Sold by: STCLab
Deployed on AWS
Wave automates Kubernetes Day 2 operations on EKS with ML-driven scaling, container rightsizing, Karpenter ops, traffic shaping, and forecasting. It replaces the manual rule-tuning you build on top of HPA and VPA.
Overview
Wave (formerly Wave Autoscale) is an intelligent workload management platform for Amazon EKS that automates Day 2 operations with ML-driven decisions instead of static thresholds. SREs and platform engineers reclaim the 60 to 70 percent of time spent on repetitive scaling and tuning, and cut cluster costs 30 to 40 percent in the process.
Six sub-brands ship together:
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Wave Autoscale: per-workload ML pod scaling (Autopilot, 2x faster than HPA), Autopilot Scheduler for time-based pre-scaling, Min/Max Recommendation for HPA replica bounds.
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Wave Sizing: Smart Sizing for continuous in-place container rightsizing (safer than VPA; pairs with Autopilot instead of conflicting).
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Wave Karpenter: production-grade Karpenter operations including a real-time Karpenter Dashboard with cost and spot tracking, Node Warmup for 10x faster cold starts, and a Spot Workload Placement webhook for per-deployment spot/on-demand split.
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Wave Flow: WASM-based priority traffic shaping on Istio that protects critical traffic during overload, plus NetFunnel virtual waiting room for traffic surges.
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Wave Insights: Cluster Resource Forecast (7 to 30 day capacity), Memory Leak Detection, Pod Scheduling Delay Detection, CPU Utilization Analysis, Idle Node Detection.
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Additional: PV auto-expansion and capacity forecast, PV cleanup, configurable webhook alerts, and a programmable REST API.
Wave runs as a Helm-deployed workload on your existing EKS infrastructure. It does not replace your CNI, control plane, or Istio.
License key required (free under 200 vCPUs cluster-wide; paid licensing above the threshold). Technical identifiers (the wave-autoscale namespace, Helm chart, and env vars) are unchanged from the Wave Autoscale era for compatibility.
Highlights
- ML scaling that learns each workload. Autopilot replaces HPA's static math formula with per-workload ML models, delivering 2x faster scaling decisions without manual threshold tuning. Pair with Autopilot Scheduler for known peaks (Black Friday, batch windows) and Min/Max Recommendation to replace replica-bound guesswork with data-driven values.
- Karpenter visibility and acceleration. Real-time Karpenter Dashboard surfaces cost, spot ratio, and NodePool analytics in one place. Node Warmup pre-provisions and pre-caches images for 10x faster cold starts (31s to 3s). Spot Workload Placement webhook splits each deployment between spot and on-demand at thresholds you control: capture spot savings without baseline-pod risk.
- Forecast capacity and predict failures. Wave Insights ML models forecast cluster resource exhaustion 7 to 30 days out, predict memory leaks hours-to-days before OOMKill, and surface per-workload pod scheduling delays without custom Prometheus rules. PV Capacity Forecast and PV Auto-Expansion prevent storage outages before they page someone.
Details
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Delivery method
Supported services
Delivery option
Helm Chart
Latest version
Operating system
Linux
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/unit |
|---|---|---|
Hourly Usage | Charges incurred on an hourly basis | $0.00 |
vCPU Over 200 | Total vCPU usage exceeding 200 across all nodes in the Kubernetes cluster | $0.004 |
Dimensions summary
Wave bills on usage, combining two dimensions. The Hourly Usage charge applies on an hourly basis for running the platform in your cluster. The vCPU Over 200 charge adds cost based on total vCPU usage that exceeds 200 across all nodes in your Kubernetes cluster. These two dimensions work together: you pay the hourly rate for ongoing operation, plus a per-unit charge that scales with cluster size once vCPU usage passes the 200 threshold. Larger clusters incur higher vCPU-based charges, while smaller clusters below the threshold pay only the hourly component.
Top-of-mind questions for buyers
The charge counts total vCPU usage across all nodes in your Kubernetes cluster. Only vCPU that exceeds 200 units is metered under this dimension. Usage at or below 200 vCPU does not trigger this charge. The count aggregates every node in the cluster, not per-workload.
You keep paying the hourly rate for running the platform. Once total cluster vCPU passes 200, the vCPU Over 200 charge applies only to the portion above 200. Usage below the threshold does not add vCPU charges. The transition follows measured usage automatically.
Both charges apply at once. The Hourly Usage charge covers ongoing platform operation regardless of size. The vCPU Over 200 charge scales with cluster size and grows as node vCPU rises. For clusters staying under 200 vCPU, only the hourly component applies.
wavek8s.com
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Delivery details
Helm Chart
Supported services: Learn more
- Amazon EKS
Helm chart
Helm charts are Kubernetes YAML manifests combined into a single package that can be installed on Kubernetes clusters. The containerized application is deployed on a cluster by running a single Helm install command to install the seller-provided Helm chart.
Version release notes
Additional details
Usage instructions
Explore documentation: https://wavek8s.com/docs/installation-guides/amazon-eks
Resources
Vendor resources
Support
Vendor support
Slack community: https://waveautoscale.slack.com Email: team@waveautoscale.com Documentation: https://wavek8s.com/docs
For installation on Amazon EKS, see https://wavek8s.com/docs/installation-guides/amazon-eks . It covers AWS Marketplace (BYOL and Metered) and GHCR install methods.
Default web console login after install: username "admin", password "waveautoscale". Change immediately from the user-management UI.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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