Dana CazacuAI experiments fail quietly through GPU sprawl. See how Atmosphere helps teams control GPU usage without slowing innovation.
AI experimentation is supposed to be fast and inexpensive. Spin up a GPU, test a model, discard what doesn’t work, and repeat. Every experiment leaves an infrastructure footprint: compute left running, storage quietly accumulating, and environments that outlive their usefulness. As organizations accelerate AI adoption, these small decisions compound, turning experimentation into a significant and often opaque infrastructure cost.
That cost is no longer theoretical. Industry data shows that nearly half of enterprises waste millions of dollars annually on underutilized GPU capacity, largely due to poor visibility and governance around AI workloads. At the same time, AI-related cloud spending is growing at double-digit rates year over year, with infrastructure (compute, storage, and networking) making up a growing share of overall AI budgets. What starts as “just an experiment” can quickly become a long-lived line item no one fully owns.
The underlying issue isn’t simply the price of hardware or cloud services; it’s how AI workloads are exposed to people. Rapid experimentation doesn’t map well to ticket-based provisioning or raw cloud access, and both approaches tend to produce the same outcome: sprawl, unclear ownership, and reactive cost control.
This is why many teams are changing how AI experimentation is delivered, using platforms like Atmosphere Openstack to provide fast, self-service access to infrastructure while making ownership, lifecycle, and boundaries explicit from the moment resources are created. When experimentation starts with clarity instead of cleanup, AI velocity no longer has to come at the expense of infrastructure control.
§ Why AI Experiments Break Traditional Infrastructure Workflows
The infrastructure cost of AI experimentation rarely shows up as a single, obvious mistake. Instead, it accumulates through small, repeatable failures that are easy to miss in fast-moving environments.
Idle or underutilized GPU capacity
One of the most common is idle or underutilized GPU capacity. Teams provision GPU-backed instances for experiments that may run for hours or days, but those resources often remain active long after meaningful work has stopped. Without clear signals around ownership or lifecycle, GPUs sit idle between experiments: expensive, available, and largely invisible until costs are reviewed later.
Environment Sprawl
Another source of cost is the environment sprawl. AI experiments tend to evolve quickly, with slight variations in configuration, data access, or model versions. Without standardized templates, teams spin up one-off environments that are difficult to reproduce and even harder to clean up. Volumes, snapshots, and temporary datasets accumulate alongside compute, creating long-lived storage costs that outlast the experiments they supported.
If you want to learn more about hidden cost of cloud sprawl on OpenStack, we recommend reading this blog post.
Human Cost
There’s also a significant human cost that rarely gets measured. Infrastructure teams spend time answering ad-hoc provisioning requests, investigating unfamiliar workloads, and tracking down who owns what when budgets are exceeded. Security and compliance teams inherit environments they didn’t approve, while finance teams try to allocate costs after the fact. None of this friction appears in a cloud bill, but it directly slows experimentation and platform evolution. You can read more about this topic here.
This is where upfront structure matters. By defining how AI resources are provisioned, what configurations are available, how long they’re expected to live, and who owns them, Atmosphere helps prevent these failures before they occur. Instead of relying on cleanup campaigns and cost reviews, teams can make responsible usage the default, turning AI experimentation from a cost risk into a predictable, repeatable platform capability.
§ Why “More Automation” Alone Doesn’t Fix This
When AI infrastructure costs start to rise, the instinct is often to add more automation. While automation is essential, it primarily helps operators manage systems, but it doesn’t change how users make decisions when they provision resources.
Where automation falls short:
- Scripts automate actions but don’t teach responsibility or ownership
- APIs provide access but don’t create clarity around cost or lifecycle
- Dashboards show usage after the fact, not when decisions are made
- Cleanup jobs react to sprawl instead of preventing it
What AI experimentation actually needs:
- Cost, ownership, and lifecycle made explicit at provisioning time
- Guardrails that guide behavior without slowing experimentation
- Consistent workflows across private and public cloud environments
Deploying OpenStack with Atmosphere addresses this by providing an infrastructure platform designed to support AI and machine learning workloads directly. Atmosphere supports multiple deployment models, cloud, hosted private cloud, and on-premises, while offering advanced GPU capabilities such as SR-IOV and DPDK for low-latency networking, PCI passthrough for maximum GPU performance, customizable GPU flavors, and native Kubernetes integration. Instead of relying on ad-hoc automation layered on top of generic infrastructure, platform teams can offer AI-ready environments through consistent, repeatable workflows that work across deployment locations.
§ How Atmosphere Changes AI Experimentation Dynamics
AI experimentation fails at scale when GPU resources are either overprovisioned to avoid friction or left running because teardown is inconvenient. The goal isn’t just faster access to compute, it’s making GPU-backed experimentation easy to start, easy to adjust, and easy to end.
With Atmosphere, teams get fast, self-service access to GPU-backed infrastructure that’s designed for real AI workloads. Native Kubernetes integration allows experiments to scale up when demand increases and scale down automatically when it doesn’t, reducing the need to reserve excess GPU capacity “just in case.” For performance-sensitive workloads, features like PCI passthrough provide direct access to GPU hardware, ensuring experiments run efficiently instead of wasting cycles on abstraction overhead. If you want to learn more about this, we highly encourage reading this blog post.
Atmosphere also changes how experiments are observed and managed over time. Built on OpenStack’s event-driven architecture, Atmosphere captures and logs every infrastructure action in real time: launching instances, resizing volumes, attaching storage, or modifying networks. This makes experiment activity visible as it happens, instead of reconstructing intent later from stale dashboards or manual investigation.
On top of this event stream, Stratometrics, Atmosphere’s integrated analytics platform, provides detailed billing usage insights, making it a valuable tool for managing resource consumption in public cloud offerings. However, for monitoring and operational observability in Atmosphere, the platform leverages a robust monitoring stack built on Prometheus, Grafana, Loki, and Alertmanager. This stack ensures real-time visibility into system health, alerting, and performance metrics, enabling teams to monitor workloads, identify potential issues, and respond proactively. Together, these tools create a comprehensive solution for optimizing both cost and performance in cloud environments. For AI workloads, this means underutilized GPUs, stalled experiments, and long-running environments are easier to identify and address. Platform teams can rebalance capacity, while users can see how their experiments evolve instead of losing track of what’s still running.
Atmosphere also improves how data and environments persist across experiments. Direct integration with high-performance block storage makes it easier to reuse large datasets without duplicating resources, while flexible scaling across cloud, hosted, and on-premises deployments allows teams to match infrastructure to the phase of their work from early experimentation to sustained training or inference.
The result is not less experimentation, but more disciplined experimentation. Atmosphere doesn’t limit what teams can run, it makes GPU usage observable, adjustable, and sustainable as AI workloads evolve.
Conclusion
AI experimentation doesn’t fail because teams move too fast; it fails because infrastructure isn’t designed to support rapid iteration responsibly. GPU-heavy workloads demand platforms that make experiments easy to start, adjust, and retire without losing visibility or control. When infrastructure access is shaped upfront instead of cleaned up later, experimentation becomes sustainable rather than wasteful. Atmosphere enables this shift by aligning real AI workloads with repeatable, observable infrastructure workflows. The result is faster innovation without hidden infrastructure debt.
If you’d like to bring Atmosphere into your organization with the help of our team of experts, reach out to our sales team today!