Zesty Kubernetes Optimization Platform
Zesty helps DevOps and platform teams manage Kubernetes costs on AWS and Azure. It is designed for organizations looking to reduce cloud waste through automated scaling and commitment management.
At a glance
- Category
- Operations
- Best for
- DevOps engineers, Platform teams, Organizations using Kubernetes on AWS or Azure
- Pricing
- Pricing consists of a base fee plus a fee per CPU or storage managed. The Commitment Manager uses a success-based model based on a percentage of savings.
- Key use cases
- Automated Cloud Cost Reduction, Savings Plan Management, Handling Traffic Spikes, Infrastructure Visibility
- Integrations
- Karpenter, Cluster Autoscaler, HPA, KEDA, AWS
- Official website
- zesty.co/
Zesty is an optimization platform for Kubernetes environments. It is designed to reduce cloud spend by coordinating resource allocation across compute, storage, and financial commitments in a single system.
The tool is built for DevOps engineers and platform teams managing Kubernetes on AWS or Azure. It helps teams move away from manual resource tuning and overprovisioning by using AI-powered demand forecasting to adjust resources.
Key capabilities include unifying horizontal and vertical scaling, rightsizing persistent volumes, and managing cloud Savings Plans. The platform is designed to help maintain application performance and SLAs while reducing idle capacity.
Buyers should confirm their specific Kubernetes setup, as headroom reduction features are designed to work with Karpenter and Cluster Autoscaler.
Key Features
Unifies horizontal and vertical pod scaling in real time to align resource requests with workload behavior.
Automates the purchase and management of micro-Savings Plans to increase coverage and reduce EC2 costs.
Automatically rightsizes persistent volumes to match workload needs and reduce spend on idle storage.
Supports scaling with Karpenter and Cluster Autoscaler to reduce the need for large CPU buffers.
Analyzes historical and real-time utilization patterns to predict workload changes and adjust resources.
Provides workload-level visibility into usage and costs with data-driven savings recommendations.
Use Cases
Reducing waste across compute and storage by adjusting resource allocations based on demand.
Managing cloud commitments using micro-purchases to maintain flexibility as usage patterns shift.
Using predictive scaling and accelerated boot times to help maintain availability during usage surges.
Monitoring resource usage at the workload level to identify spending inefficiencies.
Best For
Integrations
Pricing
Pricing consists of a base fee plus a fee per CPU or storage managed. The Commitment Manager uses a success-based model based on a percentage of savings.
FAQ
Zesty charges a base fee plus a fee based on the CPU or storage capacity managed after optimization. The Commitment Manager is billed as a percentage of the savings achieved.
Yes, an agent with read-only permissions is required for insights. Automated features like headroom reduction require an additional agent with permissions to apply changes and create nodes.
The platform is designed to maintain performance and SLAs by monitoring for events like throttling or OOM and ensuring resources are available when needed.
Measurable savings may be visible as early as one hour after a recommendation is activated, though initial recommendations typically take several days to generate.
Source category: Operations
Source subcategory: Kubernetes Management
