Learn how to run AI workloads on Kubernetes and OpenStack in 2026 with best practices for GPUs, storage, security, and hybrid cloud.
In 2026, AI workloads are becoming core infrastructure requirements, right alongside Kubernetes, storage, networking, and security. Enterprises aren’t just asking if they can run AI workloads. They’re asking:
- Where should we run them?
- How do we scale GPU-heavy workloads without breaking budgets?
- How do we avoid vendor lock-in?
- And how do we keep AI infrastructure secure, compliant, and cloud native?
For many teams, the answer is increasingly clear:
Kubernetes orchestrates the workload. OpenStack provides the infrastructure foundation.
This combination offers a powerful, open alternative to proprietary AI platforms — especially for organizations building private or hybrid clouds.
Let’s explore what’s driving this shift, and what best practices matter most when running AI workloads on OpenStack + Kubernetes in 2026.
Why AI Infrastructure Strategy Matters More Than Ever
The last few years made one thing obvious: AI is infrastructure.
Training models, running inference pipelines, deploying AI-enabled applications — all of it depends on cloud-native systems that can handle:
- GPU acceleration
- High-throughput storage
- Fast networking
- Secure multi-tenancy
- Elastic scaling
- Cost predictability
Hyperscalers offer managed AI stacks, but they come with tradeoffs:
- Rising GPU costs
- Limited workload portability
- Proprietary tooling
- Data residency constraints
- Vendor lock-in
That’s why more organizations are exploring open infrastructure for AI, especially in regulated industries like healthcare, finance, and the public sector.
The Role of Kubernetes in Cloud Native AI
Kubernetes has become the default platform for modern AI workloads because it enables:
- Portable deployment across environments
- Containerized training and inference
- Automated scaling
- Standardized CI/CD workflows
- Integration with cloud-native observability and security tooling
In short: AI teams want Kubernetes because it matches how software is built today.
But Kubernetes alone doesn’t solve everything.
AI workloads require infrastructure primitives underneath — compute, networking, storage, identity — and that’s where OpenStack plays a critical role.
Why OpenStack Still Matters for AI in 2026
OpenStack provides the building blocks needed to run AI at scale, especially in private and hybrid environments:
- On-demand virtualized GPU instances
- Multi-tenant isolation
- Software-defined networking
- Storage integration (Ceph, NVMe, object storage)
- Open APIs that avoid vendor lock-in
- Full control over data locality and compliance
When paired with Kubernetes, OpenStack becomes a flexible foundation for AI infrastructure that stays open, extensible, and enterprise-ready.
Best Practices for Running AI Workloads on OpenStack + Kubernetes
So what does it actually take to run AI workloads successfully in this stack?
Here are the key best practices teams are adopting in 2026.
1. Treat GPU Resources as a First-Class Scheduling Problem
GPUs are not just “bigger CPUs.”
They require careful scheduling, isolation, and utilization tracking.
Best practices include:
- Using Kubernetes device plugins for GPU allocation
- Configuring node pools optimized for training vs inference
- Avoiding GPU fragmentation with proper workload sizing
- Monitoring GPU utilization continuously
In OpenStack environments, teams are also adopting stronger integration between Nova scheduling and Kubernetes GPU workloads.
The goal: maximize expensive GPU resources without operational chaos.
2. Separate Training and Inference Architectures
Training workloads and inference workloads behave very differently:
Best practice: build separate infrastructure paths for each.
- Training clusters optimized for throughput
- Inference clusters optimized for responsiveness and autoscaling
OpenStack makes this easier by enabling distinct instance flavors, storage tiers, and network segmentation.
3. Use Ceph and Cloud-Native Storage Patterns for AI Data
AI workloads are storage intensive.
Datasets, checkpoints, embeddings, model artifacts — they all require:
- High throughput
- Reliable replication
- Shared access across nodes
- Object storage for long-term retention
Ceph remains one of the strongest open-source answers here, especially when integrated into OpenStack and Kubernetes environments.
Best practices include:
- Using CephFS for shared datasets
- Using object storage for model artifacts
- Ensuring fast local NVMe where needed
- Avoiding unnecessary data movement between clouds
AI is often limited not by compute, but by data gravity.
4. Build Security and Compliance Into the Platform Layer
AI workloads introduce new security risks:
- Sensitive training data exposure
- Model leakage
- Credential sprawl
- Multi-tenant GPU isolation issues
Best practices include:
- Short-lived credentials and centralized secrets management
- Strong tenant separation in OpenStack
- Network segmentation for AI pipelines
- Policy enforcement via Kubernetes admission controls
For regulated industries, OpenStack-based private AI infrastructure provides a path to compliance that public AI platforms may not.
5. Automate Everything: Day-2 Operations Matter
AI infrastructure isn’t static.
Clusters evolve constantly:
- New GPU nodes
- New models
- New frameworks
- Scaling requirements
- Security patches
The operational burden can grow quickly unless automation is built in.
Best practices include:
- Fully automated Kubernetes cluster lifecycle management
- Infrastructure-as-Code for OpenStack environments
- Zero-downtime upgrade planning
- Continuous observability for AI workloads
In 2026, the winning platforms are not the ones that launch fast, but the ones that operate cleanly at scale.
6. Design for Hybrid AI From Day One
Most organizations will not run AI in one place.
They’ll run workloads across:
- Private cloud for sensitive training
- Public cloud for burst inference
- Edge environments for low-latency AI
- Multiple regions for resilience
OpenStack + Kubernetes provides a consistent foundation for hybrid AI strategies without forcing everything into one vendor ecosystem.
Portability matters — but operational consistency matters even more.
What’s Next: AI + Cloud Native Is a KubeCon + CloudNativeCon 2026 Priority
AI infrastructure is becoming one of the biggest themes in the cloud-native ecosystem.
At KubeCon + CloudNativeCon Europe 2026, expect major discussions around:
- GPU scheduling at scale
- Kubernetes-native AI orchestration
- Open infrastructure for AI workloads
- Hybrid and sovereign AI platforms
- Security-first AI operations
As a Silver Sponsor to KubeCon + CloudNativeCone Europe, VEXXHOST is excited to be part of these conversations and to help teams build AI-ready infrastructure that stays open, scalable, and enterprise-grade.
Final Thoughts: Open AI Infrastructure Is the Future
AI workloads are reshaping how infrastructure decisions are made.
The question is no longer “Can we run AI in the cloud?”
It’s:
Can we run AI without losing control, portability, and predictability?
Kubernetes provides the orchestration layer.
OpenStack provides the infrastructure foundation.
Together, they offer an open path forward for organizations building serious AI platforms in 2026.
Want to Talk AI Infrastructure at KubeCon 2026?
If you’re exploring AI workloads on Kubernetes, private cloud GPUs, or hybrid infrastructure strategies, we’d love to connect.
Meet the VEXXHOST team at KubeCon + CloudNativeCon Europe 2026. Find us at Hall1, Booth #797.
