Scaling AI Storage Cost Effectively

Conversations about artificial intelligence (AI) tend to focus on the massive computing power it requires. What gets less attention is the data storage it needs. Where will you store the immense, varied volumes of data used to train and operate AI models? The best answer is “the cloud,” but not all cloud storage platforms are the same when it comes to performance and cost.  

This article explores how enterprises can achieve consistent AI storage performance while avoiding the surprise charges that often occur when storing large amounts of data in the cloud. 

If you’re an AI leader, you face the challenge of balancing innovation, scale, and cost. Lower-cost cloud storage, while tempting for the size of your AI data training set, might not be the best approach. It usually translates into lower storage performance, which inhibits the pace of AI innovation. Indeed, cloud storage decisions increasingly determine how quickly teams can experiment and how far budgets stretch. Lower per-gigabyte costs often come with surprisingly high fees for data egress, API access, and related functions.  

Regardless of which solution you choose, keep in mind that your AI and machine learning (ML) landscape is not static. Your dataset will change and grow over time. If you go with cloud storage, you will want a platform that can scale and adapt as AI use inevitably expands in your organization.  

Storage requirements for modern AI and ML workloads 

What do AI, ML, and analytics workloads need from cloud storage? To understand storage requirements for these workloads, it is useful to grasp the underlying AI data pipeline architecture. AI workloads pull diverse types of data from various sources into some form of storage. This may be a single cloud platform, but it could also comprise multiple clouds and on-prem storage resources. The AI environment then imports data from storage and runs it through preparation (such as cleansing and normalization), AI training, and inference generation. It’s an iterative process that pushes data back into storage before the cycle repeats.  

With the AI data pipeline in mind, let’s look at the requirements needed to support the workload.  

In addition to being compatible with AI ecosystems, such as AI frameworks, data management platforms, and GPUs, it needs massive capacity for unstructured data. The ability to ingest and serve large volumes of email, documents, media, and related content is essential. The storage must deliver high parallel read performance because AI tools often use correlation, a process that requires the simultaneous analysis of separate data streams.  

Cloud storage for AI and ML should provide “always-hot” accessibility. The data used for AI and ML should be durable and readily available, meaning the model must be able to obtain whatever data it needs as quickly as possible with consistent performance across data lifecycles. ML typically trains the AI model retrospectively, looking back at historical patterns to find where it needs to correct itself. Ideally, accessing older generations of data should not slow down this process.  

Access costs need to be predictable. AI, ML, and analytics read and write data from cloud storage in erratic patterns. This back-and-forth should not result in surprise charges for data egress or API access, which are common among many cloud storage vendors. 

The hidden cost of scaling AI: storage is the new bottleneck 

Knowing that your AI and ML workloads are going to grow, you’re trying to determine what might prevent you from easily scaling up. The surprise is that computing won’t be what impedes your AI and ML growth. Data volume and costs are your biggest gating factors. 

A further complication in planning for AI and ML growth is that AI data growth is nonlinear and bursty. It features cyclical spikes tied to training and iteration. The quantity and quality of these spikes depend on how often your AI model retrains itself. Some enterprises train only once, while others train periodically or continually. In the latter case, you’ll have a lot of data moving in and out of your cloud storage platform. For each training cycle, the AI data pipeline will have the platform ingest new data from various sources, then export it to support the AI training process. 

These cycles will cause financial problems for enterprises that are subject to traditional cloud pricing models, which penalize AI data handling at scale. Key issues include: 

• API request fees — When the storage platform ingests data from an external source, such as a data warehouse, the platform may charge an API request fee. These fees are often calculated based on small blocks of data, so moving a terabyte of data might trigger tens of thousands of API request fees. 

• Egress charges — When data exits the storage platform (egress) and goes to the AI solution, the cloud storage provider charges an egress fee. These, too, can quickly add up when you’re dealing with the large volumes of data used in AI and ML. 

• Tiering complexity — Placing data on multiple tiers of storage to achieve a lower price can also lead to access delays, which can negatively affect the AI solutions performance.

• Cost unpredictability as access patterns change — The way the AI and ML solution handles data will vary from one training session to the next. The result will be cloud storage bills that fluctuate with little predictability. 

Another surprise you might run into is that “cheap storage” can actually become expensive, as API and egress fees tend to be higher in lower-priced tiers—the storage provider assumes that you won’t be accessing or moving cold storage data very often, so they charge more for the service. If your training runs repeatedly and re-reads data from cold storage, it can result in unexpected, large bills.  Analytics jobs can yield similar results due to spikes in data read/write operations. There’s also a potential administrative cost when teams don’t know which tier each data set belongs to. 

The questions that emerge are: How can organizations store petabytes of data in the cloud without breaking the budget? How do you avoid surprise charges when storing and accessing large data volumes in the cloud? There are two essential answers. One is to become a well-informed consumer of cloud storage services. Get good at reading price and fee charts and really understand what you’re buying. The goal is cloud cost optimization. The other way to avoid surprise charges for AI/ML cloud data storage is to find a vendor with consistent pricing and no egress fees or API fees. 

Designing cost-efficient AI data pipelines 

It can be a challenge to balance performance and cost when choosing storage for AI, ML, and analytics workloads. However, with the right solution and best practices, it is possible to architect a cloud storage solution that supports both backup and active analytics. 

One of the most effective ways to control AI pipeline costs is to keep the architecture simple. Fewer platforms and APIs mean fewer components to manage, maintain, and troubleshoot. Every extra element adds friction and can interrupt AI workflows, driving up administrative effort.

Cost savings also come from simplifying the data architecture. Centralizing AI datasets, such as consolidating into an AI data lake, reduces administrative overhead and improves performance. The pipeline should scale datasets predictably, without forcing teams to constantly remodel storage costs.

For example, if one dataset, such as social media posts, is growing faster than others, its increased size may necessitate changes to the pipeline’s archiving, backups, and tiering. It’s best to avoid these administrative burdens. It might not be possible in every AI/ML scenario, but it’s worth exploring the impact of scaling datasets at the start of the pipeline design process.  

The realization of these goals depends on storage platform selection. In the best case, a single storage platform should support mixed workloads, including backup copies, active analytics, and AI training datasets. This setup allows efficient pipeline operation. For instance, Wasabi’s cloud object storage for AI eliminates the administrative work required to move data between tiers. Wasabi also offers flexible controls, so you can set time or file-size intervals, space thresholds, and even file-retrieval processes as you see fit.  

A single platform can also enable you to implement an important best practice, which is to avoid overpaying for performance you don’t need. Ideally, a cloud storage platform will allow you to match storage performance to actual workload needs. Eliminating over-tiering complexity will help you accomplish this goal. Wasabi offers simplified cloud storage tiers for your AI pipeline data to offload on-prem data. 

The AI data pipeline will require data archiving. Older versions of datasets may need to remain accessible, and so forth. This archiving, however, introduces the potential for inefficiency and unexpected costs. Wasabi offers a solution to this dilemma with its active archiving, which keeps your archived data accessible in the AI pipeline the moment you need it—without retrieval fees or access delays. 

Achieving efficiency and savings in the AI data pipeline is about more than just money. Storage has the potential to be the bottleneck that slows innovation before compute does. When the storage element of the AI data pipeline offers consistent performance, this results in more efficient GPU utilization, potentially larger training runs, and faster iteration cycles. 

Predictable pricing at scale: Wasabi Hot Cloud Storage 

Cloud object storage is the optimal cloud storage solution for AI/ML workloads. Which cloud object storage solution will work best for your AI? That will depend, of course, on your enterprise’s unique requirements. If your goal is to deploy AI storage with the best financial characteristics, performance, and scalability, consider Wasabi Hot Cloud Storage: 

• Flat, predictable pricing that aligns with AI growth patterns — The price to store AI data on Wasabi Hot Cloud Storage remains uniform as data scales continuously and access patterns evolve. 

• No egress fees — By not charging anything for egress, Wasabi Hot Cloud Storage enables frequent training runs and cross-region access to AI data, along with hybrid and multicloud AI architectures. 

• No API request charges — Eliminating this fee protects you from unexpected billing related to data ingest, cloud integration, and backup and restore processes. Not having to pay for API requests also encourages AI experimentation without fear of hidden costs. 

• AI storage designed for compute-heavy workloads — With Wasabi Fire, you get an S3-compatible, all NVMe flash storage designed to meet the needs of latency-sensitive workloads such as AI/ML, media workflows, real-time analytics, high-performance web applications, and more. 

To learn more or sign up for a free trial, visit our cloud object storage for AI page.