Enterprises will want to evaluate how this game-changing move by the AI juggernaut can effectively enhance their AI and machine learning capabilities.
Don’t wait until an incident costs you billions of dollars to create backup and recovery plans.
Recent advancements are paving the way for private cloud platforms to deploy AI and other essential workloads, causing enterprises to rethink a public cloud–only strategy.
Mainframes were the punchline of jokes in the early days of cloud computing, but their steady dependability is still critical to IT architectures.
Enterprises are not as swayed today by fancy new bells and whistles from the Big Three cloud providers. They want commodity features: low prices, reliability, and regulatory compliance.
New offerings from AWS, Google Cloud, and Microsoft Azure have upended the sovereign cloud services market—and raised questions and concerns.
We won’t make progress with AI if we don’t know how to predict costs accurately. Enterprises are running in circles.
The EU is pushing back against the dominance of US-based cloud providers. Balancing sovereignty and innovation will require a comprehensive strategy.
Many enterprises believe cloud costs are beyond their control but also lack comprehensive plans to optimize workloads and manage their usage. It’s time to take responsibility.
Less latency and high scalability are great, but does your enterprise have the skills to handle the increased complexity?
Skyrocketing cloud bills aren’t delivering on their promises, and proactive enterprises are doing something about it.
A viral video from years ago reminds us of the importance of strategy over impulse, an especially valuable point in light of AI and cloud computing.
High cloud costs rooted in insufficient training and inadequate architectural oversight use up money that could go to innovation or new markets.
Complicated cloud infrastructures can greatly benefit from automated oversight that tracks cloud resources, links costs to applications, and integrates security.
Navigating the new world requires business resilience, heterogeneous architectures, and a more diverse approach to cloud partnerships.
The rush to the cloud was an expensive experiment for many. AI could be a repeat without careful evaluation and planning.
With AI needing every spare dollar in enterprise budgets, enterprises are scrutinizing every workload to determine its optimal infrastructure.
As enterprises embrace hybrid solutions to cut costs, scale AI workloads, and set the foundation for sustainable innovation, the hyperscalers adapt their offerings.
Microsoft, AWS, Google, and others are disrupting the CPU and GPU markets with custom silicon, and we should have seen this coming.
Enterprises should look at cost-effective alternatives like private clouds, colocation, managed service providers, or hybrid solutions.
Don’t be swept away by ambitious promises of limitless innovation. Review your business case and infrastructure while you wait for this technology to mature.
Being all-in with the hyperscalers isn't working anymore. Enterprises are diversifying their data platforms for the AI era.
Faced with political uncertainty and complex global supply chains and regulations, smart enterprises will be proactive with their cloud strategies.
It’s about collaboration, not control, recognizing that enterprises need solutions that work across platforms and unique business situations.
Turns out you really can build a decentralized AI system that operates successfully across multiple public cloud providers. It’s both challenging and costly.
Cloud providers are building castles in the sky while enterprises struggle with fundamentals. The gap between AI hype and real-world implementation contributes to misallocated resources and jeopardizes billions of dollars.
The rise of AI and GPU-focused clouds is pushing enterprises into chaos. Poor planning and fragmented strategies are jeopardizing the original innovation goals.
The hyperscalers are reeling from the slower-than-expected growth of generative AI on their platforms, and many are now placing bets on agentic AI. I have some bad news.
When training an LLM has enormous costs and environmental impact, it’s worth asking what we gain by creating another one—especially if it isn’t that different from other models.
With a holistic view geared toward preventing security breaches and integration with all major cloud providers, Wiz is a definite asset to the Google ecosystem.
It’s time for enterprises to stop complaining about the lack of skilled IT workers and get to work fixing it. The companies that do will unlock billions in value.
The end of NetEase’s public cloud service has important lessons for how enterprises can mitigate risks and prepare for unexpected cloud provider closures.
Dissatisfied with the major cloud providers, CIOs are increasingly exploring hybrid models and niche providers to enhance agility and control over their infrastructure.
The tech giant offers a plan to form a group focused on agentic AI but no actual technologies or systems. What does this say about the future of its cloud leadership?
These little-known cloud resources offer lower latency and higher compute power but are best for specific use cases. Here’s how to determine when they offer reasonable solutions.
For companies struggling with high cloud costs, talent shortages, and data integration problems, a groundbreaking quantum processor isn’t the answer.
The uncontrolled and ungoverned AI apps your employees are using are becoming a real threat to cloud deployments, but banning them won’t work. Here’s what to do.
All organizations, public and private, must embrace a cloud-smart mindset, platform diversity, and skill modernization to drive smarter IT decisions.
Let’s take a realistic look at the effectiveness of finops. Great potential is often wasted because the finance team isn’t working with the engineers.
Enterprises are finally discovering that serverless computing is not a universal remedy. A mix of serverless and traditional architectures is almost always a better approach.
Don’t let your cloud journey die. Discover how to overcome common challenges to accomplish genuine digital transformation.
While enterprises question the value of public cloud services and AI investments sputter, specialized providers and private solutions are gaining momentum.
Alarming new research shows that if your enterprise is careless about managing cloud resources, it's just a matter of time before you're compromised.
Burned by astronomical cloud bills, enterprises need a strategic road map that balances flexibility and control to unlock the full potential of AI investments.
Being a quantum pioneer is turning out to be an expensive experiment. Quantum is still years away from widespread enterprise ROI.
Overprovisioning cloud resources to handle AI has sparked an epidemic of sky-high cloud bills. Adopt these strategies to control cloud spending.
An FTC report raises concerns about AI partnerships among major tech firms. However, the vibrant ecosystem of startups suggests that competition remains robust.
Harnessing the power of AI is essential for modern cloud security because AI is also the latest weapon. Enterprises must mitigate evolving threats while adopting new technologies.
As enterprises struggle to balance AI capabilities against data privacy concerns, federated learning provides the best of both worlds.
Effective financial management requires a comprehensive understanding of the hidden costs of cloud computing. A multicloud strategy makes vigilance even more critical.