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 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.
Sedai optimizes broadly. DevZero optimizes deeply where your biggest spend actually lives.
Built exclusively for Kubernetes, resource requests, limits, QoS classes, bin packing, node scheduling. Every optimization primitive available, fully leveraged.
Checkpoint/Restore In Userspace (CRIU) technology live-migrates processes without pod restarts. Stateful workloads keep running while resources are right-sized in real time.
Full NVIDIA MiG partitioning support, GPU-aware scheduling, and checkpoint/restore for training jobs on spot instances. Cut GPU costs without sacrificing utilization.
Kubernetes-native depth unlocks savings that general-purpose platforms structurally cannot reach. That's not marketing, it's the math of specialization.
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.
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.
Basic GPU awareness only. No MiG partitioning, no checkpoint/restore for spot migration. AI/ML teams are left overpaying for idle GPU capacity.
Broad-scope platforms trade depth for coverage. The result is conservative savings that leave significant cost on the table.
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
A complete breakdown across every relevant capability dimension.
Lauren Glass Mullins
personality pool
“We started applying DevZero’s recommendations on day 5, and within 24 hours our daily spend dropped by 30%. By day 30, we hit 60% total savings. That’s faster ROI than any other infrastructure investment we’ve made.”
Run a free assessment to identify overprovisioned workloads, idle capacity, and your potential savings, in minutes.