Sedai
A cloud management tool that watches how your applications actually behave and adjusts resources — scaling, costs, availability — on its own using reinforcement learning, instead of an engineer setting static rules that go stale.
🔗 Visit Sedai
Description
Most cloud cost and performance tuning is done with rules someone set once and forgot about: "scale up at 80% CPU," fixed instance sizes, manual reserved instance purchases. Traffic patterns change, but the rules usually don't. Sedai's pitch is to replace that static approach with an autonomous system that continuously learns from actual application behavior and adjusts resources in real time.
Technically, it uses reinforcement learning models trained on your specific environment's telemetry to make scaling and cost decisions across AWS, Azure, and Google Cloud, aiming to reduce cost while maintaining or improving availability — without an engineer manually tuning thresholds. There's no public pricing (it's usage- and environment-based, quoted individually), but a 30-day free trial with full platform access lets teams validate the savings against their own workload before committing. It's aimed at SRE, platform engineering, and DevOps teams managing non-trivial multi-cloud footprints.
💬 Our review
The short version: Sedai is worth trialing if your cloud bill is large enough that a few percentage points of autonomous optimization meaningfully move the needle, and you're tired of manually re-tuning scaling rules every time traffic patterns shift.
What differentiates it from a rules-based autoscaler or a manual FinOps process (like Apptio Cloudability or nOps) is the reinforcement-learning-driven decision-making — it's meant to adapt continuously rather than needing someone to periodically revisit thresholds. That said, "autonomous" tools asking to make live production scaling decisions require real trust, and the honest way to build that is the 30-day trial rather than taking the pitch at face value — watch closely during that window rather than handing over full autonomy on day one. Without public pricing, ROI math has to happen during the trial itself. <!-- ai-generated -->
💰 Pricing
📊 Global score
🤖 AI-enriched data
Basé sur l'environnement cloud et l'usage, essai gratuit 30 jours
Pros
Optimisation continue par reinforcement learning
Multi-cloud (AWS/Azure/GCP)
Essai gratuit complet 30 jours
Cons
Pas de tarif public
Confiance requise pour l'autonomie en prod