Cast AI
A tool that watches your Kubernetes clusters and automatically resizes, reschedules, and cost-optimizes the workloads running on them — including GPU workloads — so an engineer isn't manually guessing the right instance sizes.
🔗 Visit Cast AI
Description
Kubernetes gives teams enormous flexibility, but that flexibility comes with a real cost: someone has to decide how big each workload's resource requests should be, and getting it wrong either wastes money (oversized) or causes outages (undersized). Cast AI automates that decision-making, continuously rightsizing workloads and adjusting cluster infrastructure without a human manually tuning YAML files.
It specifically extends this automation to GPU workloads, which is increasingly relevant as more teams run AI/ML training and inference on Kubernetes and GPU costs are high enough that waste is expensive. Pricing is entirely custom — based on cluster count, GPU usage, and which products you select — with no public numbers, so getting a real cost estimate means going through a contact form. The target audience is DevOps, SRE, and FinOps teams running Kubernetes at an enterprise scale where manual optimization has stopped being feasible.
💬 Our review
The short version: Cast AI is worth evaluating if you're running Kubernetes at a scale where manual rightsizing has become impractical, and especially if GPU workloads are a meaningful part of your cloud bill — that's a more specific and currently underserved niche than general cost optimization.
Against a competitor like Sedai (broader multi-cloud RL-based optimization) or nOps (FinOps commitment automation), Cast AI's edge is being Kubernetes-native and GPU-aware specifically, rather than a general cloud cost tool. The lack of any public pricing signal — not even a starting range — makes it harder to self-qualify before reaching out, which is a real friction point compared to competitors that at least publish a pricing structure even if the final number is custom. <!-- ai-generated -->
💰 Pricing
📊 Global score
🤖 AI-enriched data
Personnalisé selon clusters, GPU et produits, aucun chiffre public
Pros
Rightsizing Kubernetes automatique et continu
Optimisation GPU dédiée
Pensé pour l'échelle entreprise
Cons
Zéro tarif public
Spécifique à Kubernetes uniquement