Autonomously optimize compute and inference
DevZero profiles, schedules, and rightsizes Kubernetes workloads with zero restarts. We solve for uptime anxiety and runaway infrastructure and AI costs.
Current Spend
$576,542/mo
Optimized Spend
$176,542/mo
Annual Savings
$4.8M/yr
Trusted by high-growth engineering teams
40-60
hrs/wk recovered
75% compute savings on AWS
60% compute savings on Azure
89%
less overprovisioning
67% compute savings on AWS
85%
compute reclaimed
60% compute savings on AWS
85%
less cluster sprawl
42% compute savings on AWS and Azure
THE SITUATION
Uptime anxiety is eating your margins
Your engineering team overprovisions Kubernetes clusters to prevent crashes because no one wants a call at 3 am. However, as your applications scale, the cost of that overprovisioning becomes brutal. An average team with a $10M compute bill spends $5M on memory, CPUs, and GPUs they didn't need.
Meanwhile, your traffic to LLM providers is rising out of control. You can't dictate who can use what model for which thing (because there'll be a revolt) but pinging powerful models for mundane requests makes no sense. It's like tenderizing steak with a sledgehammer.
$10M
Compute bill
$5M
Spent on idle memory,
CPUs, and GPUs
Cost Overview
Projected Monthly Cost
Actual Usage Monthly Cost
Period Cost
| Cluster① | CPU Requests | Memory Requests | |
|---|---|---|---|
 production Connected Jun 19, 2025 | 21.13 cores▼19% Utilization: 24% | 71.18 GiB▼29% Utilization: 21% |
OPTIMIZE COMPUTE
Rightsize without tradeoffs
Cutting the cloud bill isn't worth it if the result is downtime. So, DevZero builds profiles for every workload. From there, our context-aware schedulers pick and binpack the most cost-effective node for every pod. Then, we rightsize workloads in real time, adjusting CPU, memory, and GPU provisioning for further reactive binpacking.
When demand spikes or there's an AZ outage, DevZero's checkpoint-restore enables instant live migration without restarts. Clients don't need warm resources waiting idly, just in case.
See the savings before you commit.
OPTIMIZE INFERENCE (BETA) ✨
Use the right LLM (or none) every time
Measure traffic and spending with all your LLM providers, down to the team, product, and workflow. We'll simulate how to reroute that traffic to optimize for cost, latency, and reliability.
Flip a switch to implement the savings autonomously. We include a shadow cache for repeat prompts, an evaluation lab for comparing models, and a failover system to reroute traffic when an LLM is rate-limited or down.
See the overspending and model optimizations before you commit.
CLOUD AGNOSTIC
Wherever you work, we'll optimize
DevZero doesn't play favorites. We work seamlessly across AWS, Google Cloud, Azure, Oracle Cloud, OpenShift, and on-prem infrastructure. Wherever Kubernetes is in charge, we can plug in and optimize.
RUN THE NUMBERS
Verify then trust
Install our operator in less than 45 seconds. We'll monitor your compute and identify savings opportunities. See what we would do, and when you're ready, let 'er rip.
<45s
installation time via lightweight operator
$0
upfront cost or initial configuration required
24 hrs
until first active savings insights populate
30-60%
average compute bill reduction in two weeks
100%
visibility into idle memory, CPU, and GPU waste
1 click
deployment to execute optimizations
Pricing is power
DevZero doesn't just pick any node for your workload. We pick the lowest-cost instances that maintain workload performance by monitoring 3,000+ instance types, 69K+ price points, 23 GPU models, across 80+ regions spanning AWS, Azure, GCP, OCI, and OpenShift.
Best price found
$0.031/hr
AWSus-east-1$0.042/hr
Azureeastus$0.038/hr
GCPus-central1$0.031/hr
OCIus-ashburn$0.045/hr3,000+
instance types
69K+
price points
80+
regions
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
