Data is nothing new and, by now, most businesses have developed effective strategies for storing most types of data that powers their operations.

AI training data, however, is an exception. Because few organizations began embracing generative AI or developing their own AI models until recently, most lack experience deciding where and how to store the training data that powers their models.

If you want to take full advantage of genAI, this is a critical challenge to overcome. Keep reading for tips on how to handle it as we unpack strategies and best practices for storing AI training data.

What Is AI Training Data?
The Unique Storage Challenges of AI Training Data
Training Data Storage Options
The Many Ways to Store Training Data

What Is AI Training Data?

As you likely know if you're familiar with the basics of generative AI technology, AI training data refers to the data used to train the large language models (LLMs) that power genAI apps and services.

LLMs are designed to simulate human decision-making in ways that allow them to generate original content. To understand how humans think, however, LLMs must train on data produced by actual humans (or on "synthetic" data designed to resemble human-generated information). Unless they are trained on appropriate data, LLMs can't do their jobs effectively, and the genAI services that they power deliver little value.

The Unique Storage Challenges of AI Training Data

AI training data is not different in a technical sense from other common types of data. It typically includes information such as emails, documents, and possibly audio and video files. That type of data is compatible with a wide range of modern storage systems, such as databases, file storage, and block storage.

That said, the data that AI models train on is unique in other ways, which can lead to special storage challenges:

Very high data volume: AI training datasets tend to be massive, which means they can consume an enormous volume of storage space — and lead to massive storage costs, especially if storage is not cost-optimized.
Irregular data access: AI models typically only access training data when they're actively training or retraining — events that may happen on an irregular, unpredictable basis. As a result, it can be tough to predict exactly how frequently the data will need to be made available. This can affect storage strategies because some storage solutions (like "cold" cloud storage) don't make data readily available, so not knowing ahead of time exactly when you'll need the data can pose problems.
The complexity of data compression: It's possible in some cases to compress AI training data to save space. However, whether you can compress data at all, and the type of compression algorithm you can use, depends on your model's ability to work with compressed data. For this reason, compression — which is a bread-and-butter way to reduce storage costs in other contexts — isn't always a reliable option for AI training data.
Changing data: The data used for AI training may change over time; indeed, keeping data up-to-date is important for ensuring that model behavior reflects the most timely information available. This means that the ability to update training data is important — but the feasibility of making changes depends in part on how you store the data. You may also want to version-control the data so you can track how it changes over time, but not all storage systems support this.

For these reasons and more, there is no simple, one-size-fits-all approach to storing AI training data. The best strategy depends on the type of data you're dealing with, how your models interact with that data, and what your business priorities are.

Training Data Storage Options

We can't tell you exactly which storage solution is best for your training data. But we can offer some general guidelines about which types of storage strategy makes most sense under different circumstances.

Cloud object storage

In general, cloud object storage services, like Amazon S3 and Azure Blob Storage, are good options for storing training data when you have a very large volume of data to store because they offer virtually infinite storage capacity. These services also offer built-in versioning, so they are useful if you need to track changes to data over time.

On-prem scale-out storage

On-prem storage built on top of scale-out storage platforms such as Ceph are less scalable than cloud storage in most cases, so it's not ideal if you have truly vast volumes of training data to house. The tradeoff is that this approach may be more cost-effective in the long run than cloud storage, since you don't have to pay monthly fees to store your data.

Nor do you have to pay egress fees if you move the data outside the cloud — which makes on-prem storage a more economical option.

Databases

Databases are typically not an ideal way to store training data because they are less scalable and flexible than other options. That said, if your training data is structured — if, for instance, you have different categories of data and you want to store each one separately — a database could be an efficient means of doing that.

File storage

File storage, which houses data inside local file systems, is also usually not a great way to store AI training data. The structure that file systems impose on data can make it challenging to store training data that lacks any coherent structure. In addition, file storage is harder to scale because there's no simple way of extending file systems beyond a single computer or server. (Network-based storage platforms like NFS can enable this, but they are not trivial to set up.)

The exception is situations where you have a relatively small amount of training data to store, and when your model lives on the same machine that hosts the data. In that case, file storage may lead to faster training because data never needs to move over the network.

Conclusion: The Many Ways to Store Training Data

Finding high-performing, cost-effective storage for AI training data is no easy task — and the more data you have, the harder it becomes to store it in an optimal way. The good news is that many types of storage solutions are available. By carefully considering the pros and cons of each one, you can determine which storage option delivers the most benefits with the fewest drawbacks for the training data you need to store.

About the author

Christopher Tozzi is a technology analyst with subject matter expertise in cloud computing, application development, open source software, virtualization, containers and more. He also lectures at a major university in the Albany, New York, area. His book, “For Fun and Profit: A History of the Free and Open Source Software Revolution,” was published by MIT Press.