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?

AWS on AI: “As Fast as We Add Capacity, It’s Being Consumed” Amazon plans to continue to invest heavily in infrastructure for its AI and cloud businesses, CEO Andy Jassy said in the company’s Q1 earnings call Thursday. ”Our AI business right now is a multi-billion dollar annual run rate business,” said Jassy. “It’s growing triple digit percentages year over year. And as fast as we actually put the capacity in, it’s being consumed.” Jassy said AI infrastructure represents a long-term investment in business transformation for Amazon Web Services (AWS) and its customers. “If you believe your mission is to make customers’ lives easier and better every day, and you believe that every customer experience will be reinvented with AI, you’re gonna invest very aggressively in AI,” said Jassy. “And that’s what we’re doing. Before this generation of AI, we thought AWS had the chance to ultimately be a multi-hundred billion dollar revenue run rate business. We now think it could be even larger.” Jassy also said that AI should be seen as part of the larger story of cloud computing’s disruption of enterprise IT. “For companies to realize the full potential of AI, they’re going to need their infrastructure and data in the cloud,” he said. “It’s useful to remember that more than 85% of the global IT spend is still on premises, so not in the cloud yet. It seems pretty straightforward to me that this equation will flip in the next 10 to 20 years.”

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 rising AI tide lifts all clouds. Especially the largest ones. Amazon reported an exceptional quarter yesterday: AWS reaccelerated, custom silicon gained traction, retail and ads continued to compound, and it saw demand strong enough to justify one of the largest capex programs in history. That explains why investors looked past negative FCF. Shares are up ~15%. 1. AWS has moved from recovery to reacceleration AWS revenue reached $42B, +37% YoY and sharply accelerating 28% growth. This was AWS’s fastest growth in 18 quarters. It now operates at a $169B revenue run rate, with ~40% operating margin. It generates ~61% of total operating profit despite contributing only 21% of revenue. Amazon described a direct relationship between AI growth and its “core” cloud business: as customers build AI systems, their broader AWS consumption rises alongside it. The AI boom is therefore becoming a cloud-migration catalyst. 2. Amazon is becoming a vertically integrated AI systems company Amazon is assembling an increasingly integrated AI system: data centers, power, networking, Trainium accelerators, Graviton CPUs, cloud, model access through Bedrock, agent deployment through AgentCore, and applications such as Kiro, Amazon Q and Continuum. The more interchangeable models become, the more valuable the surrounding system becomes. Jassy noted: “A production agent needs somewhere secure to run, memory so it holds context, an identity so it can act on a user’s behalf, tools and data to connect to, and a way to watch what it’s doing once real traffic hits.” That description doubles as AWS’s roadmap. Bedrock added more customers during the past 6 months than it did during its first 2 years after launch. Customers spent more on Bedrock last quarter than in every previous quarter combined. 3. All chips are on the table Amazon has multi-year, multi-GW Trainium commitments from Anthropic and OpenAI, alongside adoption by Uber, Pinterest, Poolside and a growing roster of AI companies. Custom silicon matters for 3 reasons: it lowers Amazon’s dependence on Nvidia, it can expand AWS margins, and it can become a customer-acquisition tool. Amazon can use Trainium to win the anchor workload, then monetize everything surrounding it: storage, databases, CPUs, networking, security, inference, and development tooling. Graviton reinforces the strategy. Amazon said its custom Arm-based CPU is used by 98% of its top 1,000 EC2 customers, while revenue increased ~3x QoQ. Amazon raised its 2026 capex to $220B, up from its prior $200B plan and from $128B in 2025. To put this in perspective, $220B is: > than the annual revenue of most F100 companies ~28% of Amazon’s trailing-12-month revenue ~$600M of investment per day ~2x Amazon’s trailing operating income But given the growth rebound, investor appetite for capex seems to have returned. The “one model to rule them all” thesis is fading. Model abundance creates competition above the cloud but consumption within it.

Amazon announced their earnings yesterday. Like Microsoft & Google, Amazon’s Web Service business is seeing a surge of growth, up from 13% annual to 17% annual growth (16% when excluding the leap year). Aside from the overall growth of these clouds increasing, the massive investment in CapEx data centers, power plants, and GPUs is stunning. These are not one-time investments, but part of a broader trend that started to occur after the introduction of GPT 3 in mid-2020 Amazon was the first to invest significantly. Google and Microsoft would wait another two years to replicate a similar level of investment. Each of these businesses are large enough to justify it. Here are some highlights from Amazon’s earnings : “We see considerable momentum on the AI front where we’ve accumulated a multibillion-dollar revenue run rate already.” Over time, we should expect Amazon and Google, amongst others, to start to compete with Nvidia GPUs, offering their own which should meaningfully improve margins. “We have the broadest selection of NVIDIA compute instances around, but demand for our custom silicon, Trainium and Inferentia, is quite high given its favorable price performance benefits relative to available alternatives. Larger quantities of our latest generation Trainium2 is coming in the second half of 2024 and early 2025.” And those margins are increasing for the clouds, which should catalyze more companies, especially the largest spenders, to think about managing their own infrastructure. 8 percentage points increased margins in a quarter is titanic. “AWS margins increased 8 basis points sequentially off Q4” If we needed any reminder, Amazon Web Services is now a $100 billion runway business, growing 17% a year, or adding $150b in market cap per year at a 9x multiple. “Moving to AWS. Revenue was $25 billion, an increase of 17% year-over-year, and AWS is now a $100 billion annualized revenue run rate business. "

I listened to the 2nd half and the Q&A section of Amazon's Q3 2024 earnings call. It's fascinating how generative AI is fueling AWS margins. However, Amazon needs to keep CapEx in check to avoid future problems. Q3 2024 earnings reveal the company’s continued strong growth in key areas, notably AWS and digital advertising. ++ My Key Highlights ++ 📍Amazon Web Services (AWS) achieved revenue of $27.5 billion, marking a 19.1% year-over-year growth. This robust increase highlights the ongoing demand for cloud solutions and the need for AI. With an annualized run rate of $110 billion, AWS maintains its position as a dominant player in the cloud market. 📍AWS reported a significant operating income of $10.4 billion, increasing by $3.5 billion year-over-year, reflecting a 38% margin driven by optimized infrastructure costs and server lifespan extension efforts. 📍Amazon’s advertising revenue reached $14.3 billion, growing 18.8% year-over-year. This increase underscores Amazon’s success in leveraging its extensive user base and AI-driven targeting capabilities to create a robust advertising ecosystem. ++ Financial Figures ++ Revenue: $158.9 billion, up 11% year-over-year, excluding foreign exchange impact Operating Income: $17.4 billion, up 56% YoY Free Cash Flow: $46.1 billion, up 128% YoY North America Sales Growth: 9% YoY International Sales Growth: 12% YoY Advertising Revenue: $14.3 billion, 18.8% YoY growth AWS Revenue: $27.5 billion, 19.1% YoY growth AWS Annualized Run Rate: $110 billion North America Operating Margin: 5.9%, up 100 basis points YoY International Operating Margin: 3.6%, up 390 basis points YoY AWS Operating Income: $10.4 billion, up $3.5 billion YoY Capital Investments Year-to-Date: $51.9 billion Expected CapEx for 2024: Approximately $75 billion ++ What It All Means for CPGs++ We're talking to global CPG leaders in commerce, media, brand and data & analytics teams, all around the world. We repeatedly come to this conclusion together. As digital and AI-driven transformations reshape consumer interactions, Amazon’s growth in AWS and digital advertising points to two critical avenues for CPG brands: 💡Invest in Cloud-Based Data Analytics and AI: AWS’s role in enabling scalable, data-driven solutions is essential for brands looking to enhance customer insights, drive personalization, and optimize supply chains. 💡Utilize Amazon’s Expanding Ad Ecosystem: Amazon's advertising growth reinforces its platform’s impact on consumer reach. For CPG brands, utilizing Amazon’s digital advertising capabilities offers a powerful route to targeted marketing and measurable engagement. 𝗧𝗼 𝗮𝗰𝗰𝗲𝘀𝘀 𝗮𝗹𝗹 𝗼𝘂𝗿 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀, 𝗳𝗼𝗹𝗹𝗼𝘄 ecommert®, 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝘁𝗼 𝗼𝘂𝗿 𝟭𝟬,𝟱𝟬𝟬+ 𝘀𝘁𝗿𝗼𝗻𝗴 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 👇 #data #ArtificialIntelligence #CPG #ecommerce Microsoft Azure Google Cloud Security Google Microsoft Alibaba Cloud IBM Oracle Cloud Salesforce Tencent Cloud

Amazon Web Services (AWS) is going all-in on infrastructure — and the numbers are huge. We’re witnessing the largest infrastructure investment cycle in the company’s history — and it’s being driven by AI. Amazon confirmed plans to spend over $100 billion in CapEx this year. Amazon CEO Andy Jassy said during the Q1 2025 earnings call: “It’s useful to remember that more than 85 percent of the global IT spend is still on premises, so not in the cloud yet. It seems pretty straightforward to me that this equation will flip in the next 10 to 20 years. Before this generation of AI, we thought AWS has a chance to ultimately be a multi-100-billion-dollar-revenue run rate business. We now think it could be even larger.” “If you believe your mission is to make customers’ lives easier and better every day, you believe that every customer experience will be reinvented with AI. You’re going to invest very aggressively in AI. And that’s what we’re doing.” “While we offer customers the ability to do AI with multiple chip providers, and will for as long as I can foresee, customers doing AI at any significant scale realize that it can get expensive quickly. For AI to be as successful as we believe it can be, the price of inference needs to come down significantly. We consider this part of our mission and responsibility to help make it so.” “Our AI business has a multi-billion-dollar annual revenue run rate [and] continues to grow triple digit year over year percentages. And it’s still in its very early days. While there is good reason for the high optimism about AI, I conclude my AWS comments with a reminder that there is still so much on-premises infrastructure yet to be moved to the cloud. Infrastructure modernization is much less sexy to talk about than AI, but fundamental to any company’s technology and invention capabilities [and] developer productivity, speed, and cost structure. And for companies to realize the full potential of AI, they’re going to need their infrastructure and data in the cloud.” “I think we could be helping more customers drive more revenue for the business if we had more capacity. We have a lot more Trainium2 instances and the next-generation of Nvidia instances landing in the coming months. There are other parts of the supply chain that are a little bit jammed up as well, motherboards and some other componentry, and some of that just because there is so much demand right now. But I do believe that the supply chain issues, the capacity issues, will continue to get better as the year proceeds.” #ai #digitalinfrastructure https://lnkd.in/gYWGbkfG

Quantum-classical computing is transforming how we approach complex challenges by combining cloud-based quantum acceleration with traditional systems to improve speed, precision, and efficiency while enabling scalable innovation. This convergence is a key step toward making quantum computing a practical tool rather than a distant goal. Hybrid architectures allow organizations to experiment safely through APIs and SDKs that connect classical systems with Quantum Processing Units in the cloud. This setup reduces the barrier to entry and opens new paths for optimization, simulation, and predictive modeling in sectors such as finance, logistics, and materials science. Early experimentation is essential. Small proof-of-concept projects can reveal measurable gains in performance and cost-efficiency while helping teams develop internal expertise and new partnerships. Each iteration builds readiness for a future in which quantum computing will become an integral part of enterprise infrastructure. #QuantumComputing #DigitalTransformation

The next few years are going to be tough. Many legacy applications finally need to be modernized.  10 actions to survive. 1. Focus: Not every functionality needs to be migrated. Strict scope management based on real customer needs is crucial. What's your approach to scope prioritization? 2. Outcome-driven: Delivered functionality isn't the main success criterion - improved business value is. In my last project, we delivered 18% more revenue with just 60% of the migrated functionality. What metrics matter most in your modernization efforts? 3. Data-driven: Validate the value of each delivered feature through A/B testing. Combine quantitative data with user stories to paint the complete picture. 4. Incremental and iterative: From month one, deploy continuously to production through a robust delivery pipeline. Daily releases should be your minimum target. Agile and DevOps work. 5. Fail fast: Build and validate technically risky and commercially important functionalities first. Minimize basic functionality. Effectiveness before efficiency. 6. Experience-based: Don't reinvent the wheel. Learn from others who've succeeded. Shamelessly adopt state-of-the-art practices that work. 7. Human-centric: Your employees are critical to success. They understand customer needs, business processes, and legacy systems. Blend their experience with external expertise and invest in change management. 8. Be adaptable: We plan, God laughs. Observe, reflect, and adapt regularly at every organizational level. Stay self-critical and embrace change. 9. Cost-aware: Modernization isn't just about technology - it's about business value. Track and communicate both investment and returns. Create transparency about technical debt reduction and new revenue opportunities. 10. Future-proof: Design for change, not just today's requirements. Choose modern, maintainable architectures and build technical excellence into your culture. Microservices aren't dead. Which of these measures resonates most with your experience? What would you add to this list? Share your thoughts in the comments!

Cloud computing infrastructure costs represent a significant portion of expenditure for many tech companies, making it crucial to optimize efficiency to enhance the bottom line. This blog, written by the Data Team from HelloFresh, shares their journey toward optimizing their cloud computing services through a data-driven approach. The journey can be broken down into the following steps: -- Problem Identification: The team noticed a significant cost disparity, with one cluster incurring more than five times the expenses compared to the second-largest cost contributor. This discrepancy raised concerns about cost efficiency. -- In-Depth Analysis: The team delved deeper and pinpointed a specific service in Grafana (an operational dashboard) as the primary culprit. This service required frequent refreshes around the clock to support operational needs. Upon closer inspection, it became apparent that most of these queries were relatively small in size. -- Proposed Resolution: Recognizing the need to strike a balance between reducing warehouse size and minimizing the impact on business operations, the team developed a testing package in Python to simulate real-world scenarios to evaluate the business impact of varying warehouse sizes -- Outcome: Ultimately, insights suggested a clear action: downsizing the warehouse from "medium" to "small." This led to a 30% reduction in costs for the outlier warehouse, with minimal disruption to business operations. Quick Takeaway: In today's business landscape, decision-making often involves trade-offs.  By embracing a data-driven approach, organizations can navigate these trade-offs with greater efficiency and efficacy, ultimately fostering improved business outcomes. #analytics #insights #datadriven #decisionmaking #datascience #infrastructure #optimization https://lnkd.in/gubswv8k