#cloud-computing

#cloud-computing

AI workloads are already expensive due to the high cost of renting GPUs and the associated energy consumption. Memory bandwidth issues make things worse. When memory lags, workloads take longer to process. Longer runtimes result in higher costs, as cloud services charge based on hourly usage. Essentially, memory inefficiencies increase the time to compute, turning what should be cutting-edge performance into a financial headache.

Nvidia (NASDAQ: NVDA) has firmly established itself as the leading player in artificial intelligence (AI) infrastructure, as it accounts for nearly 92% of the data center GPU market. That dominance has been the foundation of its robust financial performances of recent years. In its fiscal 2026 second quarter (which ended July 27), Nvidia reported revenues of $46.7 billion, up 56% year over year and exceeding guidance, while its GAAP ( generally accepted accounting principles) gross margin was 72.4%.

Some of the personnel changes and additional reviews that we've seen in the U.S. Fed seem to continue and have resulted in longer deal cycles and some increased variability overall in that particular vertical for us.

Businesses worldwide have pushed cloud computing spending to $912.77 billion in 2025 up from $156.4 billion in 2020. But here's the interesting thing: businesses are no longer merely making the switch to cut costs. They're chasing operational nimbleness that on-premises infrastructure cannot keep up with. It's a shift that follows how entertainment platforms like download 1xbet app Saudi Arabia utilize distributed computing to support millions of simultaneous users across different geographic locations.

Colocation capacity in North American datacenters has dropped to a record low of 2.3 percent, with the construction pipeline mostly pre-leased, significantly constraining growth.

Foxconn's Cloud & Networking revenue has jumped 47%, driven primarily by AI server demand, overtaking its consumer electronics business for the first time.

According to figures from researchers at Omdia, the annual datacenter capex of cloud giant Amazon alone now exceeds $100 billion, making it roughly comparable to the entire GDP of Costa Rica.

A startup torches $120K in 72 hours because autoscaling had no ceiling; a DDoS popped up, the cluster spun 2,000 m5.24xlarges, and by the time anyone parsed the Slack noise, the bill looked like a phone number.

"Trust in public cloud providers is being eroded, especially among businesses operating outside the United States, and driving a growing movement toward sovereign and private cloud solutions."

We are grateful for AWS's partnership as GSA continues to equip agencies with modern solutions at scale and at savings. Through this unique partnership, the federal government is poised to deliver on President Trump's AI Action Plan and solidify its position as the global AI leader.

Amazon not only dominates the cloud computing market with a 30% share but also leads in e-commerce, capturing approximately 38% of the U.S. market.

When a pod is created, Kubernetes goes through a series of steps to ensure the pod is properly initialized and ready to serve traffic. This includes scheduling, resource allocation, and networking.

Multi-cloud refers to the use of multiple cloud computing services in a single architecture. This approach leverages the benefits of various public and private clouds to optimize efficiency and flexibility.

Dependence on American cloud giants has become risky in a world where geopolitical tensions can directly affect digital infrastructure, according to Bernd Wagner, CEO of Stackit.

Pinterest's Hadoop Control Center (HCC) automates the management of Hadoop clusters, transforming manual workflows into a fully automated system that enhances efficiency and scalability.

AI is evolving fast, but security isn't keeping up. Discover why zero-trust architecture is critical for safe, scalable AI agent deployment.

"Disaster recovery must be viewed as more than a compliance checkbox; it's a business viability issue," says Darrel Kent, field CTO for storage and recovery at GigaOm.

The challenge was substantial: modernize and streamline agent-related data by migrating from aging legacy systems to more scalable and accessible platforms.