Automated Kubernetes cost optimization
Monitor organization-wide and cluster-level resource spending. Automate resource allocation and scale instantly with zero downtime.
Trusted by 800+ companies globally
Key features
Maximize savings and reduce toil
Cluster autoscaler
Eliminate waste and maximize resource utilization through bin packing, pod placement scheduling, and more:
- Automatically provision the most cost-efficient compute resources
- Scale resources up and down based on real-time requirements
Workload autoscaler
Automatically rightsize workload requests up or down to ensure optimal performance and cost-effectiveness:
- Organize and scale workloads individually or in custom-defined groups
- Fine-tune the workload autoscaler according to your requirements
Commitments utilization
Unlock the full value from your commitments by boosting resource utilization:
- Use commitments across all clusters or prioritize specific clusters ones
- Automatically balance commitments and Spot Instance usage to achieve maximum savings
Spot Instance automation
Eliminate the hassle of dealing with two-minute interruptions and the challenges of managing Spot Instances:
- Use advanced Spot Instance management to balance cost and reliability
- Automatically handle Spot Instance lifecycle events, including interruptions, Spot diversity, and fallback to on-demand nodes when necessary
Spot Instance automation
Automate the entire Spot Instance lifecycle, temporarily shifting workloads to on-demand during a Spot drought.
Bin packing
Optimize resource usage by safely consolidating workloads onto fewer nodes and removing empty ones.
Pod mutations
Dynamically modify pod specifications to ensure cost-efficient scheduling and optimal resource utilization.
Rebalancer
Bring your cluster to the most optimal state either on a predefined schedule or instantly.
Memory event handling
When a pod runs out of memory, the platform immediately provisions additional resources to keep workloads stable and prevent downtime.
Advanced pod autoscaling
Scale your workloads vertically and horizontally at the same time optimizing for both performance and cost.
Setup
Get started in three steps
Learn more
Additional resources

Blog
Kubernetes Cost Optimization: Reduce Your Cloud Bill
Explore the most common Kubernetes cost traps and the steps you can take to steer clear of them.

Report
2024 Kubernetes Cost Benchmark Report
Discover K8s cost trends, optimize CPU/memory, and cut overspending with actionable tips.

Webinar
How to Reduce Your Kubernetes Costs
Discover the biggest causes of Kubernetes overspending and how to avoid them.
FAQ
Your questions, answered
Cloud cost optimization is all about reducing unnecessary cloud expenses while maximizing the efficiency of cloud services. It’s important because it helps businesses avoid waste and get better ROI from their cloud infrastructure.
Tools like AWS Cost Explorer, Azure Cost Management, Google Cloud’s Recommender provide visibility and recommendations to optimize costs. Automation solutions are a game changer that let teams seriously increase resource utilization and reduce cloud waste.
Common reasons behind waste include overprovisioning of resources, idle compute instances, inefficient data storage, excess data transfer, and misconfigured autoscaling policies.
Challenges that prevent teams from optimizing cloud costs include lack of visibility into cloud spending, ability to manage dynamic workloads, preventing overprovisioning, dealing with complex pricing models, and coordinating cost optimization efforts across multi-cloud environments.
You can optimize cloud costs by rightsizing resources, using autoscaling to provision just enough resources needed, taking advantage of cost-efficient Spot instances, removing idle resources, and optimizing storage and data transfer.
Autoscaling automatically adjusts resource capacity based on demand in real time, ensuring that you’re only paying for what you need. This prevents overprovisioning and lowers costs during low usage periods.
You can optimize your Kubernetes workloads by continuously monitoring their requirements and setting just the right requests and limits for optimal operation with no downtime caused by issues like OOM kills.
You can optimize storage costs by using the right storage tier for your data (e.g., cold or archive storage for infrequently accessed data), regularly deleting unused data, and compressing or deduplicating stored files.
Can’t find what you’re looking for?