The Kubernetes-native
alternative to Sedai
Both tools help reduce Kubernetes costs. The difference lies in the depth of the technology, the impact to resources, and most importantly, results.
Companies who slashed their Kubernetes
spend using DevZero
Sedai's limitations vs
DevZero's approach
Sedai optimizes for broad cloud coverage. DevZero optimizes deeply where your biggest spend actually lives: Kubernetes.
While Sedai spreads optimization across Lambda, ECS, RDS, and container-services, DevZero delivers significantly greater efficiency, and therefore, savings, on the infrastructure that matters most, with proactive and reactive binpacking, GPU-aware scheduling, and live workload migration.
GPU requests over time
Sedai's limitations vs DevZero's approach
Sedai optimizes broadly. DevZero optimizes deeply where your biggest spend actually lives.
Kubernetes-native, full depth
Built exclusively for Kubernetes, resource requests, limits, QoS classes, bin packing, node scheduling. Every optimization primitive available, fully leveraged.
Zero-downtime via CRIU migration
Checkpoint/Restore In Userspace (CRIU) technology live-migrates processes without pod restarts. Stateful workloads keep running while resources are right-sized in real time.
GPU-first architecture
Full NVIDIA MiG partitioning support, GPU-aware scheduling, and checkpoint/restore for training jobs on spot instances. Cut GPU costs without sacrificing utilization.
Typical savings: 40–80%
Kubernetes-native depth unlocks savings that general-purpose platforms structurally cannot reach. That's not marketing, it's the math of specialization.
Shallow Kubernetes integration
Sedai's optimizations span across multiple compute paradigms, this results in being limited to optimizing existing autoscaler configs, provided resources are already attached to autoscalers.
Pod restarts disrupt workloads
Optimization events require pod cycling, causing interruptions to stateful databases, ML training jobs, caches, and applications with long startup times - exactly when you can't afford downtime.
Limited GPU optimization
Basic GPU awareness only. No MiG partitioning, no checkpoint/restore for spot migration. AI/ML teams are left overpaying for idle GPU capacity.
Typical savings: 20–40%
Broad-scope platforms trade depth for coverage. The result is conservative savings that leave significant cost on the table.
Full Visibility.
Zero Waste.
Total Control.
CRIU live rightsizing
Pod runs, resources adjust
Intelligent bin packing
Idle node consolidation
NVIDIA MIG partitioning
GPU slice isolation
Predictive ML autoscaling
Scale before demand hits
Spot checkpoint/restore
Resume training on new node
Deep K8s primitives
QoS, PDBs, PriorityClasses
$1,200.55
32% of workloads (72)
account for ~80% of total cost
4%
13% of workloads (30)
account for ~80% of CPU usage
19%
26% of workloads (57)
account for ~80% of memory usage
0%
50% of workloads (2)
account for ~80% of GPU usage
Cost Distribution
mi-apac
$24.07
mi-earth
$24.07
mi-emea
$23.97
mi-moni
$21.08
mi-apac
qis-prece
$17.95
mi-apac
$20.90
qis-apac
$20.56
qis-preu
qis-regn
$20.52
qis-ema
qis-preu
$20.07
qis-preu
qis-nat
$20.40
gossip
gossip-int
$20.40
gossip-int
$20.36
node-back
$21.73
qis-emea
$20.23
averger
qis-ema
mi-apa
qis-mai
DevZero vs. Sedai
feature by feature
A complete breakdown across every relevant capability dimension.
Frequently asked questions
What our customers say
DevZero slashed cloud costs by 60% in 30 days, — uncovering massive waste in seconds.
Lauren Glass Mullins · CEO
With DevZero, the team is now focused on product development instead of troubleshooting infrastructure problems caused by resource constraints.
Ashish Kolhe · Head of Engineering
