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

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

Salesforce has spent the last few years unsuccessfully trying to rebrand its way into AI relevance: Einstein → Einstein GPT → Einstein Copilot → Agentforce. This week they finally announced something with an actual POV: Headless 360 - exposing core functionality as APIs, MCP tools, and CLI interfaces so agents can interact directly with Salesforce without ever touching the UI. One of the defining companies of the SaaS era is acknowledging that the future of software may not involve a human staring at a dashboard at all. That changes a lot: ▪️ SaaS gets demoted from product to plumbing If the primary relationship is with an agent, then Salesforce, Workday, HubSpot, and ServiceNow start to look less like places where work happens and more like systems where data lives. Important, sticky, necessary, but increasingly abstracted away. ▪️ Value shifts from interface quality to system legibility In the old world, the product was the screen. In the new one, the product is the operational grammar underneath the screen: the schema, the business logic, the permissions layer, the audit trail, the reliability of action. ▪️ Ease of use becomes ease of delegation The question is no longer “can a rep use this?” but “can a manager trust an agent to use this?” The new gold standard is not delightful UX but safe delegation. ▪️The middle layer gets more powerful A lot of people treat headless as if models will flatten everything above the database. In reality, orchestration matters more: deciding what agents can touch, when they act, how exceptions route, how actions are audited. This is the new enterprise control plane - and also where a lot of startup opportunity lives. ▪️Pricing gets messy It’s not just that agents don’t need seats - it’s that the unit of value is unclear. API calls? successful outcomes? autonomous actions? revenue influenced? risk assumed? Once the user is no longer the meter, pricing gets confusing. ▪️ This could entrench incumbents as much as disrupt them Everyone wants the story to be “the death of legacy SaaS.” Maybe eventually. But incumbents still own workflow depth, data, and trust. If they refactor fast enough, headless may strengthen them. The risk isn’t that they have data - it's that they also have decades of entropy wrapped around it. This move both bullish and defensive for Salesforce. Bullish, because they are finally articulating a real thesis: enterprise software consumed agent-to-agent, not human-to-screen. Defensive, because the interface layer is losing power. If the point of invocation shifts to OpenAI, Anthropic, or some enterprise agent shell, then SaaS risks becoming a fulfillment layer behind someone else’s experience. Salesforce is not going headless because it plans to lie down and die. It is going headless because it wants to compete for that upper layer itself. Let the games begin.

For years the data center industry chased bigger. Bigger campuses. Bigger power contracts. 1,000-MW mega facilities. But the AI era is exposing a flaw in that model. AI inference doesn’t want to live 1,000 miles away. When decisions must happen in milliseconds — for power grids, public safety, robotics, financial systems, or smart cities — sending data to a distant hyperscale cloud and waiting for it to come back simply doesn’t work. So the architecture is changing. Instead of one massive campus: • 1,000 smaller urban sites • Compute next to where data is created • AI inference at the edge • Capacity that can scale in weeks, not years That’s the idea behind distributed AI infrastructure. Projects like Project Qestrel are rolling out fleets of edge data centers across U.S. cities — bringing HPC and AI inference directly into metro networks. Hyperscale isn’t going away. But the future of AI won’t be one giant brain in the desert. It will be a nervous system of distributed intelligence. And the closer compute gets to the edge, the faster the world gets. #EdgeComputing #AIInfrastructure #DataCenters #AIInference

The cloud divorce is happening. And most organisations aren't ready for either side of it. Three weeks ago at Mobile World Congress, the European Commission launched EURO-3C. A €75 million project to build Europe's first federated edge-cloud infrastructure. 70+ organisations across 13 countries. Not because they love spending money. Because they've realised their data lives in someone else's country, under someone else's laws, and they can't guarantee where it goes once it leaves the device. Meanwhile, Azure UK South is struggling. If you've tried to deploy GPU-enabled VMs recently, you'll know. AllocationFailed. ZonalAllocationFailed. Quota requests that used to be auto-approved are now manually reviewed. Subreddits and community boards are filling up with engineers hitting the same walls. Microsoft's own Q&A forums show models being pulled from UK South entirely, with access restricted to what they're calling "strategically prioritized customers." West Europe is the same story. Microsoft's response? A new campus in North Yorkshire on the site of a decommissioned 1,960MW power station. Now being converted into compute.. They consumed so much power they need to become the power station. But, do we actually need all of this? Yes, AI workloads are genuinely demanding. That's real. But underneath the AI gold rush, everyday software has become obscenely resource-hungry. Teams & Chrome are unusable on an 8GB laptop if you want to do anything else. Windows ships with so much telemetry, spyware and background processing that a fresh install immediately starts phoning home to half the internet. Ten years ago, we ran entire businesses on a fraction of this compute. It worked & We didn't need a nuclear reactor to power the email server. We've normalised bloat. We've accepted that a video call needs 4GB of RAM. And now we're building power stations to run the cloud that runs the bloat. The repatriation numbers tell the story. 83% of enterprises plan to leave public cloud. 61% of Western European CIOs are shifting local. Sovereign cloud spending: $80 billion this year. But the generation of engineers who knew how to build efficient, lean infrastructure from scratch? We stopped training them a decade ago. You can't repatriate what you can't rebuild. And you can't rebuild efficiently if the software running on top demands ten times the resources it should. I've been watching this from both sides. I architect Azure environments during the day. At night, I run my own infrastructure. I'm migrating my email into a European data centre in Helsinki. My monitoring runs on a Raspberry Pi - hardware that costs less than a month of Teams licensing. The cloud isn't going anywhere. But the assumption that everything belongs there, that infinite scale is infinite, that someone else's data centre is always the right answer? That assumption is running out of power. Literally. www.readthemanual.co.uk #digitalsovereignty #selfhosting #homelab #azure

🚨 Enterprises Are Pushing Back on Cloud: Here’s Why ☁️💡 Over the past few years, the cloud became the go-to solution for enterprises—promising flexibility, scalability, and cost savings. But today, we’re at a turning point. Many CIOs, IT leaders, and enterprises are rethinking their cloud strategies. Why? Because the reality of cloud adoption isn’t always aligning with the expectations. In my latest article, I dive deeper into the growing frustrations enterprises have with cloud providers like AWS, Google Cloud, and Microsoft Azure. Here's what you need to know: 🔴 Cost Overruns and Hidden Fees: Did you know 81% of enterprises exceed planned cloud budgets? Between egress fees, scaling charges, and other unexpected costs, businesses are struggling to rein in cloud expenses. 🔴 Lock-In Challenges: Flexibility was supposed to be a core benefit of the cloud, but many enterprises are feeling trapped by vendor lock-in and eye-watering migration costs. 🔴 The Move to 'Cloud Smart': Enterprises are no longer jumping "all-in" with cloud-first strategies. Instead, they are adopting a more strategic approach, balancing workloads between the cloud, hybrid models, and private infrastructure based on specific needs. 🔴 Talent Gaps: 40% of enterprises report struggling to find skilled cloud professionals, making it hard to optimize and fully leverage cloud investments. But this isn’t a story about abandoning the cloud—it’s about rethinking it. Enterprises are taking greater control, scrutinizing their provider relationships, and exploring innovative strategies to avoid surprises and build sustainable IT ecosystems. 👉 Read the full article to learn why enterprises are pushing back on cloud providers—and what this means for the future of IT. 💬 Let’s start a conversation. How is YOUR organization approaching the challenges of rising cloud costs, lock-in, and the evolving demands of AI and workloads? #CloudComputing #CloudSmart #CIOInsights #EnterpriseIT #DigitalTransformation

I’m coming out of #HRTechConf with one big realization, we are in trouble, my friends. I could write a hype post about the shiny things, but what’s really on my mind is the gap I saw in us as analysts, buyers, and users. We used to understand tech. We spent years mastering integrations, if/then logic, and workflows. But those aren’t today’s rails. The ground has shifted and most of us haven’t learned how to move with it. If your mental model is pre-AI, you’re thinking in legacy code. The rails have been rebuilt. Data flows, system connections, and decision-making inside the machine are fundamentally different. Keep buying and building like it’s 2019 SaaS, and you’re laying train tracks for a world that now flies. How the tech rails have changed 1. Streaming-First Data Architecture The old way: nightly ETL batches. The new way: event-driven streaming using tools like Kafka or Pulsar feeding real-time feature stores and triggering AI inference in milliseconds. 2. Embeddings Are the New Index Forget tables and joins. Modern systems embed everything into high-dimensional vectors stored in vector databases such as Pinecone or Weaviate. This enables semantic search, personalization, and context retrieval at scale. 3. Retrieval-Augmented Generation Is the Default Applications no longer rely on static data calls. They dynamically retrieve relevant knowledge, inject it into prompts, and let LLMs reason in context. 4. Agents and Orchestrators Run the Show Hardcoded workflows are out. Multi-agent frameworks like LangGraph or CrewAI coordinate tasks in adaptive loops: plan, retrieve, act, evaluate, re-plan. 5. Microservices Meet Model Endpoints Traditional APIs are being joined by model endpoints that generate outcomes, not just retrieve data. Latency, token budgets, and model choice are now architectural decisions. 6. Continuous Feedback Loops Models are never “finished.” Fine-tuning, RLHF, and automated monitoring are constant. Your software stack evolves week by week. 7. Semantic Layers Beat Point Integrations Instead of brittle point-to-point API wiring, systems rely on shared semantic layers or knowledge graphs so data flows with meaning, not just schema. 8. Inference Moves to the Edge Smaller, quantized models run on devices and edge nodes for faster response times, improved privacy, and lower cost. This changes infrastructure from central to hybrid. 9. Security and Governance Go Deep Beyond firewalls, you now need prompt injection defenses, output filters, audit logs for model calls, and bias and drift monitoring dashboards. 10. Time to Deployment Is Hours, Not Quarters Containers spin up in minutes, pipelines auto-configure, and models hot-swap. A six-month rollout is no longer competitive. If you do not understand these rails, you cannot: •Ask vendors the right questions. •Connect systems to deliver compounding intelligence. •Govern for risk in a world where models shift under your feet.

Some of the most interesting developments in enterprise technology right now are happening behind the scenes, in the infrastructure powering businesses. 2026 heralds a new norm in cloud computing. Hybrid, multi-cloud, private and sovereign cloud models are becoming fundamental to how organizations build resilient, AI-powered systems. After years of migration and modernization, the cloud has shed its reputation as a cost center. It is now a strategic enabler of speed, autonomy, and competitive advantage. This is a necessary shift for forward-looking companies. Modern AI and agentic workloads demand more than single centralized cloud platforms can offer. The transformation happening today is building the operational resilience of tomorrow. Cloud 3.0 is enabling the next decade of intelligent enterprise architectures. But organizations will need to ensure they are equipped with the right skills, agile governance and adaptive mindset that enable confident operations across diverse cloud environments. I’ll be sharing more of the thinking behind Capgemini’s Top Tech Trends for 2026 in the coming weeks and you can read more here: https://lnkd.in/eJn-JxxH In the meantime, I’d love to hear in the comments how your organisation is thinking about multi-cloud strategies.