DevZero has launched an autonomous infrastructure optimization platform that reins in the exploding costs of compute and AI inference while preserving what matters most to engineers: reliability and performance. The platform optimizes Kubernetes at the cluster, node, and workload levels, and, unlike existing solutions like ScaleOps and Scale.ai, DevZero has released a checkpoint-restore capability that instantly migrates Kubernetes workloads without restarts. It means that DevZero not only reduces compute bills by 30% to 60% but also enables infrastructure to better handle the unexpected.
“During a recent availability zone outage, DevZero transparently migrated our workloads live without requiring a single restart or operational intervention from our team,” said Mihir Nair, Head of Architecture at DataBahn. “That level of resiliency gave us the confidence to push infrastructure optimization much more aggressively at DataBahn. We’re now partnering closely to extend that operational intelligence further with Kubernetes observability for outbound network traffic optimization, reduced data transfer costs, and continuous security posture monitoring. The combination of autonomous resiliency, operational visibility, and continuous optimization is fundamentally changing how modern AI and data platforms should operate at scale.”
At its core, DevZero solves for uptime anxiety. Engineering teams overprovision CPUs, memory, and GPUs to ensure that their applications and inference workloads never crash, but with scale, that overprovisioning becomes costly. The Cloud Native Computing Foundation’s Annual Cloud Native Survey found that 66% of organizations hosting generative AI models use Kubernetes to manage some or all of their inference workloads, and according to the Datadog State of Cloud Costs, 83% of container costs go to idle resources, while 54% of those are from overprovisioned cluster infrastructure. DevZero’s average client overspent on compute by 53% before adopting the platform.
While infrastructure teams do try to rightsize workloads manually, that process is labor-intensive and doesn’t scale. At the same time, teams are justifiably nervous to hand control over to autonomous systems that, when unsupervised, could under-provision compute. Thus, while compute optimization is widely promised by vendors, it’s rarely implemented.
“Infrastructure teams are hesitant to let anything manage compute autonomously because that usually comes with tradeoffs. Cutting the cloud bill isn’t worth it if the result is downtime,” said Debo Ray, CEO and co-founder of DevZero. “We built DevZero to eliminate the tradeoffs between performance, reliability, and cost. This is a platform for engineers who want autonomous optimization they can trust at 3 am.”
DevZero aims to build trust in autonomous infrastructure optimization with a system that expects the unexpected. Its profiler continuously monitors clusters, nodes, and individual workloads to build statistical models of resource demand. From there, DevZero’s context-aware scheduling and autoscaling layer places workloads efficiently and provisions cost-effective capacity using real-time data across 3,000+ instance types, 69K+ price points, 23 GPU models, and 80+ regions spanning AWS, Azure, GCP, OCI, and OpenShift. Then, DevZero rightsizes workloads in real time, adjusting CPU, memory, and GPU provisioning to reality. When demand spikes or there’s an availability zone outage, for instance, DevZero’s checkpoint-restore enables instant live migration without restarts. Clients don’t need warm resources waiting idly, just in case.
