Anubhav Dhoot

Amazon Web Services (AWS) hiking prices on EC2 Capacity Blocks is less about GPUs and more about certainty. A sharp and telling story by Nidhi Singal Jain in Network World. Thanks for the opportunity to contribute insights. The link to the story is attached, and for deeper analysis, you can head over to greyhoundresearch.com. Here’s how we at Greyhound Research see it. What used to be a niche option has quietly become essential. EC2 Capacity Blocks are not where enterprises spend most of their GPU budget, but they are where they go when failure is not an option. Most training still runs on long-term commitments or on-demand capacity. But when a model retrain is tied to a product launch, a regulatory cycle, or a revenue forecast, certainty matters more than saving a few percentage points. That is why these blocks are increasingly treated like insurance. You may not need them every day, but when everything has to run in a specific window, you need to know the GPUs will actually be there. AI work is no longer experimental. It is tied to business calendars. Miss the window, and you miss the outcome. The price hike itself is logical. AWS is not charging for raw compute. It is charging for predictability. Capacity Blocks sit inside tightly engineered UltraClusters, and holding that space open has real opportunity cost in a constrained market. Guaranteed access is now a premium feature. Other clouds are doing the same thing in different ways. Google uses scheduled reservations. Azure relies on regional capacity holds and long-term commitments. Pricing pressure does not always show up as a visible hike. Sometimes it shows up as quotas, approvals, or long waits. The common shift is this: availability is no longer assumed. For enterprises, this changes behaviour. CIOs are becoming far more deliberate about which workloads deserve guaranteed capacity and which can wait. Critical retraining gets booked. Experiments get pushed elsewhere. Idle time suddenly hurts when capacity is reserved and expensive. No one is abandoning AWS over this. But many are hedging. Core workloads stay put, while smaller jobs get tested across clouds. Not an exodus. A pressure release valve. The bigger change is mental. CIOs are no longer starting with “what does a GPU cost per hour”. They are starting with “Can I get it when I need it?” For 2026 and beyond, certainty has become the real currency of AI infrastructure. https://lnkd.in/gCxZDZbr #GreyhoundStandpoint #AWS #AIInfrastructure #GPUs #EnterpriseAI #CloudStrategy #CIO

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🚨Microsoft Earnings: The AI Adoption Curve Is Vertical Microsoft's latest earnings call was a powerful validation of its AI-first strategy, confirming that enterprise adoption is moving at an unprecedented speed. The message is clear: AI is no longer a pilot project; it's the core engine of cloud growth. 📈 Key Takeaways on Enterprise AI Adaptation The demand for integrated AI tools is exceeding supply, driving massive capital investment. * Fastest Adoption Ever: Microsoft Copilot is seeing the fastest adoption rate of any product in the Microsoft 365 suite's history. * Massive Scale: Major enterprises like Pfizer, KPMG, and Barclays are purchasing tens of thousands of Copilot seats, signaling a profound, company-wide commitment to AI deployment. * Financial Impact: AI workloads are directly responsible for the accelerating growth in the Intelligent Cloud unit, with Azure revenue growth hitting 40%. * The Benefit: Companies are translating AI integration into tangible efficiency gains and cost savings, shifting human focus to higher-value, strategic work. 🤖 The Agentic Future: GitHub and Autonomous Workflows The company is moving beyond simple code assistance toward autonomous AI agents—the next wave of transformation. * Agent Growth: There’s a surging customer base utilizing Copilot Studio and the Foundry AI Agent Service to build and deploy custom, vertical-specific agents (e.g., in healthcare for ambient documentation). * GitHub’s Agent HQ: GitHub is leading the charge in developer AI by launching Agent HQ, a "mission control" interface. This will allow developers to assign, monitor, and manage autonomous coding agents from multiple vendors (OpenAI, Google, etc.) to tackle complex tasks like technical debt modernization. The goal is to bring order to the era of agentic innovation. 🗺️ The Future AI Roadmap: Building the Cloud Factory Microsoft’s roadmap is focused on infrastructure and an open ecosystem. * Massive CapEx: The company is significantly increasing capital expenditure on GPUs, CPUs, and data centers to solidify its position as the "planet-scale cloud and AI factory." This investment is a necessity to meet future demand. * Open Agentic Web: The future relies on an open ecosystem. Microsoft is actively supporting open protocols like the Model Context Protocol (MCP) to ensure that AI agents can communicate, collaborate, and operate securely across different platforms and companies. The message is a validation of the generative AI boom: the enterprise is all-in, and the shift to autonomous agents is the next major wave. What specific business function (e.g., marketing, finance, coding) do you think will see the highest ROI from AI agents in the next 12 months? Share your thoughts below! #AI #Microsoft #MSFT #ArtificialIntelligence #Copilot #EnterpriseAI #GitHub #Azure

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Stop defaulting to REST for every microservice you build. 🛑 6 foundational API architectures govern the modern web. Choosing the wrong style doesn't just impact latency; it severely cripples system scalability. Here is exactly when to use each architecture to optimize performance and prevent system bottlenecks: 👇 1️⃣ REST (Representational State Transfer) 🌐 The undisputed, ubiquitous backbone of web services. ▪️ Mechanism: A stateless, resource-oriented design utilizing standard HTTP verbs (GET, POST, PUT, DELETE). ▪️ Best For: Public-facing APIs, standard CRUD operations, and any application requiring native HTTP caching at the edge. 2️⃣ GraphQL 🎯 The definitive cure for over-fetching and under-fetching. ▪️ Mechanism: A highly flexible query language allowing clients to request the exact data schema they need from a single, unified endpoint. ▪️ Best For: Mobile applications and complex UI dashboards where payload precision and reduced network bandwidth are absolutely paramount. 3️⃣ gRPC ⚡ The powerhouse protocol for low-latency, high-throughput systems. ▪️ Mechanism: Extremely efficient binary data serialization via Protocol Buffers, transmitted over an HTTP/2 multiplexed connection. ▪️ Best For: Internal microservice communication, real-time streaming, and highly distributed systems requiring strict data contracts. 4️⃣ WebSockets 🔄 The standard for true real-time, bi-directional interactivity. ▪️ Mechanism: Establishes a persistent, full-duplex TCP connection between the client and server after an initial HTTP handshake. ▪️ Best For: Live chat platforms, high-frequency financial trading dashboards, and multiplayer online gaming where instant data updates are required. 5️⃣ Webhooks 🎣 The "Reverse API" powering modern event-driven architecture. ▪️ Mechanism: The server automatically pushes data via an HTTP POST request the moment a specific event occurs, completely eliminating inefficient client polling. ▪️ Best For: CI/CD pipelines, third-party payment notifications, and seamless system automation. 6️⃣ SOAP (Simple Object Access Protocol) 🛡️ The legacy heavyweight of enterprise-grade security and reliability. ▪️ Mechanism: A strict, standardized XML-based protocol requiring rigidly defined contracts (WSDL). ▪️ Best For: Highly regulated environments (core banking, healthcare) requiring advanced security mechanisms (WS-Security) and ACID-compliant transactional integrity. 💡 Architectural Takeaway: Robust modern systems rarely rely on a single style. An enterprise platform might utilize REST for its public gateway, gRPC for lightning-fast internal microservice chatter, and Webhooks for asynchronous third-party billing integration. Which API architecture is currently dominating your tech stack? Are you migrating toward gRPC or sticking with the reliability of REST?💬👇 #SoftwareEngineering #APIArchitecture #SystemDesign #Microservices #TechTrends

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APIs are everywhere. But MCP architectures are quietly changing how AI systems scale. If you are building AI products in 2026, this difference matters more than you think. Traditional APIs work well for deterministic workflows. But AI systems need something else. Context. State. Intelligence. 𝐇𝐞𝐫𝐞 𝐢𝐬 𝐭𝐡𝐞 𝐬𝐢𝐦𝐩𝐥𝐞 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 𝐀𝐏𝐈 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: • Request → Response • Stateless by default • Rigid contracts • Great for CRUD and integrations 𝐌𝐂𝐏 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: • Context-aware interactions • Stateful or semi-stateful • Dynamic tool orchestration • Built for AI agents and LLM workflows 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬  • APIs serve applications • MCP serves intelligence systems If you are upgrading AI skills, this is a mindset shift, not just a tool change. Learn both. Use each where it fits. Follow the AIKaDoctor (Free AI & Data Science Resources) channel on WhatsApp: https://lnkd.in/dCTCEKKc Follow Dr. Habib Shaikh, PhD (AI) for more insights

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Part 2 of the series on Using MCP and AWS Bedrock to reduce\ complexity, enabling scalable, tool-driven automation by Sangharsh Agarwal, available today on DZone! https://lnkd.in/exXHTexy

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𝗜𝘁'𝘀 𝗵𝗮𝗿𝗱 𝘁𝗼 𝘀𝗲𝗲 𝗖𝗹𝗼𝘂𝗱𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗗𝗮𝗿𝗸 - 𝗪𝗵𝘆 Redis 𝗠𝘂𝗹𝘁𝗶-𝗖𝗹𝗼𝘂𝗱 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗪𝗵𝗲𝗻 𝘁𝗵𝗲 𝗖𝗹𝗼𝘂𝗱 𝗚𝗼𝗲𝘀 𝗗𝗮𝗿𝗸 When a major cloud goes down, so do the apps, sessions & experiences that depend on it. Outages cost revenue, trust & time. Cloud-native services like ElastiCache or Memorystore work — until their cloud doesn’t. They’re locked to a single provider, with no easy failover across regions or clouds. One outage can take everything with it. That’s why multi-cloud resilience is no longer optional. With Redis’s Active-Active architecture, data stays synchronized across clouds and regions — automatically. - Zero downtime - Seamless failover - Consistent, low-latency performance everywhere When uptime is mission-critical, Redis Multi-Cloud gives you the control & confidence to stay online — no matter who’s offline. Because “always on” should mean always ready for the unexpected. #Redis #MultiCloud #HighAvailability #Resilience #CloudComputing #DevOps https://lnkd.in/emwFT4Xb

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🔄 New in YugabyteDB 2025.1: Automatic DDL Replication via xCluster YugabyteDB now supports automatic schema (DDL) replication in xCluster. Schema changes made on the primary cluster are seamlessly pushed to the replica ... no manual steps needed. This makes it easier than ever to keep multi-cluster deployments in sync. 🚀 Read more here 👉 https://lnkd.in/e_Dfqiec #YugabyteDB #PostgreSQL #DistributedSQL #OpenSource

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As much I like AWS's beautiful cloud ecosystem, I don't like paying 2-3x for slow machines. Even though optically AWS has decent compute prices, they don't add up when the machines are slow. You will end paying lot more for the same amount of work. Most companies don't need the scale that AWS provides. Ecosystems with walled gardens come with their own set of drawbacks like noisy neighbours and vendor lock-ins. Noisy neighbour is a phrase that describes a cloud computing infrastructure co-tenant that monopolises bandwidth, disk I/O, CPU and other resources which can negatively affect other users cloud performance. Checkout this video that shows how AWS can be significantly slower compared to a dedicated VPS of same cost. https://lnkd.in/dnDkmSZ9

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