Great news for platform teams: CloudCasa has just achieved a certification for SUSE AI, giving you a proven path to protect your local AI workloads with the same rigor you apply to your databases and applications. If you’re running large language models on Kubernetes and wondering what actually needs backup – and how to do it without scripting yourself into a corner – this guide is for you.
The Sovereignty Play: Why Running LLMs Locally Changes Everything
When organizations talk about “AI sovereignty,” they mean control in three concrete ways:
➢ Your training data, embeddings, and prompt histories stay within your infrastructure. There is no border crossings, vendor handoffs, or compliance headaches. When embeddings live three racks away instead of three continents away, response times improve noticeably.
➢ You control model upgrades, GPU allocation, and request routing. No surprise deprecations, inconvenient maintenance windows, or mid-quarter pricing changes.
➢ You can prove where data lives, who accessed it, and when – without complex explanations for auditors.
Why SUSE AI Makes Local LLMs Practical
SUSE AI is built to run natively on Kubernetes, which means it plays nicely with the tools you already know – RKE2, SUSE Rancher Prime, SUSE Observability. Your AI / LLM stack becomes just another workload in a platform you’ve already secured, observed, and learned to upgrade confidently.
Installing SUSE AI is a straightforward process involving a Helm deployment onto your cluster. (even better with SUSE Rancher Prime, which provides a clear overview of the health status of all pods). This eliminates the need for complex vendor-specific orchestration and unfamiliar operational models.
Performance-wise – it is crucial to keep embeddings and vectors close to your application and on storage that you control. By doing so, you can eliminate tail latency, ensuring that users experience optimal performance and reducing frustration.
What Actually Needs Protection in Your SUSE AI Stack
This is where many teams forget about the persistent data that can’t be rolled out from git. They back up the YAML definitions but forget the expensive, time-consuming stuff those definitions create. Let’s fix that.
1. Vector Database Data (The Memory Your LLMs Rely On)
Your vector database is the retrieval brain of any RAG (Retrieval-Augmented Generation) system.
Lose the embeddings or their index, and your model gets amnesia.
Worse: recomputing embeddings over terabytes of documents can take days and burn real money in GPU hours. Protecting the Persistent Volumes backing your vector database isn’t optional – it’s the difference between a 15-minute restore and a multi-day rebuild that costs thousands in compute.
2. Training and Fine-Tuning Artifacts (Time Capsules of Expensive Compute)
Checkpoints, adapter weights (LoRA, QLoRA), preprocessed datasets, feature stores – these are the outputs of training runs that might have cost you thousands or tens of thousands in GPU time.
If you only back up the definitions but not the actual artifacts, you’re setting yourself up to rebuild computational grids for no reason. Your team will spend days reproducing work that could have been restored in minutes.
3. Inference State and Application Scaffolding
Prompt templates, routing rules, rate-limit configurations, model selection logic—these often live as ConfigMaps and Secrets. They look small and insignificant until you need the exact version that eliminated a production regression two weeks ago.
Without these, you’re not just restoring data – you’re debugging why your restored / rebuilt system behaves differently than the one that was working fine yesterday.
4. Observability Trails You Actually Rely On
Not all logs are created equal. Some are noise. Some are compliance requirements. Some are the difference between diagnosing an incident in 10 minutes versus 10 hours.
Decide which PV-backed logs you’d rather restore than regenerate from scratch, and make sure they’re in scope.
Bottom line: Protect both the blueprint (Kubernetes objects) and the house (Persistent Volumes). SUSE AI deploys cleanly via Helm and surfaces its running components in Rancher, making it simple to identify exactly which namespaces and volumes must be included.
Why CloudCasaIs the Right Backup Control Plane
CloudCasa gives you something better than a bunch of scripts:
A control plane you run – perfect for data-residency requirements and network-boundary constraints. Your auditors and security team stay happy because everything stays inside your perimeter.
Agent-based discovery – CloudCasa understands Kubernetes natively. It knows about namespaces, Persistent Volumes, and VolumeSnapshots. You don’t translate between storage concepts; you work with Kubernetes primitives. You get visibility and restorability down to a file-level.
Explicit job results and coverage metrics – You get clear answers: “Are 100% of my PVs protected?” If the answer is no, you know exactly which volume or driver issue to fix. No guessing.
AI Workload needs mobility? – You started on a small cluster and need to move it onto a bigger box?
✅ Migration (including resource-translation mechanisms) is built-into CloudCasa ofering, making sure that you can not only restrore cross-cluster or cross-infrastructure – but migrate your workloads the same way with no problems at all.
Consistent day-2 operations – Protect dev environments on CloudCasa SaaS if you want speed. Run production on self-hosted for sovereignty. Your operators use the same workflow, same UI, same operational patterns. They don’t need to learn two mental models.
And critically: it’s proven. CloudCasa’s new certification for SUSE AI means this isn’t experimentalit’s a validated approach that works.
Your Compact, Runnable Checklist
✅ Install SUSE AI via Helm; confirm pods are Ready
✅ Verify PVs exist and are Bound in Rancher
✅ Register cluster in CloudCasa; install agent with provided kubectl apply
✅ Wait for “Established” connection status
✅ Create backup job targeting SUSE AI namespaces with PVs included
✅ Run first backup and review Activity → Jobs for success
✅ Confirm 100% PV coverage
✅ Add schedules matching your RPO/RTO requirements
✅ Drill a small restore every sprint; document friction and fix it
Final Word: Make Resilience Routine
The promise of local LLMs is control. The price is responsibility.
SUSE AI gives you a Kubernetes-native way to run models where you want them, using tools and patterns you already understand. CloudCasa’s new certification for SUSE AI gives you a proven, auditable way to protect the pieces that matter – vector memory, model artifacts, inference configurations, and the scaffolding that keeps everything working together.
Do the boring things well: wire snapshots properly, watch coverage metrics, rehearse restores regularly. Then when someone ships a bad config or a node fails at 3 AM, the outcome is predictable: Click. Restore. Back to green.
That’s the difference between an incident and a Tuesday 😉
Learn more about SUSE AI: here
Get certified with SUSE AI: link
Contact us for SUSE AI ISV certification: martina.ilieva@suse.com
SUSE AI documentation: here