Cloud bills creep up quietly — an over-provisioned node pool here, an idle GPU there — and by the time someone notices, the fix means weeks of manual rightsizing that nobody has time for. A newer wave of tools tries to automate that work away entirely, continuously resizing and rebalancing your infrastructure instead of waiting for a quarterly cost review. Cast AI and Sedai both do this, but from different angles: one goes deep on Kubernetes specifically, the other applies reinforcement learning across your whole multi-cloud footprint.
Cast AI
Cast AI focuses squarely on Kubernetes: it continuously rightsizes clusters, automatically adjusting compute to match real usage instead of the padded requests most teams set by habit. It also has dedicated GPU optimization, which matters if AI/ML workloads are a growing chunk of your cluster spend.
For who: DevOps, SRE, and FinOps teams running Kubernetes at enterprise scale who want automatic, continuous rightsizing rather than a one-time audit.
Price: quote-based, customized to clusters, GPU usage, and products used — no public pricing figures.
- Strengths: automatic, continuous Kubernetes rightsizing instead of periodic manual tuning; dedicated GPU-specific optimization for AI/ML clusters; built with enterprise-scale deployments in mind
- Limits: zero public pricing, so you can't estimate cost without a sales conversation; Kubernetes-specific — it won't touch non-K8s cloud spend the way a broader FinOps tool would
Sedai
Sedai takes a broader approach: it uses reinforcement learning to continuously optimize cost and performance across AWS, Azure, and GCP at once, not just inside Kubernetes. The pitch is that it learns your workload's actual behavior over time and adjusts autonomously, rather than applying static rules.
For who: SRE, platform engineering, and DevOps teams managing multi-cloud infrastructure at scale who want one system watching cost and performance across providers.
Price: based on your cloud environment and usage, with a full 30-day free trial to test it before committing.
- Strengths: reinforcement learning that adapts to real workload behavior over time rather than static thresholds; genuinely multi-cloud (AWS, Azure, GCP) instead of Kubernetes-only; a full 30-day free trial, rare in this category
- Limits: no public pricing either, so real cost still requires a sales conversation; letting an autonomous system make production infrastructure decisions requires a real trust threshold many teams aren't ready to cross immediately
Side-by-side
| Cast AI | Sedai | |
|---|---|---|
| Entry price | Quote-based, no public figures | Usage-based, 30-day free trial |
| Core scope | Kubernetes rightsizing + GPU optimization | Multi-cloud (AWS/Azure/GCP) via reinforcement learning |
| Standout feature | Dedicated GPU cost optimization | Autonomous, learning-based optimization across clouds |
| Best fit | Enterprise teams deep in Kubernetes, incl. GPU clusters | Teams wanting one system across multiple cloud providers |
Pick Cast AI if your cost problem is specifically inside Kubernetes — including GPU-heavy AI/ML workloads — and you want deep, continuous rightsizing rather than a general multi-cloud tool. Pick Sedai if your infrastructure spans multiple cloud providers and you want one autonomous system optimizing across all of them, and you're willing to try the 30-day trial before trusting it with production decisions. If neither pricing model works for you, nOps is worth a look too — it takes a more transparent Share-of-Savings approach, automating Reserved Instance and Savings Plan commitments rather than making live infrastructure changes.