Cloud Computing Trends

About a year ago, I created a comprehensive graphic comparing the major cloud providers. As I revisit it now, I'm struck by the rapid evolution of the cloud landscape. While each provider's core competencies remain largely unchanged, there have been some significant developments and emerging trends. Let's dive in! 1. 𝗧𝗵𝗲 𝗥𝗶𝘀𝗲 𝗼𝗳 𝗠𝘂𝗹𝘁𝗶-𝗖𝗹𝗼𝘂𝗱: Increasingly, businesses are adopting a multi-cloud approach, cherry-picking services from different providers to optimize costs, avoid vendor lock-in, and take advantage of each platform's unique offerings. This shift towards a more diverse and flexible cloud strategy is a testament to the growing maturity of the market. 2. 𝗦𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗧𝗮𝗸𝗲𝘀 𝗖𝗲𝗻𝘁𝗲𝗿 𝗦𝘁𝗮𝗴𝗲: In response to the pressing need for environmental action, the big three cloud providers have all stepped up their sustainability efforts. From renewable energy initiatives to tools that help customers monitor and reduce their carbon footprint, the cloud is becoming greener. 3. 𝗧𝗵𝗲 𝗔𝗜/𝗠𝗟 𝗕𝗼𝗼𝗺: Artificial intelligence and machine learning have seen explosive growth, with each provider offering an expanding array of AI/ML services. These tools are becoming more user-friendly and accessible, democratizing AI and enabling businesses of all sizes to harness its power.     4. 𝗧𝗵𝗲 𝗘𝗱𝗴𝗲 𝗘𝘅𝗽𝗮𝗻𝗱𝘀: Edge computing has come into its own, with Azure Arc, AWS Outposts, and Google Anthos all seeing significant enhancements. This development is crucial for IoT, real-time data processing, and low-latency applications. As the intelligent edge continues to evolve, it's opening up exciting new possibilities. 🚀 5. S𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀 𝗦𝗶𝗺𝗽𝗹𝗶𝗰𝗶𝘁𝘆: Serverless computing has been a game-changer, abstracting away infrastructure management and enabling developers to focus on writing code. Over the past year, serverless offerings have continued to mature, with improved tooling, easier integration, and more robust functionalities. As always, the "best" cloud provider is the one that aligns with your unique requirements, existing infrastructure, and long-term objectives. It's crucial to periodically reassess your cloud strategy to ensure it remains optimized for your evolving needs. I'm curious to hear your thoughts! What notable changes or trends have you observed in the cloud ecosystem recently?

7 Cloud Migration Strategies Every Cloud Engineer Should Know (with scenario questions for interviews) Cloud migration can originate from on-premises infrastructure or from another cloud provider. And it goes beyond just moving data. It's about strategically deciding the best approach for each application and workload. The goal is to optimize performance, cost, and long-term viability in the cloud. Here’s a simple breakdown of the key strategies you should focus on: 1/ Retain (Revisit later) ↳ Keep workloads on-prem if they aren’t cloud-ready or are still needed locally. Scenario : You have a critical legacy application with custom hardware dependencies. How would you initially approach its cloud migration? 2/ Retire (Decommission) ↳ Eliminate outdated or unused parts to reduce cost and simplify the system. Scenario : During an assessment, you identify an old reporting tool used by only a few employees once a month. What's your recommendation? 3/ Repurchase (Drop & Shop) ↳ Replace legacy apps with SaaS alternatives, a fast and cost-effective solution. Scenario : Your company's on-premise CRM system (example) is outdated and costly to maintain. What quick cloud solution might you consider? 4/ Rehost (Lift & Shift) ↳ Move your application to the cloud as-is, with no code changes needed. Scenario : A non-critical internal application needs to move to the cloud quickly with minimal disruption. What strategy would you prioritize? 5/ Replatform (Lift, Tinker & Shift) ↳ Make light optimizations before migration, for better performance with minimal effort. Scenario : You're migrating a web application, and a small change to its database will significantly improve cloud performance. What strategy does this align with? 6/ Relocate (Many Providers) ↳ Change the hosting provider without modifying the app, a quick and simple approach. Scenario : Your current cloud provider is increasing prices significantly for a specific set of VMs. How might you address this without rewriting applications? 7/ Refactor (Re-architect) ↳ Redesign your application for cloud-native capabilities, making it scalable and future-ready. Scenario : A monolithic, highly scalable customer-facing application is experiencing performance bottlenecks on-prem. What long-term cloud strategy would you propose?. Beyond these strategies themselves, successful cloud migration also focuses on: - thorough assessment, - understanding dependencies, - meticulous planning, - and continuous optimization Just remember: successful migration isn't just about the tools, but the approach. Very important to understands the "why" behind each strategy — not just the "how." Dropping a newsletter this Thursday with detailed scenario based questions (and example answers) for each of these patterns — subscribe now to get it -> https://lnkd.in/dBNJPv9U • • • If you found this useful.. 🔔 Follow me (Vishakha) for more Cloud & DevOps insights ♻️ Share so others can learn as well

The Death of SaaS (as We Know It) Satya Nadella recently shared a fascinating perspective: AI is poised to replace traditional application layers, embedding business logic directly at the database level. This marks a profound shift: one that could redefine the very foundation of SaaS. Imagine a future where AI doesn’t just power apps but replaces them. Business logic, instead of flowing through multiple layers of UI, middleware, and APIs, is orchestrated directly with the database. This means the end of bloated, layered software and the beginning of lean, AI-native architectures. The ripple effects are massive. SaaS as a subscription model may lose relevance as modular AI-driven workflows dominate. Interfaces will transform, shifting away from dashboards and fixed workflows to adaptive, real-time experiences—think voice commands, conversational AI, or neural interfaces. Even the app store economy may collapse under the weight of this new paradigm, replaced by marketplaces for AI-driven workflows instead of apps. This could imply the extinction for the SaaS we know today. For developers, businesses, and consumers, this shift will reshape how software is built, sold, and used. The question isn’t if SaaS is dying; it’s what comes next. What do you think? Is this the end of SaaS, or the beginning of something even more disruptive?

Salesforce just fired the starting gun on a seismic shift in how we pay for software. At Salesforce #Agentforce, they announced they’re moving away from the traditional per-seat SaaS model to a consumption-based pricing for their AI agents. This is huge. Why? Because it signals the end of paying just to have access to technology. Instead, we’re moving toward paying for outcomes—the actual value delivered. Think about it. In a world where AI agents can perform the job functions of entire departments, does it make sense to charge per seat? Probably not. Here’s what’s changing: - From access to outcomes: Companies will pay for what the AI actually accomplishes. - From subscriptions to value: Pricing adjusts based on usage and results. - From Software-as-a-Service to Agent-as-a-Service: Technology that collaborates with you as a partner This isn’t just a tweak in pricing—it’s a radical upending of commercial models for large SaaS companies. What does this mean for businesses? - Budgeting will evolve: Costs align directly with value received. - ROI becomes clearer: Easier to measure the direct impact of technology investments. - Greater flexibility: Scale usage up or down based on needs without worrying about seat counts. It’s an exciting time, but also a challenging one. Is every SaaS company ready to embrace a model where companies pay directly for the value they receive? At Uniti AI, we’ve been thinking along these lines. We price our AI agents based on the amount of work they do, not on how many seats a company has. I believe this is the future. What do you think? Is the per-seat model on its way out?

🚨 Kubernetes 1.33 Is Dropping — And It’s a Major Leap for Platform Engineering 🚀 Just when we thought Kubernetes had hit its maturity stride, v1.33 arrives with some of the most impactful enhancements in recent memory. Whether you’re building platforms, scaling infrastructure, or debugging production workloads — this release is for you. Here’s what makes 1.33 a serious game changer: 🔐 User Namespaces for Pods (GA) Pods now run in isolated Linux user namespaces, separating UIDs/GIDs from the host OS. → Huge boost for multi-tenancy and defense-in-depth security. ⚙️ Live Pod Resizing (Beta) Dynamically adjust CPU & memory on running pods — no restarts. → Smooth scaling. Zero downtime. Efficient resource management. 🧹 Ordered Namespace Deletion (Alpha) Kubernetes now supports controlled deletion of namespaces with ordered finalizers. → Fewer orphaned resources. Cleaner GitOps workflows. 🛠️ Sidecar Lifecycle Support (Beta) Sidecars can now start and stop independently from main app containers. → No more lifecycle bugs. More precise container orchestration. 🔍 Ephemeral Debug Containers (GA) Attach temporary containers to live pods for real-time troubleshooting. → Debug in production without disrupting your app. Game on. 💡 TL;DR Kubernetes 1.33 is not just another patch — it’s a strategic upgrade that makes clusters more secure, more resilient, and easier to operate at scale. ✅ Enhanced isolation ✅ Seamless resource updates ✅ Predictable operations ✅ Real-time observability If you’re in DevOps, SRE, or Platform Engineering — this release is a big deal. 📌 Curious to hear from others: ➡️ Which feature are you most excited about? ➡️ How are you preparing your clusters for 1.33? Let’s discuss. 👇 #Kubernetes #CloudNative #PlatformEngineering #DevOps #SRE #K8s133 #OpenSource #InfrastructureAsCode #Security #Observability

"Where is AI taking us?" "Is SaaS dead?" and "Will there even be a need for software?" I hope this post puts all the changes we are experiencing into context so we can navigate (and shape) this innovation landscape together. Having sold software for 30 years, I’ve had the privilege of experiencing the journey from on-premises software and data centers to cloud-based SaaS and now toward AI-based systems. This journey reveals a remarkably consistent theme: - Innovation reacts to inefficiency, - it is triggered by drastic changes in the market and - It proceeds at a very high pace, which is uncomfortable for most of us. To explore AI's revolution and anticipated impact, I’ve created a chart that maps the transformation of enterprise software, from the rise of servers and virtualization to the emergence of AI-agent marketplaces. Please trace the journey with your eyes (or finger), and you will see three trends emerge that will likely shape the future of GTM: i) Sustainable SaaS ii) AI-Led GTM iii) Consumption-Based Pricing As a side note, at Winning by Design, we have started using the term "PPX" as a shorthand to describe the various forms of consumption-based pricing, e.g., Pay Per Action, Pay Per Use, and Pay Per Outcome. It allows us to simplify the shift as SaaS --> PPX. Having said that, the next chapter in GTM appears to belong to autonomous GTM systems. These systems are AI-powered to enable durable growth from the get-go, and they are driven by PPX-based business models that will accelerate customer adoption. This is no different than subscription models did for SaaS over a decade ago vs. paying upfront perpetual software. In this, durable growth reflects a combination of GTM Efficiency and high GRR/NRR. The latter is governed by a new First Principle known to all 3,500 Revenue Architects out there (say it with me :-): Recurring Revenue is the result of Recurring Impact. Okay.. so where does that leave us? Well, let’s be clear: SaaS isn’t over—not even close! Many SaaS Scaleups and Grownups will simply evolve, powered by AI, into Sustainable SaaS Scaleups and Grownups. Some will fail, some will succeed. Meanwhile, Startups can choose to either pick Sustainable SaaS or embrace a PPX pricing strategy. And as they go to market, they can now opt for Product-Led Growth (low), AI-led growth (mid), or Human-Led Growth (high-end). Furthermore, AI-enabled GTM motions will democratize GTM, allowing companies from across the globe to compete with the existing SaaS juggernauts. These startups must show up with innovation, as today's buyers are more likely to side with a trusted brand. Simply put, there's something for everyone this holiday season. 💙 💙 💙 👉 What do you think will define the future of software?

20 years ago Mary Meeker called the internet’s rise; 10 years ago, the mobile revolution. Last week, she made her biggest bet yet... And it has nothing to do with models. In her new 340-page report, Meeker reveals what’s actually driving AI’s future: Infrastructure. Not just chips. But power, land, CapEx, and velocity. Here are the 10 most important takeaways from her report ranked from most to least significant: 1. CapEx is now strategy. $455B in 2024 AI data center spend. A 63% YoY jump. Not a spike, this is a structural shift. Infrastructure is the product. 2. Power is the gating item. Data centers use ~1.5% of global electricity. Demand is growing 4x faster than grid supply. The bottleneck is the grid. 3. Inference eats the future. Training is episodic. Inference is forever. As AI agents scale, inference will drive long-term infra costs. 4. Speed is a strategic moat. xAI built a 750,000 sq. ft. facility in 122 days. Deployed 200K GPUs in 7 months. Fast build = competitive edge. 5. Custom chips = stack control. Amazon (#Trainium), Google (#TPU), Microsoft (#Phi). Silicon is no longer optional, it’s leverage. 6. Overbuild is intentional. Hyperscalers are doing what Amazon Web Services (AWS) did in 2006: build ahead of demand. Surplus compute becomes a platform. 7. Emerging markets are the next frontier. 50% of internet users. <10% of AI infra. With the right energy and capital stack, emerging markets could leapfrog legacy hubs. 8. AI data centers are AI factories. "Apply energy, get intelligence." - Jensen Huang, NVIDIA CEO. Not metaphor. New economics. 9. Cooling and grid tie-ins are the edge. Latency, liquid cooling, substation access, infra is no longer just real estate. It’s engineering. 10. Sovereignty is back. Governments are co-investing in “Sovereign AI.” Infra is no longer neutral, it’s strategic. The next wave of AI winners won’t be those with the smartest models. They’ll be the ones who control the stack those models run on. #datacenters

SaaS isn’t slowing down. It’s getting eaten alive ... cannibalized. Look at the Aventis Advisors growth chart. The trend isn’t subtle, it’s a cliff. We went from 36 percent growth to 12 percent. Almost a decade of down and to the right. With forecasts pointing to 11 percent … and falling, this feels like a pretty big structural shift. SaaS is starting to look like utilities and pipelines, durable and necessary, but no longer where the real upside lives. And the reason is pretty simple. AI is cannibalizing the very work SaaS used to monetize. Here’s what the chart doesn’t show, but every operator feels. 1) SaaS used to sell “workflows.” AI sells “outcomes” (OaaS). Agents do the work inside the tool, so the tool stops being the product. The labor becomes the product. 2) Budgets (and investors) are leaving SaaS and flowing to digital labor. CFOs aren’t buying more seats. They’re buying fewer humans. AI fits. SaaS doesn’t. 3) Feature parity killed differentiation. Entire categories are indistinguishable. CRM, CX, marketing automation … all the same. AI exposes how thin the moats always were. 4) Enterprises hit peak-SaaS years ago. Now they’re consolidating and cutting 20 to 40 percent of their stack. AI accelerates that purge. 5) AI startups are growing at speeds SaaS can’t touch. When companies hit nine figures in months, not years, investor expectations reset. SaaS looks slow, expensive, and operationally bloated. 6) Value is moving down the stack. The action is in compute, data, agents, and orchestration. SaaS is becoming a UI layer that AI sits on, not the engine driving the work. The growth-rate collapse isn’t a mystery, it’s more of a transfer of value. SaaS is maturing into a stable, cash-flow asset class. AI is becoming the new growth engine of the enterprise. That means founders have a choice, build SaaS and optimize it like infrastructure, or build AI agents that replace the workflows SaaS was built to capture. One path gives you stable multiples, the other gives you growth.

Financial Times Headlines "How Lidl accidentally took on the big guns of cloud computing " This push for a EuroCloud is why a grocery chain like Lidl can suddenly emerge as a cloud competitor, snatching market share from AWS. Schwarz Digits generated €1.9 billion in sales last year and has secured major clients such as SAP and Bayern Munich. This is no small-scale experiment. The StackIT page highlights a broad range of services, from basic compute-network-storage to managed databases, messaging, Kubernetes, monitoring, security, and more. AWS is scrambling to respond, recently announcing a €7.8 billion investment in an AWS European Sovereign Cloud, with the first region expected to launch in Germany by the end of 2025. But will that be enough to regain the trust of European businesses? The pricing factor adds another interesting dimension. Lidl disrupted the retail sector with its low-cost groceries—could it do the same in cloud computing? With Schwarz Digits, could Lidl become the affordable EuroCloud option businesses are seeking? AWS’s challenge will be to prove that its new European Sovereign Cloud can match the trust, security, and cost-effectiveness that these companies need. As this unfolds, it's evident that the era of one-stop-cloud-shops is coming to an end. The market is fragmenting, and specialized, regionally-focused providers like Schwarz Digits are stepping in to address the gaps left by the global giants. 1: The Article mentioned: https://lnkd.in/eJpWZ4gP 2: StackIT https://www.stackit.de/en/

Tech is hitting a rare inflection point where the ground shifts faster than leaders can map it.    Arriving just in time for the holidays, Deloitte Tech Trends 2026 (https://deloi.tt/4aEpSfJ) is all about that shift — not someday, not theoretically, but what’s unfolding right now inside the enterprise.    Over the past year, I’ve heard a noticeable change in conversation with tech leaders. The question used to be “if” AI was the right move. Now, it’s about turning experimentation into real impact before they get left behind.   The urgency is real. A technology that once took decades to reach mass adoption now does it in weeks.   Innovation is moving in a flywheel (better models → more apps → more data → more investment → lower costs → even better models) and accelerating faster than any prior tech cycle.    This year’s report zeroes in on five forces reshaping the enterprise:    🟢 Physical AI: Intelligence stepping off screens and into the physical world.  🟢 Agentic AI: Pilots everywhere, yet scarce production, and a focus on why redesigning operations matters more than deploying agents.  🟢 The compute reckoning: Token economics rewriting cloud strategy  as usage skyrockets.  🟢 The great rebuild: Orgs are redesigning around speed, modularity, and human–agent teams.  🟢 AI advantage vs. AI risk: Security racing to defend at machine speed against threats operating with the same intelligence we’re harnessing.    The pattern across all five is more than just an enhancement cycle – it’s a rebuild cycle. And the organizations pulling ahead are the ones willing to rethink the playbook entirely. They’re redesigning instead of automating, prioritizing velocity over perfection, and anchoring every investment to real business outcomes.    So, grab some hot chocolate, a warm blanket, and dive into the trends we’re exploring for 2026 and beyond in Tech Trends 2026. A MASSIVE thank you to all the incredible minds that brought these insights to life: Kelly Raskovich, Jim Rowan, Tim Gaus, Franz Gilbert, Caroline Brown, Nitin Mittal, Parth Patwari, Ed Burns, Nicholas Merizzi, Chris Thomas, Lou DiLorenzo, Anjali Shaikh, Michael Caplan, Erika Maguire, Sunny Aziz, Adnan Amjad, Naresh Persaud, Mark Nicholson, Brett Davis, Simona Spelman, Amit Chaudhary, and Ranjit Bawa.