Cloud Computing Insider

Not every cloud product changed enterprise computing. Many were useful. Some were overhyped. A few were absorbed into larger platforms and forgotten. But a small number of cloud technologies fundamentally changed how enterprises buy, build, run, scale, and govern systems. In this episode of Cloud Computing Insider, David Linthicum breaks down five cloud products and technology categories that actually mattered: Microsoft Azure, cloud data and analytics platforms, serverless computing, Kubernetes and managed container platforms, and the original AWS building blocks, EC2 and S3. These technologies changed enterprise computing because they reduced real constraints. They helped companies avoid long hardware procurement cycles, standardize platforms, reduce server management, break down data silos, and modernize legacy integration patterns. This is not a list of the newest or flashiest cloud services. It is a list of the cloud technologies that changed enterprise behavior. They reshaped IT operating models, architecture decisions, application deployment, data strategy, governance, and the economics of infrastructure. The cloud products that mattered most were not just products. They became patterns that every major enterprise technology organization had to respond to.

The cloud was supposed to be infinite — but AI may have finally exposed its limits. In this video, we break down why Amazon Web Services is growing at a massive pace, yet still struggling to keep up with demand from AI companies and enterprise customers. AWS revenue is surging, backlog is exploding, and Amazon is planning enormous infrastructure spending, but even that may not be enough to satisfy demand through 2026 and 2027.  This story is bigger than one earnings report. It shows how artificial intelligence is changing the economics of cloud computing. The new bottlenecks are not just software or algorithms — they are power grids, data centers, GPUs, memory, cooling systems, networking gear, and construction timelines. Cloud capacity is becoming a scarce resource, and companies that reserve compute early may gain a serious advantage over competitors who wait too long.  We'll look at why AWS is still winning financially, why that success creates new pressure, and why the future of AI may depend less on smarter models and more on who can build enough physical infrastructure fast enough.

For two decades, enterprise architects fought tooth and nail against proprietary APIs, databases, and multi-year cloud contracts to keep their systems portable. We thought containers, microservices, and Kubernetes had finally solved the vendor lock-in problem for good. But while everyone was celebrating software portability, the major cloud providers quietly pulled off the ultimate architectural bait-and-switch: they moved the lock-in down into the physical silicon. In this video, I break down why custom cloud chips—like AWS Graviton and Trainium, Google TPUs and Axion, and Microsoft Azure's Maia and Cobalt—are rewriting the rules of enterprise IT. While these purpose-built processors offer unbeatable price-to-performance and massive runtime cost cuts on paper, they come with a hidden operational tax. Once your core AI models, compilation pipelines, and runtime frameworks are hyper-tuned to execute on a specific vendor's proprietary instruction set, you can't just containerize your workload and shift it elsewhere. The hardware moat is real, and it's turning standard cloud migrations into multi-million-dollar re-engineering nightmares. Watch this breakdown before you migrate your mission-critical applications to custom cloud hardware, and learn how to navigate performance gains without falling into the silicon trap.

Technology has never been more powerful, more available, or more affordable to deploy. Cloud computing, SaaS, DevOps, automation, cybersecurity platforms, data tools, and now AI were all supposed to make IT faster, cheaper, and more valuable to the business. But the numbers tell a more uncomfortable story: organizations are spending more on technology than ever, while productivity gains and business outcomes are not keeping pace.  In this conversation, David Linthicum breaks down why IT may actually be getting worse at its core job: turning technology dollars into measurable business value. The issue is not that the tools are failing. The issue is that IT organizations have become buried in complexity, technical debt, poor architecture, bloated processes, cloud waste, and metrics that reward activity instead of outcomes.  If global IT spending keeps climbing while productivity growth slows, then leaders need to ask a harder question: are we really becoming more efficient, or are we just buying more technology to work around the problems we refuse to fix?  This episode challenges the conventional wisdom around digital transformation and asks whether modern IT has lost the plot.    

Moving a petabyte out of the public cloud isn't a technical problem — it's a money problem. Uploading is free on every major provider, but downloading that same data back to your own servers triggers egress fees that can run into the tens of thousands of dollars. In this video, I break down exactly what it costs to pull one petabyte from AWS, Azure, and Google Cloud back to on-premises systems, comparing the open internet against private circuits like Direct Connect, ExpressRoute, and Cloud Interconnect. I walk through the real math, the hidden fees nobody mentions, the time each path takes, and the trade-offs around security, governance, and bandwidth. If you're planning a cloud migration, a repatriation, or just want to understand why the cloud keeps your data sticky, this is the breakdown you need before you sign anything — or before you hit "download" on a petabyte.