CAST AI
Application Performance and Kubernetes Automation. On autopilot.
By CAST AI Group Inc. · HQ Miami, US · 4.8/5 Value-Position score (estimate)
Positioning guardrails
Best for
- Platform, SRE and FinOps teams running Kubernetes on EKS, AKS, GKE, OpenShift or on-prem clusters who want rightsizing and node provisioning automated
- Companies with heavy Kubernetes overspend that want Spot automation, bin packing and committed-use efficiency without manual capacity planning
- Teams running AI/ML or GPU workloads on Kubernetes that need GPU optimization and interruption prediction
- Engineering organisations that want hands-off optimization without rewriting YAML or hand-managing node pools
- Multi-cluster estates (5+ clusters) spread across clouds or hybrid environments
Ideal size: 50–5,000+ employees people · Cloud-native scale-up or enterprise with a dedicated platform/SRE team
Not for
- Workloads that do not run on Kubernetes (plain VMs, serverless-only, traditional data centres)
- Very small teams with no platform, DevOps or SRE function to onboard and govern automated changes
- Buyers who require published, self-serve list pricing before speaking to sales
- Teams that want a purely advisory cost dashboard rather than automated cluster changes
- Companies seeking a general cloud FinOps tool that also covers non-Kubernetes spend
Value metrics scorecard
Time-to-Value
Under a day (connect in minutes)
~1 days to first production value
Total Cost of Ownership
On request
Custom quote; price depends on environment specifics such as cluster count and GPU usage. Free trial available; no public list price.
Implementation Friction
2/5
Engineering + admin effort required
Value-Position score
out of 5 · model estimate
Full cost breakdown
Mandatory implementation fee
None
Seat tiers
Not published. Quote form scopes by number of clusters (1-4, 5-10, 10+) and GPU use.
Add-on costs
- Separate product modules quoted individually: Workload Optimization, Cluster Optimization, Karpenter, GPU Optimization (OMNI), Database Optimization, Storage Optimization, Coding Platform (Kimchi)
Company & support
Who is behind CAST AI, and how your team gets help once it is live.
Company
- Founded
- Not recorded
- Headquarters
- Miami, US
How you get support
- PhoneNot listed
- EmailPlan not stated
- Live chatNot listed
- Support portal / ticketsNot listed
- Community forumPlan not stated
- Help centre / docsAll plans
- Dedicated account managerNot listed
- In person / on-siteNot listed
- Hours
- Not recorded
- Response time
- Not stated
Support email and an open Slack community are offered from the docs page, alongside self-serve documentation. No support hours, SLA or response time is published.
“Not listed” means the vendor’s public pages don’t mention that channel, not that it is unavailable. Ask about it during evaluation.
Market position
Where CAST AI sits against its closest alternatives. Pick any two of cost, speed, friction and buyer score, and up to 9 companies to compare.
Quadrant view
Typical annual cost × Time-to-value
The lines cross at the median of the solutions shown, so about half sit on each side of each line.
Companies on the chart 6 / 10
- CAST AI
- ProsperOps
- ExpenseIn
- Usage.ai
- Xeneta
- Zesty
Add or change companies
Up to 10 companies including CAST AI. Listed closest first.
Stack fit signal
Compatibility with standard B2B ecosystems.
No supported MCP path today, so it cannot be driven from an AI client.
AI & MCP readiness
What CAST AI ships in AI, and what it asks of your ecosystem.
AI features shipped
Vendor describes 'agentic runbooks' that remediate drift, image issues and policy violations with approval workflows, plus a Cast Engine predictive model trained on data from thousands of clusters that forecasts Spot interruptions up to 30 minutes ahead and rightsizes pods at millicore level. No AI model providers or training-data terms are stated on the reviewed pages.
Your data & models
- Trains on your data
- Not recorded — ask the vendor
- Runs on
- Not recorded
- AI pricing
- Not recorded
In your ecosystem
- AI connection
- Not supported
- Model key
- Not recorded
- AI usage audit
- Not recorded
Compliance attestations
* IAPP AIGP certifies individuals, not products. It means named staff hold the credential — not that the platform does.
Bottom line
CAST AI is a Kubernetes optimization and automation platform that turns workload, infrastructure, cost and SLO signals into automated actions: workload rightsizing, node provisioning, Spot and GPU optimization, and agentic runbooks. It connects to EKS, AKS, GKE, OpenShift and on-prem clusters in minutes with no infrastructure changes, starts read-only, and is used by 2,100+ companies. Pricing is a custom quote based on environment, with a free trial; published case studies cite 40–80% cloud cost reductions.
Frequently asked questions
How is CAST AI priced?
The pricing page states that pricing depends on environment-specific factors and that a custom quote is required (the form asks about cluster count and GPU use). No public list price is published, a free trial is offered, and no mandatory implementation fee is stated.
How long does implementation take?
Cast AI says clusters connect in minutes with zero changes to EKS, AKS, GKE or on-prem clusters, starting in read-only mode. Connections can be made via castctl CLI, the console (with agentless Cloud Connect discovery), Terraform, Helm or Pulumi, and workloads are observed before automated changes are applied.
What savings have customers reported?
Vendor-published case studies quote 40–70% (Akamai), 45%+ (Altruist), 50–80% (Banking Circle), 70% (Wio Bank), 80% (NielsenIQ) and 30–40% for already-optimized estates. These are vendor-published figures, so treat them as directional and validate against your own baseline.
Does CAST AI replace our existing autoscalers or Karpenter?
Cast AI positions itself as extending Karpenter with workload-aware decisions rather than requiring a rewrite; it also automates HPA/VPA-style rightsizing, bin packing, node provisioning and Spot interruption prediction, with approval workflows for agentic runbook actions.
What support and documentation are available?
The docs site offers self-serve documentation and getting-started guides, an email route to the support team, and an open Slack community for technical discussion. No support hours, SLA or response-time commitment is published on the pages reviewed.