When public-cloud limits throttle growth, DevOps teams turn to OpenStack for cost-controlled scale, deeper observability, and GPU-ready performance. Here's why
DevOps teams move fast. But at a certain scale, public cloud stops keeping up.
Of course, the issues don't hit all at once. Initially, it's only a few delayed provisioning requests for GPU-backed nodes. Then it’s a patchwork of cost reports that don’t map to internal projects. Eventually, the team needs infrastructure that responds to real workload demands, not just what a service catalog allows.
This is the point where many teams start exploring OpenStack. Not to replace public cloud altogether, but to build a parallel environment they can tune more precisely. With OpenStack, and tools like Atmosphere, DevOps teams gain access to programmable infrastructure that mirrors their operational priorities.
Let’s walk through the specific pressure points that lead teams here.
When Public Cloud Starts Slowing You Down
Provisioning may get unpredictable
Public cloud handles most workloads well, but high-demand resources like GPUs and memory-heavy nodes aren’t always available on demand, especially in certain regions or during peak hours. And some teams end up overprovisioning just to ensure availability making it very expensive.
Telemetry gaps can emerge
Standard observability tools like CloudWatch or Stackdriver work for most metrics. But GPU memory usage, MIG partitioning stats, and kernel-level diagnostics often need extra tooling like NVIDIA DCGM, Prometheus exporters, and custom dashboards. You can wire these in, but permissions and integrations can get complicated quickly.
Cost tracking doesn’t always match real usage
Cloud cost tools exist but mapping actual resource usage like GPU-hour spend per project often requires tagging, custom metrics, and manual reconciliation. Finance teams operate on lagging data and DevOps teams have little visibility into resource efficiency.
How Atmosphere Helps DevOps Teams Regain Control
Atmosphere gives DevOps teams building blocks - compute, storage, networking, and identity. This can be configured to match real workloads like Kubernetes, CI/CD pipelines, AI training jobs, and persistent applications all benefit from infrastructure that can be shaped at the platform level.
Here’s how it works in practice.
Resource configuration at your level of control
In Hosted and On-Premise editions, teams can define custom flavors to match workload requirements, including CPU-to-memory ratios, local NVMe storage, or isolated GPU hosts. GPU passthrough, SR-IOV, and OVN overlays are supported on compatible hardware in Hosted and On-Premise deployments.
