5 Reasons Why AI Teams are Turning to Cloud Object Storage

AI teams are increasingly relying on cloud object storage to store and manage their ever-growing volumes of data needed to train, fine-tune, and operate today’s most powerful AI models. On the surface, the reason is simple: Most AI workloads—including machine learning pipelines, Retrieval Augmented Generation (RAG), and inferencing—love unstructured data. And storing all that messy information and metadata is what object storage was built for. 

We’ve all seen that stat where unstructured data like images, videos, emails, documents, sensor logs, etc. now accounts for more than 80% of all enterprise data. Unfortunately, much of this data is locked in silos or lives on systems not built for AI. 

Centralizing all that valuable data is a major driver behind AI teams’ interest in cloud object storage. It provides a simple, scalable, and cost-effective way to make unstructured data AI-ready. In this post we’ll explore that and four other reasons why cloud object storage could be a good fit for your organization’s AI workloads. 

1. Lower the cost (and risk) of starting your AI journey 

Getting an AI initiative off the ground can require significant investment in compute, storage, and talent. For storage in particular, traditional on-premises infrastructure often demands large upfront capital expenditures that aren’t feasible for teams still exploring their first use cases. 

Cloud object storage flips that model. Instead of CapEx, you pay as you go, scaling storage costs with your needs. This makes it easier to run pilots, test new models, and adjust your strategy without betting the farm on new and expensive infrastructure. 

If you're experimenting with AI, cloud object storage helps you start fast without the financial burden of building out a hardware stack. 

2. Scale without breaking your budget 

AI workloads are data-hungry and notoriously dynamic. Vision models, large language models (LLMs), and fine-tuning operations often balloon in size as projects evolve and new variables are introduced. Not only do data volumes grow precipitously, capacity needs can spike unpredictably. 

The elastic scalability of the cloud is designed to support this type of growth. Need to double your amount of capacity next week? Cloud object storage can handle it with zero disruptions (see how leading AI teams are managing their explosive data growth with object storage).  

However, you need to be careful when choosing a cloud object storage provider, as AI workloads can be API-intensive. With retraining, inference, and pipeline automation all pulling from the same datasets repeatedly, fees for API requests and other transactions can add up fast. To ensure low, predictable pricing when storing AI data at scale, look for a provider with simple, usage-based pricing. 

3. Work with best-of-breed compute resources, wherever they are 

Modern AI workloads are modular by design. Teams often run compute in one cloud, handle orchestration in another, and pull data from a variety of internal and external sources. Trying to lock compute and storage into a single environment just doesn’t make sense anymore. 

Decoupling storage from compute gives you the flexibility to use the tools and clouds that work best for each workload. S3-compatible object storage integrates easily with public cloud, hybrid deployments, or specialized GPU environments. That means teams can mix and match compute and storage environments to meet their specific needs or as the market dictates—without being locked into a walled garden solution. 

If your AI architecture includes GPU-first clouds, on-prem data centers, containerized apps, or federated data pipelines, cloud object storage delivers the flexibility to tie it all together. 

4. Break down data silos and feed your AI pipeline 

AI models don’t just need data—they need high-volume, context-rich, unstructured data. But across many organizations, that data is locked away in legacy systems, departmental servers, or cold archives that aren’t connected to AI workflows. 

Object storage helps break down those silos. It supports large-scale unstructured data ingestion and makes assets like images, videos, logs, and documents accessible across multiple phases of the AI pipeline—from training to fine-tuning to inferencing.  

The flat, metadata-driven architecture of cloud object storage enables AI teams to tag, query, and retrieve specific subsets of data quickly and efficiently, making it easier to feed models exactly what they need, where and when they need it. 

If you’re trying to unify your enterprise data for AI, cloud object storage helps make it accessible and usable. 

5. Protect your AI assets and ensure resilience 

Whether you’re building proprietary models, fine-tuning with sensitive customer data, or archiving outputs for compliance or future retraining, that content must be protected. 

Cloud object storage offers strong foundations for data durability, immutability, and geographic redundancy. Features like Object Lock, versioning, and native encryption (at rest and in transit) help safeguard data from tampering or unauthorized access while supporting compliance with industry and regulatory standards such as GDPR, HIPAA, and FERPA. 

For AI teams, it’s more than just security. If your models need to be rebuilt, retrained, or revalidated, having a reliable, unaltered source of truth in the cloud could be a lifesaver. 

A foundation for scalable, sustainable AI 

Your storage strategy will have a major impact on the speed, cost, and success of your AI initiatives. Whether you're piloting your first LLM or scaling a RAG-based application across the enterprise, you need infrastructure that keeps up. 

Cloud object storage is built for the way AI works today. It handles unstructured data at scale, integrates easily with any compute environment, and doesn’t require massive upfront investment. It also brings the flexibility to adapt as your data pipelines evolve—from ingest to inference to archiving. 

While many platforms offer object storage, not all are optimized for AI. Hyperscalers often introduce unpredictable costs through egress and API request fees and impose complex tiering structures that can stall experimentation and drive up total cost of ownership. 

Wasabi eliminates those barriers with high-performance, S3-compatible cloud object storage that is truly usage-based: one flat rate for capacity with no fees for egress, API requests, or other transactions. Our defense-in-depth approach to security includes fee-free immutability and Multi-User Authentication, an industry-first feature that makes it impossible for any one individual—even an admin—to delete a storage bucket or your entire account without multiple authorized approvals, adding an extra layer of protection for your most critical AI data. 

Perhaps that’s why more teams are choosing Wasabi to support their data-intensive AI initiatives.