Cloud storage is a core component of modern IT infrastructure, powering everything from enterprise applications to AI workloads. But as organizations migrate more data to the cloud, costs can quickly spiral out of control. Without a clear strategy for analyzing and managing cloud storage expenses, businesses risk paying for unused capacity, inefficient performance, and redundant data copies.
Artificial intelligence runs on data — but not just any data. The most valuable insights often come from relational databases such as Oracle, SQL Server, DB2, or Postgres. These systems have powered enterprises for decades, storing mission-critical transactional data that AI models now want to consume in real time.
Traditional copy data management approaches often rely on cumbersome and storage-intensive methods — such as full data replication — to create multiple copies of data for various purposes like machine learning, AI agents, backups, testing, development, and analytics. However, these conventional methods can lead to significant storage overhead, increased costs, and complexity in managing multiple data copies.
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.
For CTOs, CIOs, and data architects, the challenge of balancing development velocity with infrastructure costs has never been more critical. As organizations scale their cloud operations and expand their development teams, traditional approaches to data management create bottlenecks that directly impact both technical performance and financial outcomes. The solution isn’t to make small improvements to current processes, but to completely change how development and testing teams get and use data.
The volume of healthcare data is exploding, doubling every 73 days. As organizations move to the cloud to manage this influx, many are facing unanticipated challenges: soaring cloud bills, sluggish access to data, and increased pressure to support AI initiatives.
When it comes to optimizing Microsoft Azure for enterprise databases like SQL Server and Oracle, choosing the right cloud storage can be the difference between an environment that simply runs—and one that thrives. Enterprises often face a decision between Azure-native options, such as Azure NetApp Files (ANF), and specialized third-party solutions like Silk, which are purpose-built for high-performance data workloads.
In today’s data-driven world, organizations need faster, more flexible, and more cost-effective ways to store and manage their data in the cloud. Enter software-defined cloud storage — a modern approach that transforms how enterprises handle data across cloud environments. In this blog, we’ll break down what software-defined cloud storage is, its benefits, common use cases, and why Silk is the premier vendor for your enterprise storage needs.
Artificial Intelligence (AI) is enabling more of our teams to consume more data, and AI agents increasingly need access to live systems in real-time. Especially during quarter end, which is also the busiest time for transactions.
In today’s cloud-driven world, organizations face an ongoing challenge: balancing cost-efficiency with top-tier performance. Enterprises using relational database workloads often encounter bottlenecks, such as high storage costs, sluggish processing speeds, or a lack of scalability to meet evolving demands, especially when preparing for the AI era.