🚀 Roadrunner SMB Update Big milestone this week for Roadrunner SMB. We’ve crossed the hardest technical boundary in the architecture: 👉 CTDB-based SMB clustering running cleanly inside the dynamic AWS ECS environment — with DynamoDB as the authoritative metadata layer. That means: * no shared TDB files * no fragile NFS-style locking assumptions * no single-node failure domains Just elastic, distributed SMB that scales from 1 to 8 nodes under a single namespace. If you’ve ever tried to run clustered Samba in the cloud, you know how non-trivial this is. ⸻ What’s done ✅ CTDB running reliably in ECS (awsvpc mode, multi-node) ✅ DynamoDB as authoritative ACL store (replacing file_ntacls.tdb) ✅ Cluster coordination stable under fail/recover scenarios ✅ Core architecture validated — production-viable path confirmed ⸻ What’s next 🔲 Performance scale testing/validation 🔲 Final quality and failure-mode validation 🔲 AWS Marketplace seller registration 🔲 Targeting Q2 2026 launch 🚀 ⸻ Why this matters Affordable VDI/DaaS in the AWS cloud: * elastic scaling to petabytes atop EFS * metadata p90 latency < 10 microseconds * no infrastructure to manage * Windows-compatible file share semantics * designed for VDI, home drives, and enterprise workloads ⸻ The goal Deliver the Elastic SMB cloud file service on AWS: 👉 Elastic, enterprise-grade Windows file sharing — without file servers ⸻ More soon. 🚀 #AWS #CloudStorage #SMB #Startup #AWSMarketplace #ECS #Samba

👀 4 years of watching AWS launch new compute tiers. Same pattern every time. 👀 FinOps teams scramble to figure out how to commit without getting burned. Lambda's new 32 GB memory / 16 vCPU support is the latest version of this. It's not a tweak — it's serverless becoming a real compute tier for ML inference, data processing, and media workloads. And with configurable memory-to-vCPU ratios (2:1, 4:1, 8:1), teams will tune these like EC2 instances. Which means the same underutilization risk that exists for Reserved Instances is coming to serverless. The answer isn't "don't commit." It's commit with underutilization protection — so if your Lambda usage drops, you're not stuck paying for capacity you're not using. That's what Archera's release guarantee does. Same mechanic, new tier. Running heavy Lambda workloads? Read the full announcement link in comments & let me know what you're seeing on cost. #FinOps #Serverless #AWS #CloudCost #Lambda

🚀 AWS just changed the game for high-performance serverless workloads. Introducing Lambda Managed Instances — where you get the simplicity of serverless and the power of EC2 ⚡ 🔍 Key Highlights: • No more cold starts — always warm environments • Multi-concurrency → handle parallel requests efficiently • Runs in your AWS account (better control & isolation) • Choose your own instance type (Graviton, CPU-optimized, etc.) 💡 This is NOT your typical Lambda: You’re trading scale-to-zero for consistent performance & predictability 👉 Best suited for: • High-throughput APIs • Batch & data processing • Long-running workloads ⚠️ Not ideal for: • Spiky traffic • Low usage apps 📊 In short: Serverless is evolving from “event-driven only” → to performance-driven architectures I created a quick visual cheat sheet 👇 Would love to hear your thoughts — would you use this in production? #AWS #Lambda #Serverless #CloudComputing #DevOps #CloudArchitecture #AWSLambda #Scalability #TechInsights

🚀 Big News in the Cloud Space! As of April 2026, AWS has introduced a game-changing feature — *Amazon S3 Files*. For the first time, S3 buckets can now be mounted directly as a shared file system, bringing a whole new level of flexibility for developers and data engineers. 🔹 Key Highlights: • Native file system access to S3 • Supports NFS mounts • Works seamlessly with EC2, Lambda, EKS, and ECS • Enables high-performance, low-latency data access This innovation bridges the gap between object storage and traditional file systems, making it easier to build scalable, data-intensive applications without changing existing workflows. 💡 This is a big step toward simplifying cloud architectures and improving developer productivity. Excited to see how this evolves and how teams start leveraging it in real-world use cases! #AWS #AmazonS3 #AWSWorld #DataEngineering #Devops #CloudInnovation #CloudComputing

How I Reduced AWS Costs Without Touching Production Traffic. A while ago, I noticed something interesting. Our AWS bill was increasing—but nothing had changed in traffic. No new features. No sudden spike in users. So where was the cost coming from? Instead of guessing, I followed a simple approach:   Monitor → Measure → Remediate Step 1: Monitor I started with AWS Budgets and AWS Cost Explorer. That’s where the first insight came in: 👉 A few services were quietly contributing to most of the cost. 📊Step 2: Measure Next, I analyzed Amazon EC2 usage. What I found was common in many environments: Over-provisioned instances Idle resources running 24/7 Dev environments not being used—but still costing money To validate and optimize this, I used AWS Compute Optimizer, which helped me choose the right instance types and sizes based on actual utilization patterns. ⚙️Step 3: Remediate Then came the real impact. I focused on practical optimizations: Cleaned up idle resources using AWS Trusted Advisor Moved stable workloads to Savings Plans / Reserved Instances Used Spot Instances for non-critical workloads Enabled Auto Scaling for demand-based scaling Scheduled shutdown of dev/test environments Removed unused EBS volumes Applied S3 lifecycle policies to reduce storage costs 👉Achieved 20–30% overall cost savings by eliminating waste and optimizing pricing models Bonus: Serverless Optimization For workloads on AWS Lambda, I used AWS Lambda Power Tuning to find the optimal memory configuration—balancing performance and cost efficiently. What I Learned Cost optimization is not a one-time task. It’s a continuous process of: Monitoring Right-sizing Choosing the right pricing model Most savings don’t come from big changes—but from fixing small inefficiencies. Final Thought You don’t always need new architecture to reduce costs. Sometimes, you just need better visibility. 💬 Curious—what worked for you? What strategies have helped you optimize cloud costs? #AWS #DevOps #CloudComputing #FinOps #CostOptimization #CloudArchitecture #Engineering #EC2 #Serverless #AWSLambda #Microservices

$800/month in AWS charges. No traffic spikes. No new services. No obvious reason. It was NAT Gateway — but not the part most people check. Cross-AZ traffic. Instances in us-east-1b routing through a NAT Gateway sitting in us-east-1a. Looks like internal traffic. Bills like external. $0.01/GB doesn’t sound like much until you’re moving 80TB/month internally. The fix: → One NAT Gateway per AZ → Move S3 and DynamoDB traffic to VPC endpoints — free within the same region Bill dropped from $800 to under $50 the following month. The frustrating part — this doesn’t have an obvious line item name. It hides inside “Data Transfer.” Most teams never drill into it. When did you last look at your VPC data transfer breakdown? #AWS #CloudCost #FinOps #DevOps #CloudComputing

Day 2 of my cloud computing learning journey 🚀 Today I explored how cloud-native applications are architected to handle scale — and it completely reframes how I think about backend systems. In a traditional setup, one server does everything — web serving, compute, storage. Cloud-native breaks all of that apart intentionally. Here's what I learned: → API requests hit a Load Balancer, the single stable entry point → The LB distributes traffic across a fleet of EC2 instances, each running the same web server → For lightweight operations, the VM handles the request inline and returns a response → For heavy compute, the VM drops a job onto a queue (SQS) and immediately returns 202 Accepted — the client doesn't wait → A separate pool of worker VMs polls the queue and processes jobs independently → Both layers autoscale — web VMs scale with incoming requests, worker VMs scale with queue depth → When demand drops, instances are terminated automatically to optimize cost The key insight: VMs are stateless and interchangeable. They can start and shut down freely because all persistent state lives in managed services — S3, RDS, Redis — that exist independently of any individual instance. This is what makes cloud infrastructure resilient, cost-efficient, and scalable by design — not by accident. Day 3 tomorrow. Documenting everything publicly to stay accountable. #CloudComputing #AWS #CloudNative #LearningInPublic #DevOps #SoftwareEngineering #SystemDesign

Ever wondered what building a tech startup really looks like behind the scenes? We’re opening up our roadmap to the world. Instead of guessing what to build next, we’re doing something simple: asking you what you’d actually pay for. If you had to choose,which one of these intrigues you the most: - DB Cron: run recurring DB jobs without external cron - Auto Indexing: detect and fix missing indexes automatically - Cloud Real-Time Monitoring: live dashboards + alerts - SkyScanner-Style DB Pricing: compare costs across AWS regions - DB Storage Downsizing: reclaim unused storage automatically We’re building this with you, not for you. Take a look → https://lnkd.in/gyMmD4Hp #postgresql #database #managedDatabases #devops #aws #buildinpublic

All the best to Selfhost team. I feel Database storage downsizing will be the best idea or feature that would be enabled by Selfhost followed by ideas managed database auto scheduler feature and Auto indexing feature with much detailed and finer control.

AWS Lambda vs Azure Functions: Engineer's Field Guide Latest insights on Serverless Computing focusing on AWS Lambda, covering key developments from Apr 10 to Apr 10, 2026. 📅 Coverage period: Apr 10 - Apr 10, 2026 Read the full analysis 👇 #TechNews #TechnologyTrends #ServerlessComputing #AWSLambda #AzureFunctions #Innovation #DigitalTransformation https://lnkd.in/gZrS_hPV