DevZero vs Sedai

Sedai does not read application code and models each workload from external monitoring metrics only.

Request traffic comes from connected APM tools, including Istio mesh metrics read through Datadog or Prometheus.

Spot recommendations cover ECS on Fargate today, and Spot recommendations for EKS on EC2 are listed as coming soon.

Classifies workloads as CPU-bound, memory-bound, general-purpose, or sensitive, packs compatible pods on fitting nodes, and supports opt-in by namespace, workload, or node pool.

Smart Scheduler deschedules pods to free nodes, and Sedai documents no checkpoint and restore of running processes.

Sets baseline p95 and p99 latency and error-rate SLOs per workload over 30 days, and grades each release against earlier ones with Release Intelligence scorecards.

Shows current spend, estimated savings, and realized savings per resource on the Opportunities and Savings pages; the docs describe no shared-cost allocation model.

The Amazon EKS page lists workload cost attribution across compute, GPU, storage, and network. The docs do not describe how network spend is allocated to workloads.

The Amazon EKS page lists workload cost attribution that includes storage. The docs do not describe how volume costs are mapped back to individual workloads.

Reads AWS and Azure billing exports and recommends Savings Plans purchases; the EKS page also lists Reserved Instances. Google committed use discounts are not documented.

Sets replica counts and, for supported workloads, picks the HPA scaling metric and threshold based on whether the workload is CPU-bound or memory-bound.

Models traffic seasonality from monitoring data to size replicas and pick execution windows; the docs do not describe scaling ahead of forecast traffic.

Sedai does not read application code, so scaling uses CPU, memory, traffic, and latency metrics without runtime-specific tuning.

Sizes CPU, memory, and replicas from each workload's own behavior model, so the same workload can get different settings on different clusters and hardware.

The Improve performance goal targets the lowest latency, and cost goals cap the allowed latency increase as a percentage; signals come from the connected APM tool.

Recommends node pool groups and machine types from a SKU database; Copilot execution covers Cluster Autoscaler on GKE only, with AWS, Azure, and Karpenter on the roadmap.

Migrates EKS managed node groups to Karpenter with pre-flight checks and automatic rollback, and feeds right-sized pod requests to it; Karpenter node execution is on the roadmap.

Cluster Constraints set a minimum node count per node group and a maximum across all groups, and scale-down schedules shrink clusters during set time windows.

Groups workloads by resource affinity across the whole cluster and recommends node pools and SKUs that fit them; Smart Scheduler then places pods by profile.

Detects errors, out-of-memory, CPU exhaustion, and timeouts, and alerts through Slack, PagerDuty, Teams, Jira, or webhooks; restart and throttling alerts are not listed.

No free tier. A free read-only cost analysis covers one or two cloud environments, takes about 30 minutes to set up, and reports results in about a week.