Hybrid Cloud Storage Solutions for AI Workloads Guide

What are hybrid cloud storage solutions for AI workloads?

Hybrid cloud storage solutions for AI workloads combine local storage and cloud services so data can be processed where it best fits the workload. The aim is not to keep every dataset in one location, but to balance performance, cost, security, compliance and capacity across the AI lifecycle. The Storage Networking Industry Association’s analysis describes hybrid storage as workload-dependent, particularly for large, bursty training runs and elastic demand patterns.

For an IT delivery team, this means storage architecture becomes part of the AI operating model rather than a backend detail. Training data, model checkpoints, weights, embeddings and inference data may have different access patterns and latency requirements. A practical design gives each data type an appropriate storage tier while keeping movement, security and operational ownership clear.

Why do AI workloads need hybrid cloud storage?

AI workloads are storage-intensive because they repeatedly read large datasets, write checkpoints and move model artefacts between processing environments. Training may create sustained demand for throughput, while inference can require predictable access to smaller model files and frequently used data. A single storage platform may therefore be convenient, but it is not automatically the best fit for every stage.

Hybrid architecture also addresses the tension between local performance and cloud scalability. Nasuni describes hybrid cloud storage as combining cloud object storage scalability with local performance through intelligent file caching, while also bringing security into a unified platform. This pattern can let teams keep frequently accessed data close to workloads without treating every dataset as permanently local.

There is also evidence that organisations are choosing a balanced approach rather than moving all AI infrastructure to one side. An A10 Networks report says 42% of respondents favoured a balanced hybrid approach between on-premises and cloud infrastructure for AI workloads. The report identifies latency, availability and performance as key drivers, which are practical concerns for infrastructure and delivery leaders.

How should you design hybrid storage for AI training?

Start by separating the training workflow into data preparation, active training, checkpointing and model distribution. These stages may share data, but they do not necessarily need the same storage behaviour. Data preparation can use scalable object storage, active training may need data close to accelerators, and checkpointing can require low-latency writes that protect progress during long-running jobs.

Google Cloud’s AI Hypercomputer documentation says Cloud Storage is designed for massive datasets used in AI training and bulk inference. It also identifies the Rapid family as a way to reduce data bottlenecks by colocating data with GPU or TPU clusters. The important operational principle is to place high-volume training data near the compute that consumes it, rather than assuming network distance will be invisible to the workload.

Google Cloud’s TPU guidance distinguishes between performance and cost choices. Regional Cloud Storage buckets with Rapid Cache and client-side tuning are recommended for the lowest cost, while Cloud Storage Rapid Bucket is recommended for best performance and scale. This gives teams a useful decision framework: use the less aggressive option when cost optimisation matters most, and use the performance-focused option when data access is limiting the training job.

Checkpointing deserves separate treatment because a failed or interrupted training run can make recovery dependent on the most recent usable checkpoint. Google Cloud’s guidance identifies Managed Lustre for high performance without extensive tuning when sub-1 millisecond latency is required, including synchronous checkpoints and high-speed weight propagation. That makes low-latency file storage a targeted tool for demanding workflow steps rather than a default destination for every dataset.

Which storage options fit different AI workload needs?

The right choice depends on whether the dominant requirement is scale, latency, caching, or access across existing environments. The following comparison uses the capabilities described in the research notes and focuses on how an operator might position each option.

Storage approach	Best fit	Main operational value	Key consideration
Cloud Storage with regional buckets and Rapid Cache	Cost-conscious training and inference data access	Scalable storage with caching and client-side tuning	Requires attention to placement and tuning
Cloud Storage Rapid Bucket	Training or inference where performance and scale are the priority	Data is positioned for high performance near AI compute	Performance is prioritised over the lowest-cost configuration
Managed Lustre	Synchronous checkpoints and high-speed weight propagation	Sub-1 millisecond latency without extensive tuning	Best reserved for workflows that need this latency profile
Hyperdisk ML	Small model caching across many instances	Supports model caching and scaling to up to 2,500 instances	Suited to caching rather than every storage requirement
IBM Storage Scale	AI data pipelines spanning storage environments	Global file and object data layer with live-data access	Requires planning across IBM and third-party environments
Intelligent file caching in a hybrid platform	Edge access with cloud object scalability	Brings frequently used data closer to users or workloads	Cache behaviour must match access patterns

Google Cloud’s TPU storage guidance also says Hyperdisk ML supports small model caching and scales to up to 2,500 instances. That makes it relevant when many instances need access to model files without treating the entire training corpus as local block storage. As with all tiers, the design question is what data needs fast repeat access and what data can remain in a scalable shared repository.

How does hybrid cloud storage support RAG and live AI data?

Retrieval-augmented generation, or RAG, depends on data pipelines that prepare and retrieve information for an AI system. IBM describes Storage Scale as software-defined file and object storage that provides a global, high-performance data layer and integrates with IBM Fusion Content-Aware Storage for RAG and AI data pipelines. The practical implication is that storage must support both the source data and the processing path that turns it into usable AI input.

IBM also says Storage Scale can connect IBM and third-party storage environments and allow AI workloads to operate on live datasets in place. That approach can reduce latency and unnecessary data movement when copying information into a separate AI repository would create delay or operational duplication. For delivery teams, the benefit is less about selecting a named product and more about preserving controlled access to current data while AI workloads use it.

The same research says Storage Scale is available in all-flash and hybrid configurations and is cloud-ready for VMware, Kubernetes and OpenStack environments. Those deployment options matter when an organisation has several infrastructure estates rather than one standard platform. The architecture should therefore define where data is authoritative, which systems can access it, and how changes are reflected in AI pipelines.

What should IT teams measure before selecting a solution?

Begin with workload measurements rather than a storage product shortlist. Record dataset size, read and write patterns, checkpoint frequency, model-cache requirements, latency sensitivity and whether demand is steady or bursty. SNIA’s guidance supports this approach by framing hybrid storage as a way to balance cost, security, compliance and performance across different AI lifecycle stages.

Next, identify where data movement creates avoidable work. If a dataset is repeatedly copied between local storage, cloud object storage and AI compute, the architecture may be paying in transfer time and operational complexity. IBM’s description of live-data access and Nasuni’s description of intelligent caching both point toward reducing unnecessary movement while keeping scalable storage available.

Finally, define ownership and recovery responsibilities. The storage design should state who manages data placement, cache policy, access controls, checkpoint retention and restoration tests. Teams can also use an AI Cost Calculator to structure cost discussions, while an AI Uptime tracker can help connect storage availability to the reliability of AI services.

How do you implement hybrid storage without creating a new silo?

Use a staged implementation that begins with one representative workload. Choose a training, inference or RAG pipeline with measurable storage delays and document its current data path. Then test whether caching, regional placement, a performance-focused bucket, low-latency file storage or a shared file-and-object layer improves the specific bottleneck.

Keep the data classification simple enough to operate. Separate active data, reusable source data, checkpoints, model files and lower-frequency content, then map each category to a storage tier. This avoids the common delivery problem of creating a technically sophisticated design that no team can maintain consistently.

Governance should be tested alongside performance. Confirm how the design handles access, security, compliance requirements, failure recovery and expansion to additional AI workloads. A hybrid architecture is successful when teams can explain why data is in each location and can change that placement without disrupting the AI service.

FAQ: hybrid cloud storage for AI workloads

Is hybrid cloud storage better than cloud-only storage for AI?

Not for every workload. Hybrid storage is useful when an organisation must balance cost, security, compliance, latency and performance, or when demand is bursty and elastic. The best architecture depends on the access pattern and lifecycle stage rather than on a universal preference for local or cloud storage.

Where should AI training data be stored?

Large training datasets can use scalable cloud object storage, especially when the storage is positioned near GPU or TPU clusters. Google Cloud recommends regional buckets with Rapid Cache and client-side tuning for the lowest cost, while Rapid Bucket is recommended for best performance and scale. The choice should follow the workload’s cost and throughput requirements.

What storage is appropriate for AI checkpoints?

When synchronous checkpoints and high-speed weight propagation require sub-1 millisecond latency, Google Cloud’s TPU guidance identifies Managed Lustre as a suitable option. Checkpoint storage should be selected based on recovery requirements and write behaviour, not simply on total capacity.

How can teams reduce unnecessary AI data movement?

Use storage that can keep data close to the workload or allow AI systems to operate on live datasets where appropriate. IBM describes Storage Scale as connecting IBM and third-party environments and supporting live-data access, while Nasuni describes intelligent file caching for local performance with cloud object scalability. Start with the data path that causes the most delay, then test a targeted change.