DevZero

Engineering teams overprovision Kubernetes clusters to avoid the 3am outage call, and as applications scale that safety margin becomes expensive: the page cites an example team with a $10M compute bill where $5M goes to idle memory, CPUs, and GPUs. At the same time, traffic to LLM providers is growing uncontrollably, and teams can't easily dictate which model gets used for which task without pushback from developers, so expensive models get used for mundane requests. Existing autoscaling approaches don't solve this because teams keep warm resources idle 'just in case' rather than risk downtime.

DevZero installs a lightweight operator (under 45 seconds) that profiles every Kubernetes workload, then uses context-aware schedulers to binpack pods onto the most cost-effective nodes across AWS, Azure, GCP, Oracle Cloud, OpenShift, and on-prem infrastructure. It rightsizes CPU, memory, and GPU provisioning in real time and uses checkpoint-restore to live-migrate workloads without restarts during demand spikes or AZ outages, so teams don't need idle warm capacity. It also monitors 3,000+ instance types, 69K+ price points, 23 GPU models, and 80+ regions to find the lowest-cost instance that still meets performance needs. A separate beta product, Optimize Inference, measures LLM traffic and spend by team or workflow, simulates rerouting for cost/latency/reliability, and includes a shadow cache for repeat prompts, an evaluation lab for comparing models, and failover when a provider is rate-limited or down.