On-prem, Cloud, or Hybrid? Choosing the Right Storage Strategy for AI Workloads

AI models are powerful tools for enterprises of all sizes, but they don’t come that way out of the box. AI needs a steady diet of their fuel of choice: data. Because of this, most AI solutions need a robust storage solution to handle the amount of data needed to train and continuously support their development.

Primarily, these storage solutions come from one of two camps: on-premises storage hardware or the cloud. While they both offer unique benefits to an AI storage solution, neither one can sustain large AI data needs on its own. Rather, a hybrid solution of on-prem and cloud storage offers enterprises a “best-of-both-worlds" approach that gives AI models the space they need to grow and the speed to deliver results.

What makes good AI storage?

AI interacts with storage at different stages in its journey from ingestion to training and, finally, archival. At each point, the data has different requirements depending on how it’s being used. These requirements can be broken down into four general areas:

• Scalability: Ability to handle massive and growing datasets within budgetary requirements.

• Performance: High throughput for training and inference.

• Data Management: Lifecycle, access, and metadata management.

• Security: Encryption, access control, and data integrity features.

A complete data solution should check all the boxes. Individually, on-prem and cloud storage meet some of these requirements, but not all.

On-premises storage: performance and control with trade-offs

Let’s face it: there’s no beating on-premises storage hardware for certain use cases. Its power and proximity to data are crucial to the success of an AI, but rigidity and cost can be a roadblock.

PRO: Ultra-low latency for real-time applications

If there’s one thing AI values more than data, it’s speed. The viability of an AI application drops dramatically if its compute operations can’t reach the data it’s trying to query. Hosting storage locally allows the lowest amount of latency between data and the compute.

PRO: Predictable performance for tightly-coupled workloads

Cloud-hosted storage will always be susceptible to bandwidth limitations. Depending on the day, your connection speed could be significantly less than what it usually is due to increased activity or disruptions with your Internet Service Provider (ISP). Locally hosted storage bypasses this issue entirely, ensuring you’ll always have the best connection to your data.

CON: High CapEx spending required upfront

The high cost of an array of AI-ready storage hardware can be daunting, especially for organizations that are new to AI or aren’t sure yet what they’ll use the technology to do.

CON: Limited capacity and poor scalability without large reinvestment

All premises-based storage devices come with a set capacity limit. The AI data pipeline’s sky-high storage demands virtually guarantee that your organization will reach this limit sooner rather than later, requiring yet more expensive drives to be purchased.

CON: Physical infrastructure demands IT overhead and long provisioning cycles

Owning your own storage hardware also means managing your own in-house data center. Providing power, cooling, and regular maintenance can be challenging and expensive over time.

It’s important to remember that AI is an extremely new technology and it’s not always immediately apparent how your organization will benefit from adopting it. Given the high cost of purchasing on-prem storage equipment, your team should know ahead of time what the return on investment (ROI) will look like for an AI solution built on-premises.

Cloud object storage for AI: scalable and cost-effective

The cloud is where all the big, public AI models like ChatGPT, Google Gemini, and Microsoft Copilot live. Cloud storage is flexible enough for newcomers and experienced AI users looking to scale-up. That being said, cloud storage for AI use cases is limited by its performance and potentially high costs.

PRO: Storing massive, unstructured datasets with global reach

Unstructured data is the fuel that powers AI models, and the more fuel they have, the better they run. There are virtually no limits to how much data you can store in a public cloud service, making them ideal for data-hungry use cases like AI training. Unlike on-premises based storage, data storage in the cloud can be accessed from any internet-connected device, including AI applications. The ability of to query against your organization’s data globally, not just a siloed portion, is game changing for AI models efficacy.

PRO: Seamless scalability, even at exabyte scale

The cloud is unrivaled when it comes to scalability; its ability to ingest a nearly limitless quantity of new data without any upfront cost or forecasting makes it ideal for AI workloads. Training an AI model on the same data over and over does little to help its predictive capabilities, so giving an AI access to a steady influx of new data keeps it up to date on the most recent trends and expands its real-world usability.

PRO: Flexible deployment options

The cloud is ideal for organizations looking to take their first steps into an AI strategy. There is no hardware to purchase before you get started so you can begin with as much or as little data as you’d like without taking on undue expense.

CON: Limited throughput and bandwidth restrictions

Data stored in the cloud is subject to the unpredictable forces of bandwidth. As we’ve mentioned, speed is an essential component in a successful AI deployment, and any delays can hamper your solution’s efficacy. In an all-cloud environment, compute resources, too, are limited by bandwidth. Proximity is a factor here—if your cloud storage host and your compute host are in different locations (not impossible in the decentralized world of cloud services) then your performance will likely suffer as a result.

CON: Unpredictable costs (if you’re not careful)

Though the cloud eschews the upfront capital expenditure (CapEx) of on-prem deployment, it can become expensive if not monitored. Within hyperscaler environments, every inference, analysis, and query generated by AI models creates an API request that is charged to the user at the end of every billing cycle. Though these charges appear small—just fractions of a penny per 1,000 requests—they quickly balloon when scaled across an entire workload and can overrun any planned AI storage budgets.

CON: Limited choice with vendor lock-in

A hyperscaler cloud provider like Google or Microsoft will often offer a cloud compute product in addition to storage. While this may seem convenient, users will find that this is likely their only option. Hyperscalers impose hefty “fines” in the form of data egress and access charges that make moving data from one cloud to another virtually impossible. Once in the “walled garden” of a hyperscaler cloud environment, organizations are restricted by the fees associated with utilizing solutions from multiple providers that might suit their needs best.

Hybrid AI storage models: best of both worlds

On-prem and cloud-based storage both have their place in the AI training pipeline. Relying on only one to the exclusion of all else is working with one hand behind your back. Combining cloud and local storage media for a hybridized approach gives organizations a mix of speed and scale that AI workloads thrive on.  

On-prem storage: latency-sensitive workloads with regulatory considerations

Leverage on-prem storage for its speed and proximity to compute resources. There’s no substitute for local storage’s read-write speeds when training and inferencing data in an AI workload. In this hybrid configuration, you’ll never fight for bandwidth or encounter ISP issues, ensuring your AI applications will always be highly performant when you need it most.  

Local storage also helps keep your organization compliant with any data sovereignty or other industry regulations with greater controls for how and where your data is stored. 

Cloud object storage: scalable ingestion and archive

The cloud’s incomparable scalability and global accessibility gives your AI models access to a wealth of data from across the organization, freeing data from silos. The ability to continually ingest new data keeps AI models current and IT managers free from additional hardware procurement. The cloud’s scalability can also be applied to an archive where data is sent following its processing, training and inferencing.

Wasabi Hot Cloud Storage for AI workloads

Wasabi flips the script on traditional cloud object storage. With no fees for egress or API requests, data owners are free to access, index, and retrieve their data at no additional cost. This not only radically increases the affordability of cloud storage in AI contexts but offers a level of predictability not found in any other cloud storage provider.

Our S3 compatibility makes us interoperable with leading compute platforms both in the cloud and locally, giving users the freedom to choose the best solution for their needs. At a low cost per-TB and a single tier of high-performance storage, Wasabi is even an ideal destination for archival storage with high accessibility needs (active archiving).

Conclusion

The data storage needs of AI workloads vary significantly from one moment to the next—size and scale to take in vast quantities of new and existing data, and low latency to run data against computational resource. The trick isn’t to find a single storage solution to meet all these needs at once, but combining storage solutions to create a hybrid of cloud and on-premises storage that can adapt to suit any situation.

Wasabi’s singular combination of predictable pricing and performance makes us uniquely situated to handle the massive datasets that fuel AI. With high read/write speeds and no fees for egress or API requests, you can move data in and out of the cloud in an instant without any additional cost. As AI architectures continue to be refined, we are committed to supporting all the storage and compute options available through our S3-compatible API. Hybrid storage gives your organization the freedom to use your data how you want, and your cloud storage data is never freer than it is with Wasabi.