As organizations race to innovate in AI, efficient, scalable, and cost-effective cloud storage has become key to their success. Whether you’re training massive models or deploying real-time inference pipelines, following best practices for AI storage will help you maximize performance, minimize costs, and ensure the integrity and availability of your most valuable AI asset—data.
In this blog, we’re going to take a look at five of those best practices, to help you get the most out of your cloud storage solution when working with AI.
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1. Understand Your Data Lifecycle
You’ve assembled your training data set, loaded it into fast storage next to your GPU compute, and hit the button to start your training. What happens when the training run is complete? If you’re just going to delete that data set, then great—enter rm -r and simply move on.
If not, though, you’ll need to carefully consider the ongoing costs of storage. Leaving that dataset where it is will likely cost you many times over what you’d spend archiving it to a more cost-effective location. By fully understanding and mapping your data lifecycle—and distinguishing between active (e.g., during model training), and inactive data (e.g., archived/dated model versions)—you can manage your storage costs much more efficiently.
2. Check In Your Checkpoints
Training AI models is a delicate, resource-intensive process. Hardware failures, software bugs, and even power outages can derail week-long training runs, wasting precious time and compute resources.
The two most important steps you can take to avoid these kinds of snags are:
- Frequent checkpointing: This means regularly saving a model’s state so you can pick the training process back up from the last checkpoint, rather than starting all over again at square one.
- Backup checkpoint data to the cloud: Storing checkpoint data on only local drives alone can be very risky. If the local storage fails, your checkpoints—and all the progress they represent—could be lost. That’s why you should always back up checkpoint data to secure cloud storage solutions as well. This dual approach ensures both speed (for quick recovery) and durability (for disaster recovery), letting you and your team rest easy knowing your hard work is being protected.
3. Keep Your Model Safe
With that same spirit in mind, don’t forget that your models require safekeeping, too. It takes a lot of time and money to train AI models, so protecting them—whether from hardware failure, human error, ransomware attack, or other threats—is absolutely paramount. To safeguard your models:
- Use your cloud provider’s object lock to prevent accidental or malicious deletion.
- Implement regular, automated backups of both model binaries and associated metadata.
- Store critical models in geographically redundant locations for disaster recovery.
These few simple steps can go a very long way to ensuring that your valuable, hard-earned models remain safe and functional, even when things take a turn for the worse.
4. Don’t Lock Your Data Behind a Paywall
Let’s imagine you’re planning your next training run. When looking at cloud providers, you discover that you can realize significant savings by switching GPU compute providers. The only problem is, your current provider will charge you an arm and a leg to move the data to where it needs to be. There’s still a net gain from moving, but you lose significant margin by paying this exorbitant “exit toll,” known as an egress fee. This is why, before committing to a storage provider, you should carefully review its pricing structures and fees, including the following:
- Calculate the total cost of moving your data, not just storing it.
- Consider multi-cloud strategies or providers that offer free or low-cost egress for AI workloads.
By understanding these costs upfront, you retain the flexibility to optimize your infrastructure as business needs evolve, and avoid the all-too-common trap of hidden fees.
5. Do the Mirroring Math: The Replication Equation
Let’s imagine you’ve found yourself a cost-effective storage option with a specialized cloud object storage provider. Even after finding the right solution with the right pricing structure and performance, there are considerations to be made. No matter how quickly you can download the data, if compute and data are in different locations there’s no escaping the fact that your GPUs might be spinning idle waiting for that data to arrive.
To avoid this predicament, break out your calculator and do the “mirroring math”:
- Calculate the time and cost required to replicate (mirror) data to a location near your GPUs before training starts.
- Weigh the benefits of lower storage costs against the potential delays and additional storage expenses during training.
- For large or frequently accessed datasets, it may be worth pre-staging data in high-throughput storage close to your compute.
Ask yourself: Is it faster and/or cheaper to replicate the data upfront to be in close proximity to your GPUs, or does the time required to mirror the data and the additional storage cost during the training run outweigh the benefits? Intelligent data placement—balancing cost, performance, and proximity—ensures your AI workloads run efficiently and cost-effectively.
Building a Future-Proof AI Storage Strategy
The relentless pace of AI innovation demands a storage strategy that is agile, scalable, and cost-effective. Thankfully, the above five best practices can go quite a long way to ensuring the long-term success of your AI project
By understanding the entirety of your data lifecycle—checkpointing wisely, securing your models, avoiding data lock-in, and optimizing data placement—your team is laying the groundwork for sustained AI success. No matter what industry you’re in, these best practices will help to control costs, accelerate innovation, maintain compliance, and protect your team’s most valuable digital assets, in both the near and long term.
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