spot cost trends, budget risks, and unexpected usage early.
Customer Stories
A Recognized Leader in Cloud Management
Advanced technology partner AWS, G2 4.8 rating, FinOps Foundation member and many more
