Audyense
CAST AI
DevOps · cast.ai
Overview
CAST AI is a Kubernetes automation platform that continuously and automatically rightsizes pods, selects cost-optimal node and spot-instance combinations, and bin-packs workloads across AWS, GCP, and Azure without manual tuning. It targets platform and DevOps teams running production Kubernetes clusters who want to cut compute costs without hand-managing autoscaling policies.
The problem CAST AI solves
Kubernetes platform and DevOps teams running production clusters on AWS, GCP, or Azure struggle to keep compute costs proportional to actual workload usage, since over-provisioned nodes, manual spot-instance management, and static autoscaling rules leave significant cloud spend on the table as clusters grow. CAST AI solves this by continuously and automatically rightsizing pods, selecting cost-optimal node and spot combinations, and bin-packing workloads without manual tuning, which reviewers report cuts Kubernetes compute costs by 30-75%.
Decision context
Use these points to test whether the product fits your operation, not just whether it has a long feature list.
- Published starting price: Custom pricing. Confirm user, usage, and feature limits for the plan you would actually buy.
- Deployment: cloud. Check security, data-residency, and access requirements for every team that will use it.
- Verified integrations include AWS, Google Cloud Platform, Microsoft Azure, Oracle Cloud, Kubernetes, Prometheus. Validate sync direction and plan limits for the connections that matter.
- This record was last checked on 7/25/2026; pricing and features can change.
How to evaluate CAST AI
A listing helps create a shortlist; a trial with the team’s real workflow decides whether the tool fits. Use this reading with the structured facts and confirm changes with the vendor.
Workflow fit
The record describes it as a fit for Platform/DevOps teams running production Kubernetes at meaningful scale, Multi-cloud or single-cloud EKS/GKE/AKS environments wanting automated rightsizing and spot management, Organizations wanting to eliminate manual capacity-planning and autoscaling toil. Check that this context matches the volume, roles, and processes your team needs it to support.
Pilot questions
- Can CAST AI complete the critical workflow without manual work outside the product?
- Do the recorded connections (AWS, Google Cloud Platform, Microsoft Azure, Oracle Cloud) support the sync direction, permissions, and volume we need?
- What user, usage, storage, support, or security limits appear after the headline starting price?
Evidence and freshness
This record was checked on 7/25/2026. That date tells you when the record was reviewed, not that the vendor has left its terms unchanged since then.
Best for
- Platform/DevOps teams running production Kubernetes at meaningful scale
- Multi-cloud or single-cloud EKS/GKE/AKS environments wanting automated rightsizing and spot management
- Organizations wanting to eliminate manual capacity-planning and autoscaling toil
Not a fit if
- Very small clusters or early-stage teams with minimal Kubernetes spend
- Teams needing a full observability/APM/log-management suite in one tool
Why it’s listed
- Automated Kubernetes cost optimization is a fast-growing DevOps niche not otherwise covered in the catalog
- Multi-cloud (AWS/GCP/Azure) autoscaling and bin-packing automation
- Strong, well-reviewed track record on G2 and AWS Marketplace
Pricing
Enterprise / Custom
Custom pricingPricing is based on cluster size and cloud spend across Kubernetes Workload, Cluster, GPU, Database, and Storage Optimization — contact CAST AI for a custom quote.
- Automated Kubernetes rightsizing & bin-packing
- Spot instance orchestration
- Multi-cloud cost visibility (AWS/GCP/Azure)
- GPU workload optimization
Features
Integrations
Security & compliance
Pros & cons
Pros
- Significant, fast cost reduction (30-75% reported across reviews)
- Strong automation that handles scaling, bin-packing, and spot-instance decisions with minimal manual tuning
- Ease of use and intuitive dashboard with quick onboarding
- Responsive customer support
Cons
- Learning curve for advanced automation policies, especially stateful workloads or non-standard cluster configs
- Reviewers want more granular workload-level cost-allocation reporting
- Documentation gaps in certain areas
- No published self-serve pricing; the current pricing page requires contacting sales for a quote
What we found
4.8/5 on G2 (~69 reviews); reviewers report 30-75% Kubernetes cost reductions and strong automation, with a learning curve for advanced policies on non-standard cluster configs.
Ratings and review counts come from public review platforms. We link to the original source and keep the underlying review text out of this profile.
User reviews
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