Cloud Storage for AI: Key Considerations, Options, and How to Choose the Best Fit

Artificial intelligence (AI) is only as good as the data it runs on. Whether you’re training massive language models, fine-tuning predictive algorithms, or running real-time inference, your data needs a home that can keep up. That’s where cloud storage for AI comes in. Choosing the right storage isn’t just about capacity — it’s about speed, scalability, integration, and cost-effectiveness.

Below, we’ll walk through what to consider, what your options are, the roadblocks you might face, and why Silk is the best choice for AI workloads.

Why Cloud Storage Matters for AI

AI workloads put unique demands on your infrastructure. They require:

• High throughput and low latency for rapid data access during model training and inference.

• Massive scalability to handle growing datasets without downtime.

• Seamless integration with compute environments, analytics tools, and AI frameworks.

• Data durability and security to protect valuable intellectual property and meet compliance standards.

Without the right foundation, even the most advanced AI projects can stall.

Key Considerations When Choosing Cloud Storage for AI

When evaluating cloud storage for AI, keep these factors in mind:

Performance for Large Datasets

AI training often involves terabytes or even petabytes of data. Your storage must deliver consistent, high-speed read/write performance so your GPUs or CPUs aren’t idling.

Scalability and Flexibility

AI workloads are dynamic — you may need to scale up rapidly for a training run, then scale down for inference. Look for storage that can adjust to your needs without costly overprovisioning.

Cost Management

Storing massive datasets can rack up bills quickly. Understand not just the storage cost per GB, but also egress fees, API call charges, and the expense of creating and maintaining data copies.

Data Proximity to Compute

The closer your data is to your compute resources, the less latency you’ll have. Co-locating storage and AI workloads in the same cloud region (or even on the same infrastructure) can significantly improve performance.

Security and Compliance

For regulated industries like healthcare or finance, your storage must offer robust encryption, access controls, and compliance certifications.

Options for Cloud Storage for AI

Most AI teams choose between:

• Object Storage (e.g., AWS S3, Azure Blob, Google Cloud Storage): Great for storing large amounts of unstructured data, but may require extra optimization for high-performance AI workloads.

• Block Storage (e.g., AWS EBS, Azure Managed Disks): Provides low-latency access and is well-suited for databases and structured data.

• File Storage (e.g., Azure Files, Amazon EFS): Useful for workloads that need shared access and compatibility with POSIX file systems.

• Specialized AI Storage Platforms: Built specifically for AI and machine learning, offering tuned performance, integrated data pipelines, and advanced scaling.

Common Roadblocks in AI Cloud Storage

Even with powerful cloud platforms, AI teams often run into challenges:

• The Performance Bottleneck: Storage can’t keep up with high-speed GPU clusters, slowing training times.

• Data Copy Sprawl: Multiple environments and teams lead to many redundant data copies, eating up budget and increasing security risks.

• Complex Integration: Stitching together compute, storage, and AI frameworks can take weeks of engineering work.

• Unpredictable Costs: Ingress/egress fees and API request charges can cause surprise bills.

Why Silk Is the Best Cloud Storage for AI

Silk was built to eliminate these challenges. Our high-performance software-defined cloud storage platform integrates seamlessly into your AI architecture to give you:

• Blazing-fast performance so your GPUs never sit idle.

• Cost savings through efficient storage utilization and reduced data copy requirements.

• Zero-complexity integration with your existing cloud environment — no re-platforming required.

• Enterprise-grade security to keep your AI data protected and compliant.

Whether you’re running AI in dev/test or full-scale production, Silk accelerates your workloads, reduces complexity, and optimizes costs — making it the smart choice for cloud storage for AI.