Cloud Computing Benefits for Startups

Microservices won’t fix bad design—they’ll amplify it. Splitting a monolith into microservices doesn’t magically solve scaling issues. It introduces network latency, data consistency challenges, and operational complexity. Before going micro, ask: • Does each service have a clear, single responsibility? • How will services communicate—sync or async? • Can failures be isolated without breaking the whole system? • Have we properly defined bounded contexts? • Who will be the owner of that new service? • Is the team structured for microservices, or will this cause silos and slowdowns? • Can you afford it? Microservices work best when your architecture demands it, not when hype drives it. Choose wisely.

Oracle rolled out a major agentic AI release. It introduced 𝐎𝐫𝐚𝐜𝐥𝐞 𝐅𝐮𝐬𝐢𝐨𝐧 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬  — a new class of enterprise applications designed to help AI agents work inside real business workflows, not around them. A few details worth adding beyond what I covered in the video: 𝐅𝐨𝐫 𝐅𝐢𝐧𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐒𝐮𝐩𝐩𝐥𝐲 𝐂𝐡𝐚𝐢𝐧 — helping teams move from manual follow-ups and fragmented handoffs to more proactive execution across collections, claims settlement, cost accounting close, sourcing, logistics, warehouse operations, and sales order exceptions. 𝐅𝐨𝐫 𝐇𝐑 — focused on workflows like hiring, workforce operations, manager support, employee help, team learning, career advancement, and talent review. 𝐅𝐨𝐫 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 — built for sales, service, and marketing processes, including sales command centers, service management, cross-sell programs, marketing workflows, and contract compliance. 𝐎𝐫𝐚𝐜𝐥𝐞 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐒𝐭𝐮𝐝𝐢𝐨 + 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐁𝐮𝐢𝐥𝐝𝐞𝐫 — gives companies a way to build, customize, connect, and run their own agentic workflows using Oracle, partner, and external agents. The broader point is simple: Enterprise AI becomes much more useful when it understands the systems, rules, approvals, and workflows it is expected to operate inside. 📍Full release here: https://lnkd.in/gwD7kvJd Oracle AI Database #oraclepartner

Fact of the day… if built with a frugal mindset, private cloud in the right location/DC is approximately 71% cheaper than the equivalent public cloud environment with 79% lower emissions. This is the result of a study we’ve just finished for an enterprise org looking at cloud repatriation for approx 50% of their workloads - the less “bursty” or dynamic apps (I.e. the dull yet surprisingly large environments). Assumptions they used in the model: 1) Public cloud would be deployed how they currently consumed it (based on their reality not marketing pipedreams), located in Dublin, London and Frankfurt. 2) New collocation DC’s would be in South Scotland and either Sweden or Paris. 3) Backup/DR delivered in region by local MSP. 4) Infrastructure utilisation would be maxed with an 80% operating threshold with “on demand” provisioning by vendor to maintain spare capacity. 5) Service would be managed by a 3rd party up to the OS. 6) Prod and Dev environments would be split, with very different SLAs/SLOs to reduce costs 7) Assets would be sweated for 6 years (where practical). 8) Automated deployment patterns “cloud like” consumption (very useful for auto terminating dev instances which were more than 45% of their usage). Conclusion… don’t believe the hype from public cloud marketing. You can actually accelerate meeting your net zero targets with a bit of lateral thinking. Cloud is not always the solution you think it is. Ironically over consumption of public cloud is likely increasing your digital emissions. Finally, please buy from local providers. You’re not just helping the environment, but you’re also helping local tech firms to grow, creating jobs and helping society. #greenops #cloud #scope3

On-prem is back. Not for nostalgia. For physics, pricing, and paperwork. Data has gravity. Petabytes don’t like to travel. It’s faster and cheaper to bring compute to the bytes than to ship the bytes around. Privacy and sovereignty still bite. Auditors ask two questions: where does the data sleep, and who holds the keys. If the answers are “here” and “us,” life gets simpler. GPUs are roulette. If you need H100s every day, renting at surge rates is a tax. Buy or colocate and get on with it. Modern on-prem isn’t 2009. Thin control plane in the cloud, fat data plane on site. Kubernetes, GitOps. Hot data local; cold archive upstairs. Bottom line: hybrid by default, use cloud for spikes and experiments, keep on-prem for the big, steady, data-near jobs. Auditors in the room: what do you really care about?

Microsoft’s AI agent ecosystem is becoming a complete stack. Building an enterprise agent is no longer only about choosing a model. It requires development tools, orchestration, runtime infrastructure, enterprise data, security, monitoring, and business applications working together. Here is how the Microsoft Azure ecosystem fits together: 𝗖𝗼𝗿𝗲 𝗔𝗴𝗲𝗻𝘁 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀 Microsoft Foundry, Foundry Agent Service, Copilot Studio, Agent 365, Power Platform, and GitHub Copilot help teams design and deliver agents. 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗮𝗻𝗱 𝗦𝗗𝗞𝘀 Semantic Kernel, Microsoft Agent Framework, Foundry SDK, Microsoft 365 Agents SDK, and developer tools support custom agent development. 𝗥𝘂𝗻𝘁𝗶𝗺𝗲 𝗮𝗻𝗱 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀 Container Apps, AKS, Functions, Logic Apps, API Management, Service Bus, Microsoft Graph, and Power Automate connect agents with business systems. 𝗠𝗼𝗱𝗲𝗹𝘀, 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲, 𝗮𝗻𝗱 𝗠𝗲𝗺𝗼𝗿𝘆 Foundry Models, Azure OpenAI, Phi, Foundry IQ, AI Search, Cosmos DB, Redis, Fabric, OneLake, and Blob Storage provide intelligence and grounding. 𝗣𝗿𝗲𝗯𝘂𝗶𝗹𝘁 𝗔𝗴𝗲𝗻𝘁𝘀 Microsoft offers specialized agents across research, analytics, security, coding, sales, customer service, and data workflows. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗔𝗴𝗲𝗻𝘁𝗢𝗽𝘀 Entra ID, Key Vault, Purview, Defender for Cloud, Content Safety, Azure Monitor, and Application Insights provide control and visibility. The simple way to remember it: Build. Connect. Ground. Secure. Monitor. Scale. Microsoft’s agent ecosystem spans the complete lifecycle, from model selection and development to deployment, governance, and enterprise adoption. Which part of the Microsoft AI agent stack is your team exploring first?

Following strong interest from public administrations and IT providers, we have published further clarification on our Cloud Sovereignty Framework – a key tool used by the European Commission in its recent sovereign cloud procurement. ☁️   By embedding sovereignty directly into cloud acquisition, the Commission set a benchmark for secure and values-based digital infrastructure in Europe. What makes the Framework particularly significant is its structured approach to evaluating sovereignty through two complementary mechanisms:   🔹 The Sovereignty Effectiveness Assurance Level (SEAL) that measure levels of sovereignty and resilience. 🔹 An overall sovereignty score based on 48 criteria including strategic, legal and jurisdictional ones, data and AI, operational, supply chain, technological, security and compliance, as well as environmental sustainability.   This Framework sends a strong signal to the market: sovereignty is no longer an abstract policy discussion, but an operational requirement in public procurement.   Find the implementation guidance here 👉 https://lnkd.in/eWQRn74x

We analyzed the tech stacks of 17 successful AI startups that raised $120M+ in 2024. This is what ACTUALLY correlates with fundraising success: 𝗙𝗿𝗼𝗻𝘁𝗲𝗻𝗱: • Next.js dominated (13/17 startups) • Tailwind CSS for styling (11/17) • 4 used ShadCN UI components • 3 used Chakra UI • TypeScript was universal 𝗕𝗮𝗰𝗸𝗲𝗻𝗱: • Python with FastAPI (8/17) • Node.js with Express (6/17) • 3 used Go for performance-critical microservices • PostgreSQL was the primary database (10/17) • Most used a combination of SQL + vector DBs (Pinecone/Weaviate) 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁: • 11/17 deployed on AWS • 5 chose Vercel + AWS combination • CI/CD with GitHub Actions (14/17) • Docker was universal, Kubernetes was rare (only 3/17) • 13/17 used serverless for at least part of their stack 𝗔𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: • 14/17 used OpenAI APIs as primary models • 5 used Anthropic's Claude for specific features • 6 fine-tuned models on their own data • Only 2 deployed their own open-source LLMs Most interestingly, the data showed ZERO correlation between technology sophistication and funding success. What DID correlate? • Time to initial user feedback (strongest correlation) • Weekly deployment frequency • Time from idea to revenue Our client who raised $500K built on: • FastAPI backend with PostgreSQL + pgvector • Next.js frontend with Tailwind • LangChain for AI orchestration • OpenAI API with fine-tuned RAG • Vercel for frontend, AWS Lambda for backend Build cost: $25K Time to market: 6 weeks What they DIDN'T waste time on: • Complex microservices architecture • Training custom foundation models • Custom UI frameworks • Premature optimization for scale TAKEAWAY: The founders who raised successfully concentrated engineering hours on their core AI differentiation, not rebuilding infrastructure that already exists. What's your experience with early-stage AI stacks? Have you seen similar patterns?

𝐀𝐳𝐮𝐫𝐞 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐋𝐚𝐧𝐝𝐢𝐧𝐠 𝐙𝐨𝐧𝐞 𝐟𝐨𝐫 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 Most teams think Deploying AI on Azure means spinning up a Model Endpoint. It does not. At Enterprise Scale, Agentic AI requires Identity Isolation, Governance Controls, Networking Architecture, and Operational Guardrails built in from Day-1. Here is what a Production-Grade Azure Landing Zone for Agentic AI actually includes: 𝟏. 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐚𝐧𝐝 𝐓𝐞𝐧𝐚𝐧𝐭 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧     - Microsoft Entra ID for identity control   - Integration with on-prem Active Directory when required  This is the control plane for everything that follows. 𝟐. 𝐈𝐝𝐞𝐧𝐭𝐢𝐭𝐲 𝐚𝐧𝐝 𝐀𝐜𝐜𝐞𝐬𝐬 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭  - Privileged Identity Management for elevated roles   - Custom roles for DevOps and AI teams  Without strict IAM, autonomous agents become uncontrolled automation. 𝟑. 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 - Microsoft Sentinel   - Log Analytics workspace   - Role and policy assignments  Centralized visibility across all AI workloads. 𝟒. 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐯𝐢𝐭𝐲    - ExpressRoute   - VPN gateways   - Virtual network peering   - Private DNS resolver  Agents calling APIs and tools must operate inside controlled network boundaries. 𝟓. 𝐋𝐚𝐧𝐝𝐢𝐧𝐠 𝐙𝐨𝐧𝐞 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧𝐬    - Virtual networks per region   - DNS, UDRs, NSGs, ASGs   - Azure Key Vault   - Storage accounts   - Backup and recovery  This is where agentic AI workloads actually run. 𝟔. 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐃𝐞𝐯𝐎𝐩𝐬 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧    - Git repositories   - Boards and wiki   - Deployment pipelines   - Role and policy templates  Infrastructure and AI deployment must be reproducible. 𝟕. 𝐒𝐚𝐧𝐝𝐛𝐨𝐱 - Application isolation   - Policy and role controls  Safe experimentation before production rollout. 𝟖. 𝐒𝐞𝐜𝐮𝐫𝐞 𝐀𝐈 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐀𝐬𝐬𝐞𝐭𝐬     - Protect model weights and APIs   - Backup policies   - In-guest policies and configuration enforcement  AI systems are infrastructure. Treat them like crown jewels. 𝐓𝐡𝐞 𝐫𝐞𝐚𝐥 𝐥𝐞𝐬𝐬𝐨𝐧 𝐢𝐬 𝐭𝐡𝐢𝐬. Agentic AI is not just a model.   It is a distributed system. And distributed systems require architecture discipline. Landing zones are not overhead.   They are the foundation that allows AI agents to scale without breaking governance, security, or compliance. If your AI does not have a landing zone, it is not Enterprise-Ready. Reference Microsoft Landing Zone Architecture - https://lnkd.in/eezM3-W5 ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) Karuparti for more PS: If you found this valuable, join my weekly newsletter where I document the real-world journey of AI transformation. ✉️ Free subscription: https://lnkd.in/exc4upeq #Azure #EnterpriseAI #AIAgents

The End of Cloud-Only AI: New Survey Shows 79% of Enterprises Shifting to Hybrid and On-Premises I’ve been reviewing the results of a recent survey of 432 enterprise executives on where they plan to run their AI systems. The findings are clear and point to a significant shift in enterprise thinking. Only 11% of organizations prefer a cloud-first approach. In contrast, 48% favor hybrid-first and 31% favor on-premises-first — meaning 79% are moving away from cloud-only as their primary strategy. The reasons are straightforward. When asked what drives their deployment decisions, executives ranked the following as most important: security and privacy, regulatory compliance, cost predictability, and data residency. Factors like speed to deploy and access to the newest cloud AI services ranked much lower. This suggests enterprises are prioritizing control, governance, and predictable costs over convenience. The data also shows that cloud does not lead in any major workload category. Hybrid is preferred for training, fine-tuning, customer-facing AI, and internal copilots, while on-premises leads for sensitive data and highly regulated workloads. When forced to choose, executives consistently selected control over speed, keeping sensitive data in-house over accessing the latest capabilities, and workload-specific deployment over a single model. This points to a clear pivot: beyond-prime resources are gaining priority over pure cloud deployments, driven largely by cost and security concerns. Over the next five years, I expect hybrid to become the dominant model for enterprise AI, with on-premises remaining essential for sensitive and regulated workloads and cloud playing a supporting, rather than leading, role. I’m not sure all technology providers are fully recognizing this shift yet. What are you seeing in your organizations?

Everyone has access to the same AI now. Almost nobody shows you the hard part: making it work for your business.    Oracle walked me through it, one industry at a time.    What that looks like in practice:    → Public sector: accelerating updates of legacy systems, and getting services to citizens faster  → Utilities and engineering: de-risking huge, complex projects with data you can trace  → Manufacturing: smarter factories that catch maintenance issues before becoming failures  → Financial services: sharper fraud detection and personalisation  → Retail and hospitality: streamlining operations and deepening customer loyalty    Different sectors, different problems. But underneath every single one was the same thing.    It all comes back to your data.    This is what I watched unfold at Oracle's AI Live event in London.     Everyone has access to the same frontier AI now. That part has levelled out.     What no one else has is your customers, your contracts, your operations, the way your specific business actually runs.     That's what the intelligence has to plug into.    I spoke with Hammad Hussain about this, and he dropped a great insight - the 4 D’s:    This is Oracle's answer to why it can work across every industry:    1. Distribution — the clients themselves. Oracle already works across most sectors, so it has the reach.  2. Data — clients trust Oracle with their data, and Oracle is the custodian of it for them.  3. Domain knowledge — just by working with those clients, Oracle understands the workflow and how each business actually operates.  4. Delivery capabilities — Oracle is actually delivering AI to many of them, not just advising.    Rob McCargow at PwC UK also made a great point about the danger of disconnected pilots scattered across the business, with no one joining them up.     That’s why it’s so important to make AI part of the whole organisation's strategy, owned from the top.     The AI is the easy part now. Everyone has it.     The winners will be the ones who treat their own data as the real asset and wire the intelligence into the way they already work, end to end.  That’s where the enduring advantage lies.     Follow me Alex Banks for daily AI highlights and insights.