Multi-cloud has moved far beyond buzzword status. Today, enterprises are intentionally spreading workloads across multiple cloud providers to improve resilience, increase flexibility, avoid vendor lock-in, and tap into best-of-breed services.
For many database teams, the cloud promised limitless scalability and performance on demand. But in practice, it often delivers something far more frustrating: bottlenecks, inconsistent throughput, and mounting costs just to maintain acceptable SLAs.
For most enterprises, the cloud promise is simple: scale faster, innovate more, and pay only for what you use. But as workloads grow – particularly AI, analytics, and database-heavy applications – cloud bills often rise faster than expected. That’s why enterprises are actively searching for ways to reduce cloud costs while still meeting their performance needs.
It’s still running – but not really living. Your production database shuffles along, groaning under the weight of analytics queries, AI workloads, and constant demands for copies. It’s slow, unresponsive, and always hungry for more resources.
Ever been amazed when your bank spots fraud in real time, or your favorite app seems to know exactly what you want next? That’s AI inference in action — and it’s changing the way businesses operate every day. AI inference is the process of trained AI or machine learning models making predictions, classifying new inputs, and generating outputs from unseen data or patterns. It’s the moment AI gets real — when models leave the lab and start making a difference in the real world.
If your cloud bill seems to keep rising no matter how many optimizations you make, you’re not alone. Most enterprises are unknowingly paying for far more storage and compute capacity than they actually use. In fact, studies show that as much as 40% of cloud storage is overprovisioned — allocated but never utilized. It’s a quiet drain on budgets that grows month over month, line by line, across multiple environments.
Cloud data is more important than ever. Organizations are relying on it to power AI, analytics, and mission-critical workloads — and storage strategies are evolving to keep pace. To help IT and data leaders understand the trends, challenges, and opportunities shaping cloud data growth, we’re launching the 2026 State of Cloud Data & Storage Benchmark Survey.
Cloud adoption is no longer optional – it’s essential. But as organizations migrate critical databases and workloads to the cloud, they often discover an unexpected challenge: controlling costs. What seems simple at first glance can quickly spiral into runaway expenses without the right strategies in place.
As organizations generate and store more data than ever before, choosing the right storage solution has become critical. Two common options are traditional storage and software-defined cloud storage. While both approaches serve the same fundamental purpose – storing and protecting data – they differ greatly in terms of flexibility, scalability, and cost.
As artificial intelligence (AI) adoption accelerates, enterprises are facing a new set of challenges. AI workloads are complex, unpredictable, and data-intensive, creating massive strain on cloud infrastructure. Traditional cloud storage architectures weren’t built with these demands in mind—leading to skyrocketing costs, performance bottlenecks, and increased risk to mission-critical production data.