5 Signs It's Time to Move Your AI Workloads from Public Cloud to Private Cloud

Public cloud works. Until it doesn’t. And the point where it stops working is due to the slow buildup of friction that starts eating into your iteration speed before you’ve even named it an infrastructure problem.

The honest case for private cloud vs public cloud is not “private is better.” It's certain workload characteristics that hit real ceilings on shared infrastructure. For instance, distributed training at scale, regulated data and MLOps pipelines where auditability matters. For these, you’re not paying for flexibility, you're paying for constraints.

Here are the five signals worth paying attention to. 

1: Your Benchmark Results Are Not Stable

Same job, same VM type, two consecutive days. The throughput numbers are different. You are not making changes but the environment is.

On shared multi-tenant infrastructure, your GPU VM is physically co-located with workloads you cannot see. PCIe bandwidth, memory bus access and NUMA topology, all of it is crowded. On a single-node job, this is manageable noise. Once you’re coordinating across multiple nodes, that noise compounds across every all-reduce and every pipeline stage.

Unstable benchmarks are not just annoying. They mean you can’t trust your architectural decisions. You can’t compare checkpoints between runs. You can’t project how a longer training job will behave at the target scale. 

On a dedicated private cloud GPU cluster like NVIDIA B300s, you’re not competing for physical resources with anyone. The hardware is yours for the duration of your workload. That’s not a guarantee public cloud can make, regardless of how the VM is marketed.

2: Your Scaling Efficiency Falls Apart Past a Certain Node Count

You’ve profiled the job. Individual GPU utilisation looks fine. But effective throughput, what you actually get per GPU when you scale the job out degrades as you add nodes. The compute is not the bottleneck. The network is.

Most public cloud GPU offerings use Ethernet-based interconnects. Under low contention, they perform reasonably well. But multi-tenant networks are not low-contention by design, and as your job scales, the all-reduce operations that were fast on 8 nodes start stalling on 32 because the network cannot keep up with what the training loop demands.

The interconnect fabric matters a lot for large-scale distributed training. Latency, bandwidth and fabric behave under congestion; all directly affect how well your job scales. This is a hardware and topology question, not a configuration one. You cannot tune your way out of a shared Ethernet fabric when you need the throughput characteristics of InfiniBand.

If your scaling efficiency is dropping off as you add nodes, the interconnect architecture is almost certainly a contributing factor and it is one that private cloud infrastructure built for AI training can address.

For instance, our Secure Private Cloud supports high-performance networking designs selected based on workload scale, architecture and other requirements. It offers both Spectrum-X RoCE (Ethernet) and InfiniBand fabrics, chosen according to performance, operational and cost considerations to ensure the network can keep pace with distributed training demands and doesn’t become the bottleneck as you scale.

3: Storage Is Sitting On Your Critical Path

Your GPUs are idle during data loading. Checkpoint writes are blocking your pipeline. The compute is paid for and it is sitting there waiting.

Some public cloud AI workflows end up spread across object storage, attached block storage and ephemeral SSDs out of necessity. This is not because it is the right architecture but because that is what is available. Managing that tiering is real engineering time. Pre-staging datasets, handling persistent volume claims, designing preemption and checkpoint durability, none of that produces model improvements. 

A private cloud lets you provision storage that aligns with your performance, latency and persistence needs:

• Local NVMe (ephemeral / scratch): high-throughput storage for data staging, caching and fast checkpointing during training runs
• Shared Storage Volumes (SSVs): persistent block storage for datasets, checkpoints and artefacts across restarts
• Secure Object Storage: durable storage for data ingress/egress and long-term retention, with encryption in transit
• Parallel filesystem: high-throughput, shared access across nodes for distributed training and HPC workloads

4: Your Orchestration Layer Doesn’t Fit Workload Mix

You have long-running distributed training jobs, batch inference pipelines, hyperparameter sweeps and interactive sessions. Managed Kubernetes handles some of this. It doesn’t handle all of it well. Gang scheduling on K8s is imperfect under contention. SLURM would be a better fit for the training workloads but it’s not on the table. So, your engineering team works around the scheduler instead of working on the actual problem. 

Orchestration flexibility is one of those things that looks like a nice-to-have until you’re debugging a 64-node job that died three hours in because the scheduler placed nodes sub-optimally and you had no way to control it. The scheduler shapes everything: preemption behaviour, queue priority, resource fragmentation and blast radius of a failed job.

A private cloud deployment that supports both managed Kubernetes and managed SLURM means you use the right tool for each workload class rather than forcing everything through the same abstraction. Our Secure Private Cloud offers four deployment options, one of which is a Managed Platform where the entire cluster stack (infrastructure, OS, networking, storage and orchestrator) is operated for you, so you only focus on running workloads.

5: Compliance Is Starting To Dictate Your Infrastructure Decisions

Data residency mandates, GDPR, sector-specific regulations in financial services, healthcare or government, all these at some point stop being a legal team concern and start being your problem. You are spending time mapping cloud region configurations to regulatory requirements. The question “where does my training data actually go?” does not have a clean answer.

On some shared public clouds, data can traverse shared network paths, sit in shared storage tiers and get backed up to regions you did not specify. That is workable for some workloads. For regulated data, it does not work.

Private cloud gives you hard ownership boundaries. Single-tenant infrastructure means your data does not share physical hardware with other organisations. The physical location of your compute, storage and network is explicit and auditable. Jurisdiction is not something you configure after the fact- it is a baseline property of how the environment is built. 

For teams operating in regulated sectors, this actually speeds things up. Our Secure Private Cloud supports compliance alignment tailored to your specific deployment and final design scope, helping you validate requirements with security and legal teams more efficiently and move to running workloads sooner.

Public And Private Cloud Coexist

The question is which workloads go where.

Most mature AI/ML teams run hybrid. Public cloud handles experimentation, early-stage iteration and low-stakes batch jobs; private cloud handles production training runs, regulated data processing and anything where benchmark reliability and throughput consistency are non-negotiable.

The cost of migrating a workload to a private cloud is real but finite. But the cost of running production training on infrastructure that gives you non-deterministic benchmarks, degraded scaling and compliance gaps is ongoing. You pay in wasted GPU-hours, slower iteration and engineering time spent on workarounds.

If you’re starting to see these patterns in your workloads, it is a strong signal to rethink how your infrastructure is set up for training at scale. A conversation with our team can help you map your current stack to a Secure Private Cloud design that’s built around your needs, so you can eliminate bottlenecks.