In a recent survey, a staggering 82% of IT leaders reported experiencing performance issues with their AI workloads within the past year, primarily due to bandwidth and data processing limitations. At the same time, 93% agreed that there’s a greater expectation within their organizations for IT leaders to minimize time-to-revenue for their AI-driven IT infrastructure.
These statistics highlight the predicament that most AI infrastructure and operations teams face today: the challenge of balancing scalability with performance while staying on budget with two of their most expensive operational expense (OpEx) line item costs. Organizations are looking for their AI initiatives to pay off, while IT teams struggle to overcome the unique data challenges they face across the AI model/workload lifecycle—including scalability, performance, and cost management.
Ebook: “Why Object Storage Is Ideal for AI Workflows”
Want to take a deeper dive into the world of object storage? Check out our latest ebook, “Why Object Storage is Ideal for AI Workloads,” and discover the advantages this architecture has to offer across the model lifecycle.
Choosing the Right Cloud-Based Object Storage Provider for AI Data: There’s A Lot to Consider
Choosing the right object storage provider is one of the most consequential decisions infrastructure teams make when building AI‑powered applications. A mis-step can introduce hidden costs, brittle performance, and operational friction that put the brakes on time‑to‑insight and undermine ROI. Selecting or transitioning between cloud-based object storage providers demands careful consideration, as capabilities can vary significantly.
To ensure your AI infrastructure is robust and cost-effective, thoroughly evaluate providers based on several critical factors:
Low latency & high throughput
Performance is critical when selecting a cloud-based object storage provider for AI data. Low latency and high throughput in particular are key as they ensure rapid data access and processing. Low latency minimizes delays in distributing data to GPU clusters, dramatically enhancing training and inference efficiency. Meanwhile, high throughput prevents bottlenecks and improves overall system performance when working with the massive datasets typical of AI applications.
Reliability & uptime
Reliability is foundational. Even minor downtime can severely impact productivity, halt critical AI processes, and delay strategic objectives. Providers must offer clear service level agreements (SLAs) ensuring high availability, typically at 99.9% uptime or higher. Redundant architectures, data replication across regions, and reliable backup strategies are essential to maintain continuous and uninterrupted data access. Finally, when selecting a cloud-based object storage solution, data durability is table stakes.
Transparent & predictable pricing
Budget predictability is crucial for infrastructure planning and growth forecasting. Complex pricing structures, minimum retention periods, hidden fees for data transfers (egress), API requests, and retrieval charges can quickly erode cost-effectiveness. Providers should offer clear, simple pricing structures with explicit, predictable costs for all services involved. Ideally, charges for common activities such as data retrieval, ingress, and transactions should be minimized or eliminated to facilitate efficient AI workflows without unexpected budget impacts.
Data accessibility
Rapid, consistent data accessibility is non-negotiable for AI applications, especially during model training and inference, where delays can significantly degrade performance and outcomes. Providers offering “cold” storage tiers may appear economical upfront but introduce retrieval latency that could hamper time-sensitive applications. Opting for “hot” or always-on storage tiers ensures data remains immediately accessible without incurring delays, essential for high-performance AI workloads. Data portability is another important consideration for AI workloads, as the ability to freely transfer data to the GPU cloud (or clouds) of your choosing greatly increases flexibility and reduces the risk of lock-in.
Scalability and elasticity
AI initiatives typically experience fluctuating data storage demands, requiring infrastructure that can seamlessly scale with growth. Effective providers offer a scalable storage model capable of handling rapid expansions in data volume without performance degradation or significant architectural changes. Elastic scalability ensures that infrastructure teams can effortlessly manage peaks in data collection, processing, and model training demands.
Security and compliance
Security considerations cannot be overstated, particularly when dealing with sensitive or regulated data. Providers must demonstrate rigorous security standards, including data encryption (at rest and in transit), comprehensive access controls, detailed audit logs, and certifications such as SOC 2 compliance. These measures collectively ensure data integrity, protect against breaches, and ensure compliance with regulatory standards.
Leveraging the open cloud: Making data storage a critical part of your AI workflows
The open cloud is a cloud architecture and philosophy rooted in interoperability, data portability, and freedom from vendor lock-in. Unlike proprietary cloud ecosystems that tether customers to a single provider’s toolsets, APIs, and infrastructure, the open cloud is designed to enable seamless integration across platforms, tools, and environments. It supports open standards and APIs, gives users full control over their data, and allows organizations to choose best-in-class services without being locked into a single ecosystem.
In practical terms, the open cloud supports flexible data movement across public clouds, private clouds, and on-prem environments. It gives organizations the autonomy to mix and match services (e.g., compute from one provider, storage from another) and shift workloads as business or technical needs evolve—without punitive costs or excessive reconfiguration.
As organizations accelerate AI adoption, the open cloud offers clear, strategic advantages across every phase of the AI lifecycle—from data ingestion and preprocessing to training, tuning, and inference.
How Backblaze can help
The Backblaze B2 Cloud Storage platform facilitates smooth integration across various AI tools and platforms, and with Backblaze B2 Overdrive, you get a product designed to move exabyte-scale datasets at up to terabit speeds without the eye-watering price tag.
- S3 compatibility: Backblaze’s S3-compatible API ensures easy integration with existing applications and frameworks like TensorFlow and PyTorch.
- GPU compute environments: Backblaze partners with GPU providers like Vultr and PureNodal, enabling efficient data processing for training models on high-performance hardware without egress fees.
- MLOps platforms: Its compatibility with MLOps workflows allows users to streamline model lifecycle management while leveraging Backblaze’s reliable storage backbone. Together, these integrations simplify the AI deployment process and ensure maximum flexibility across cloud environments.
What makes B2 Overdrive different?
B2 Overdrive gets you all the above, plus it offers a specialized solution at a fraction of competitors’ costs. Here’s what you get:
- Up to 1Tbps throughput: In other words, the kind of speed that lets you move petabytes of data fast without complex architecture.
- Unlimited free egress: Move as much data as you want, whenever you want, to wherever you want. Egress is totally free.
- Private networking support: Transfer data at maximum speed through secure private networking connections to your infrastructure.
It’s built on the foundation of our always-hot cloud storage infrastructure, with no minimum file size requirements, no deletion fees, and powerful features like Event Notifications so you can build responsive and automated workflows. We’ll be sharing some of the innovations under the hood in the coming months—so, stay tuned to our series on the engineering behind performance.
