Run the fleet, not the clusters

Kubernetes management
that scales good decisions

Somewhere between your fifth and fifteenth cluster, manual Kubernetes management becomes unmanageable. DevZero replaces ClickOps and reactive decision-making with policy-driven automations.
Scans your cluster locally. No signup, nothing leaves your machine.
70.8%of requested CPU never used
2,378workloads in a p90 cluster
3clouds, one control plane
Fleet
live
Production · 48
Staging · 12
In policyDriftingAt risk
11 clusters outside policy · drift found across 3 clouds
ClusterIssuesHealth
AWSprod-us-east-1744
GCPprod-eu-west-1363
Azurestaging-us-2188
AWSdev-us-1clear95

Companies who slashed their Kubernetes
spend
using DevZero

DATABAHN
Starburst
Fi
Outerbounds
Codilas
personality pool
Onnitech
OpenObserve
Parsimo
Dentira
DATABAHN
Starburst
Fi
Outerbounds
Codilas
personality pool
Onnitech
OpenObserve
Parsimo
Dentira

The fleet problem at scale

Cluster count grows linearly.Attention does not.

One cluster is a system you can hold in your head. Thirty is a black box of ad hoc resource requests and scaling policies. Unless you have an unlimited hiring budget, fleet management quietly turns into incident response. You learn a workload was sized wrong when it wakes someone up at 3am.

Workloads in one large cluster1 square = 10
94
about what a team can keep current by hand
2,378
what the cluster is actually running
25× more workloads than anyone is re-reading each week
Typical figures from live Kubernetes fleets
WorkloadRequests Based
Cost
OptimizationsHealthNamespace
ml--nar-earth-performan…
CPU: $5.3172 · Mem.: $0.7060 · GPU: $21.80
$27.8314Not OptimizedUpdatesearch-aws-ml…Optimize
ml--nar-earth-staging-u…
CPU: $5.5440 · Mem.: $0.3409 · GPU: $15.8C
$21.6873Not OptimizedUpdatesearch-aws-ml…Optimize
ml--nar-mars-performan…
CPU: $4.7951 · Mem.: $0.6284 · GPU: $16.32
$21.7511Not OptimizedUpdatesearch-aws-ml…Optimize
ml--mars-staging-use1
CPU: $5.4751 · Mem.: $0.3218 · GPU: $13.65
$19.4502Not OptimizedUpdatesearch-aws-ml…Optimize
ml--mercury-staging-use1
CPU: $8.4314 · Mem.: $0.6993 · GPU: $0.941
$10.0718Not OptimizedUpdatesearch-aws-ml…Optimize
nuwa-embedding-servic…
CPU: $2.4192 · Mem.: $0.9325 · GPU: $0.00
$3.3517Not OptimizedUpdatesearch-aws-ml…Optimize
ml--apac-mars-perform…
CPU: $4.0049 · Mem.: $0.5248 · GPU: $20.15
$24.6867Not OptimizedUpdatesearch-aws-ml…Optimize
ml--earth-performance-…
CPU: $4.0644 · Mem.: $0.5397 · GPU: $20.17
$24.7751Not OptimizedUpdatesearch-aws-ml…Optimize

What autonomous management means

Anyone can hand you a list.Fewer will act on it.

Plenty of tools can flag problems in your Kubernetes infrastructure. DevZero address them autonomously. It watches what each workload uses and rightsizes without restarts, upholding your standards for performance and reliability.

Control surface

Policies and operators,not a console to babysit

DevZero generates policies instead of more things to click, approve, and monitor. Customize by namespace, label, workload kind or name pattern. Set floors and ceilings on requests, which percentile to target, how much headroom to leave, and how fast to scale anything down. DevZero maintains your boundaries.

The fleet adapts
Most specific policy wins · precedence is always clear
Priority1Nameapi-server
WINS
Priority2Labelapi-server
2nd match
Priority3Namespaceproduction
3rd match
Priority4Defaultfallback
last resort
optimization mode per namespace
mode: conservative
production
applies cautiously, extra headroom
mode: aggressive
dev / staging
applies quickly, tighter margins

Built for the team that owns every cluster

EKS
841
pods managed
GKE
519
pods managed
AKS
302
pods managed
On-prem
178
pods managed
Single tenant1,840
pods · 4 clusters
$41kMonthly Savings
3Cloud Providers
4Clusters
1Tenant

DevZero runs one policy set across EKS, GKE, AKS, OCI, and on-prem. You get consistency and cost visibility by team, namespace and workload owner instead of by console.

cgroup v2 · live
CPU throttle
78%
Mem PSI stall
43ms
OOM events
0
sub-second
prometheus · 15s avg
CPU usage
38%
Mem usage
51%
Throttle visible
averaged out

P99 latency spike · api-server-7f6d9

throttle_pct=0.78 · caught in 180ms window

Detected

See precisely which workloads use what resources, not a model of what they might be using. Get the data to begin making policy decisions within 24 hours.

DEVZERO

cpu.request: 2000m → 680m · conf=0.94
Triggered

SLACK · #PLATFORM-RIGHTSIZING

Advisory mode · saves $2.84/hr
Posted

GITHUB · HELM-VALUES PATCH

resources.requests.cpu: "680m"
PR open

Nobody needs another console they forget to open. Our integration patches workloads directly. Alerting keeps you updated when critical workloads are modified.

Fleet management with and without automation

Capability
DevZero
Manual / GUI-driven
See cost and utilization per cluster
Single view across EKS, GKE, AKS and on-prem
Rightsize workloads without a human in the loop
Apply changes without restarting pods
Enforce guardrails as policy, not convention
Keep working as cluster count grows
Consolidate nodes after rightsizing

What our customers say

Databahn logo

We were essentially able to reduce the cost of that cluster by about 75%. On AWS, DevZero demonstrated they could achieve significantly higher savings than we initially thought possible.

Mihir Nair

Mihir Nair

Head of Architecture, Databahn

Frequently asked questions

Your fleet already knows what's wrong with it.Point something at it and find out.

The agent installs read-only in about a minute and gives you the full picture of your fleet inside 24 hours. It gets no write access until you say so.