🚀 Excited to share the launch of Amazon Aurora Serverless's new platform version — delivering up to 30% better performance, 45% faster scaling, and a smarter autoscaling algorithm. Proud of the incredible work the team has done. Kudos 🎉

S3 just became a high-performance File System. Stop choosing between S3 scalability and file system speed. With Amazon S3 Files, your compute resources and AI agents can finally work with S3 data natively – no silos, no sync lag, and no code changes. The Bottom Line: 🪣 90% Lower Costs – Stop paying to move data between systems. 🪣 10M+ IOPS – Enterprise-grade performance for any workload. 🪣 4TB/s+ Throughput – Massive scale for ML and AI pipelines. 🪣 25,000 Connections – Simultaneous access across EC2, EKS, and Lambda. 🪣 Zero Migrations – Works with your existing buckets and tools today. Stop moving your data. Start scaling it. Read the full breakdown on our blog: https://lnkd.in/d4jV68H2 #AWS #CloudArchitecture #S3 #DataOps #Cloudride

AWS just raised EC2 Capacity Block pricing by 15%. Every region. Every ML instance type. p4d, p5, trn1, all of them. If your team reserves GPU capacity for training or inference, your next invoice is going up. No grandfathering. This matters because most teams budget GPU costs based on last quarter's rates. A 15% jump mid-cycle breaks forecast accuracy and blows through committed budgets before anyone notices. Three things to check right now: 1. Review your Capacity Block reservations. Are you actually using the full reserved window, or paying for GPU hours that sit idle overnight and on weekends? 2. Compare on-demand vs Capacity Block vs Savings Plans for your ML workloads. The math just changed. For bursty training jobs, on-demand might now be cheaper than reserving. 3. Check your inference fleet. If you scaled up GPU instances for a model launch and never scaled back down, that 15% compounds on waste you already had. We built Akal Cloud to surface exactly this kind of cost drift. Ask Akal "what are we spending on GPU instances this quarter vs last?" and it shows the delta from live data, not a CUR file from three days ago. When was the last time you re-evaluated your GPU reservation strategy? #AkalCloud #AWS #EC2 #MachineLearning #FinOps

🚨 Amazon Web Services just upgraded S3 ✅ On April 7, 2026, AWS introduced Amazon S3 Files and honestly, this feels like one of the biggest updates S3 has ever had. Until now, S3 was clearly “object storage.” Powerful, but not something you’d treat like a real file system. Here’s what you can do now: → Mount S3 buckets like a normal file system → Get extremely fast (sub-millisecond) access → Use POSIX permissions (UID/GID) natively → Connect directly with Lambda, EC2, and EKS → Skip workarounds like s3fs and data staging Your applications even AI workloads can read and write to S3 just like it’s a local disk. At the same time, your data team can still use APIs on the same data. No duplication. No extra pipelines. No friction. If you’re in DevOps, data engineering, or building AI systems, this is a big relief.

Big update from Amazon Web Services 🚀 On April 7, 2026, AWS introduced Amazon S3 Files—and this might be one of the most significant upgrades to S3 so far. Until now, S3 has been known as powerful object storage—but not something you could truly use like a traditional file system. That changes now 👇 With S3 Files, you can: → Mount S3 buckets just like a local file system → Experience ultra-fast, sub-millisecond access → Use native POSIX permissions (UID/GID) → Integrate directly with services like Lambda, EC2, and EKS → Eliminate workarounds like s3fs and data staging The biggest win? Your applications—including AI workloads—can now read and write data to S3 as if it were a local disk. Meanwhile, data teams can continue accessing the same data via APIs. ✅ No duplication ✅ No extra pipelines ✅ No unnecessary complexity This is a big step toward unifying storage and compute in a much more seamless way. #AWS #CloudComputing #S3 #DevOps #AI #CloudInnovation

Moving AlphaFold to AWS changes the experience: provision the right GPU, keep terabytes of databases warm, and scale from a single user to a multi-user service.

Amazon S3 Files: S3 now behaves like a file system For years, teams had to choose: S3 → scalable but API-driven File systems → easy access but limited scale This led to data duplication and complex pipelines. 💡 Amazon S3 Files changes that You can now access S3 buckets with file system semantics—no data movement, no rework. 🔑 What you get: Single source of truth High throughput (TB/s scale) Massive concurrency Zero migration 🎯 Impact: Simpler architectures, faster analytics & AI/ML workflows. #AWS #S3 #Cloud #DataEngineering #AI

Amazon CloudWatch introduces Container Insights with OpenTelemetry metrics for Amazon EKS, available in public preview. Building on the existing Container Insights experience, this capability provides deeper visibility into EKS clusters by collecting more metrics from widely adopted open source and AWS collectors and sending them to CloudWatch using the OpenTelemetry Protocol

AWS just introduced Amazon S3 Files and this is genuinely exciting. For the first time, you can work with Amazon S3 data using file-like access: • cd, ls, read, write • Direct access from EC2, containers, or functions • No need to move data across storage layers 💡 What makes this powerful: You can now process data directly where it lives in S3. That means: • Simpler architectures • Less data duplication • Better cost efficiency ⚠️ Of course, S3 is still object storage so behavior won’t fully match Amazon EFS. But this is a big step toward bridging that gap. Really looking forward to exploring real-world use cases of this 🚀 #AWS #CloudEngineering #DevOps #S3