Sedai vs. Turbonomic

Trusted by industry leaders

Crush Cloud Costs Without Compromising Uptime or Performance

30%+

Cloud cost reduction

30%+

Application performance improvement

1,000+

Hours engineering time saved

Why Leading Enterprises Choose Sedai Over Turbonomic

Turbonomic

(Cost Tool)

Rule-based, reactive automation
Focused on compute (VMs, containers)
Cost, performance, availability
Significant engineering effort to integrate
SLO-driven scaling for containers (incl. GPU/GenAI)
Limited guardrails via automation policies

(Autonomous AI Platform)

Proactive, continuous and autonomous optimization
Full-stack, incl. compute, containers, serverless, storage, data
Cost, performance, availability
OOB integration w. IaC, CI/CD, ITSM
SLO-aware optimization for full stack
Built-in safety checks before actions

K8s Only
Cost Only
Static recommendations
K8’s in-cluster cost monitoring only
N/A (no automated changes)
No (reactive alerts only)

	Sedai (Autonomous AI Platform)	Kubecost (Cost Tool)
Integrations	Out-of-the-box integrations for Compute, Data, and Storage	K8s Only
Optimization Goals	Cost, Performance, Availability	Cost Only
Approach	Autonomous & ML-Driven	Static recommendations
Infrastructure Support	Multi-Cloud - K8s, VMs, Serverless, Storage	K8’s in-cluster cost monitoring only
Safety Mechanisms	Patented AI Safety Checks + Automated Rollbacks	N/A (no automated changes)
Proactive Remediation	Yes (SLO monitoring, proactive prevention	No (reactive alerts only)

Optimization Goals

Cost, Performance & Availability — not just cost

Infrastructure Scope

K8s, VMs, ECS, Lambda, S3, RDS, and more

Autonomy Level

Autonomous AI with built-in guardrails

Flexibility

CoPilot or AutoPilot — you stay in control

Optimize everything, not just Compute

Out-of-the-box integration in minutes

Compute

Storage

Data

EKS / ECS

EC2 / Fargate

Lambda

S3 / EBS

RDS / EMR*

AKS / VMs

Blob* /
Managed Disk*

GKE / Compute Engine*

Cloud Storage* / Persistent Disk*

Dataflow

Kubernetes (all)

Databricks / Snowflake*

Compute

EKS

ECS

EC2

Fargate

Lambda

Storage

EBS

Data

RDS

EMR*

Compute

AKS

VMs

Storage

Blob*

Managed Disk*

Compute

GKE

Compute
Engine*

Storage

Cloud
Storage*

Persistent
Disk*

Data

Dataflow

Compute

Kubernetes (all)

Data

Databricks

Snowflake*

Sedai has helped us save millions of dollars by optimizing and managing our own back-end services. But most importantly, what Sedai has done very well is allow us to respond in real time when anomalies are detected.

Suresh Sangiah

SVP of Engineering, Palo Alto Networks

Suresh Sangiah

SVP of Engineering, Palo Alto Networks

Got Questions? We’ve Got You Covered.

What is Sedai’s optimization model?

Sedai uses autonomous AI agents and reinforcement learning to balance cost, performance, and availability continuously — learning and improving over time.

How does Sedai ensure safe changes in production?

Every action is vetted by rigorous safety checks and simulations; patented guardrails let you approve or veto any optimization.

Does Sedai work outside of Kubernetes?

Yes. Sedai supports Kubernetes (EKS/AKS/GKE & on‑prem), VMs, serverless (Lambda), storage (S3, EBS), databases and more.

What results can we expect?

Customers typically see 30–50%+ cost savings and 30%+ performance gains within months, with zero incidents.

How is pricing structured?

30-day free trial + production-ready POC; then paid SaaS or self-hosted tiers optimized for your scale.

How does Sedai work with IaC platforms (Terraform, AWS CloudFormation etc.)?

Sedai integrates with these IaC platforms to ensure that resource configurations remain in sync with the IaC files. You can integrate your preferred Git-hosting platforms with Sedai to connect your IaC in Sedai’s settings pages. Sedai offers a feature where it can map resources to IaC configuration files, providing flexibility in how infrastructure management needs are met. Sedai can also provide an option to issue an IaC pull request when operating in Recommend Mode, which will allow Sedai to provide recommendations for configuration changes but offload the execution of these changes to the IaC and its associated CI/CD pipeline.