What Serverless Actually Means Beyond AWS Lambda

☁️ AWS Cheat Sheet 2026: The Services You Actually Need to Know ☁️ With 200+ services, AWS can feel like a labyrinth. But for 90% of use cases, you only need to master the "Core 20." Here is a breakdown of the essentials for every Cloud & DevOps Engineer. 👇 🏗️ 1. Compute (The Brains) EC2: Virtual servers. You manage the OS. Lambda: Serverless functions. Run code without provisioning servers. Fargate: Serverless containers. Run Docker without managing EC2 instances. 📦 2. Storage (The Memory) S3: Object storage. Infinite scaling for images, logs, and static sites. EBS: Block storage. Hard drives for your EC2 instances. EFS: Shared file system. One "drive" connected to multiple servers. 🌐 3. Networking (The Roads) VPC: Your private, isolated section of the AWS cloud. Route 53: Scalable DNS and domain registration. CloudFront: Content Delivery Network (CDN) to speed up your app globally. ALB/NLB: Load balancers to distribute traffic across your targets. 🗄️ 4. Databases (The Filing Cabinet) RDS: Managed Relational DBs (MySQL, Postgres, SQL Server). DynamoDB: Ultra-fast, serverless NoSQL database. ElastiCache: In-memory caching (Redis/Memcached) for speed. 🛡️ 5. Security (The Guard) IAM: Identity & Access Management. Who can do what? (Always follow Least Privilege). Secrets Manager: Securely store and rotate API keys and passwords. KMS: Key Management Service. Encrypt your data at rest. 🚀 6. DevOps & Automation (The Factory) CodePipeline: Orchestrates your CI/CD workflow. CloudFormation / CDK: Infrastructure as Code (IaC). Define your cloud in JSON, YAML, or TypeScript/Python. EKS: Managed Kubernetes (The industry standard for container orchestration). CloudWatch: Monitoring, logs, and alarms to see if things are breaking. 💡 Pro-Tip: If you are just starting, focus on IAM, VPC, EC2, and S3. They are the four pillars that almost every other service is built upon. What AWS service was the hardest for you to wrap your head around? For me, it was definitely VPC networking! Let's discuss in the comments. 💬 #AWS #CloudComputing #DevOps #SolutionsArchitect #TechCareer #AmazonWebServices #CloudNative #LearningPath

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.

For years, the serverless conversation had an unspoken ceiling. You could build almost anything on Lambda. Until you couldn't. The moment your workload needed more than 10 GB of memory, you were back to managing EC2 instances, ECS tasks, or some hybrid that nobody enjoyed maintaining. Lambda Managed Instances quietly changes that equation. 32 GB of memory. Your choice of EC2 instance families (C, M, R, including Graviton4). Multi-concurrent invocations per execution environment. Savings Plans and Reserved Instance pricing. And AWS still handles provisioning, patching, scaling, and routing. What makes this interesting isn't the spec bump. It's what it unlocks. ML inference with large models held resident in memory across invocations, no dedicated endpoint needed. Loading millions of records into a DataFrame at init and serving sub-millisecond analytical queries. Vector similarity search over large embedding indexes without spinning up a separate vector database. These were all "sorry, not Lambda" conversations. Now they're not. The part that matters most for the teams I work with across LATAM: you configure the infrastructure once at the Capacity Provider level, not per function. Multiple functions share the same provider. That's a meaningful simplification when you're running dozens of microservices across accounts. There's a nuance worth watching, though. Multi-concurrency per execution environment means your code needs to be thread-safe. That's a different contract than what most Lambda developers are used to. The teams that treat this as "just Lambda with more RAM" will hit surprises. The ones that understand they're getting a new compute primitive, somewhere between Lambda and Fargate, will build things we haven't seen yet. Serverless just got a lot harder to outgrow. https://lnkd.in/dRHPQYvH #AWSLambda #ServerlessArchitecture #CloudCompute

Every release teaches you something. v0.4.0 taught me something uncomfortable. The numbers were wrong. Every resource in `finops find-waste` showed $0.00 for cost estimates. You knew the resource was idle. You just didn't know what it was costing you. Which made the output feel academic rather than actionable. That's fixed. finops-agent v0.4.0 is out. 🚀 Here's what changed: → Proper pricing tables for every AWS resource — EC2 (40+ instance types), EBS, ELB, NAT Gateway, EKS, RDS (Multi-AZ aware). No more $0.00. → Live pricing APIs — AWS Pricing API, Azure prices.azure.com, GCP Cloud Billing Catalog. In-memory cache with hardcoded fallback. Always an estimate. → 20 new resource collectors across all 4 clouds — RDS, S3, Lambda, CloudFront, API Gateway, Cloud SQL, GCS, Cloud Run, Cloud Functions, BigQuery, Pub/Sub, SQL Databases, Cosmos DB, CDN, Autonomous DB, Object Storage and more. → CPU-based idle instance detection — CloudWatch, Azure Monitor, GCP Cloud Monitoring, OCI Monitoring. Avg CPU < 5% over 14 days? Flagged. That forgotten test instance from 8 months ago? Found. → Stopped database detection — RDS, Azure SQL, Cloud SQL, Autonomous DB stopped but still charging storage. → Savings Plans + Reserved Instance tracking — utilization visibility for AWS commitments. → 171 tests passing. Up from 116. The waste detection is finally complete. If it's running and idle, it gets found. Full write-up on what changed and why 👇 📖 https://lnkd.in/gFgUg3_e ⭐ https://lnkd.in/gsU74w-i 🐳 docker pull mathumathi247/finops-agent:latest Got feedback? Drop me a LinkedIn DM or shoot me a mail. I read every one. 🙏 #FinOps #OpenSource #CloudCost #AWS #Azure #GCP #OCI #AIAgents #BuildInPublic #DevOps

Most AWS cost assessments produce a report nobody reads. Here's what a real one actually uncovers. Teams almost always walk in assuming the problem is rightsizing. Too many m5.xlarges, not enough Compute Optimizer runs. That's the surface read. What assessments actually find: 80% of the savings trace back to architecture decisions made 18 months ago that nobody has revisited. A three-node RDS cluster sized for a traffic spike that never arrived. An EKS cluster running at 40% utilization because autoscaling was never configured. Dev and staging environments running 24/7 at 40% of production cost, serving zero customers at 2am. These aren't resource-level inefficiencies. They're architectural ones, and they compound quietly for months before the CFO starts asking questions. The second thing that kills most self-directed optimization efforts: tagging coverage below 50%. When half your resources have no owner, no environment tag, no cost center -- you can cut spend, but you can't attribute it. The savings disappear into the next monthly bill. Six months later you're back where you started, with no proof anything changed. That's the pattern. Visible costs get reviewed. Hidden attribution gaps don't. The deliverable that actually changes this isn't a recommendation list. It's a sequenced roadmap with specific dollar figures, an owner per action, and a timeline. Not "rightsizing could save 25-40% on compute." More like: "these 14 instances move from m5.xlarge to m6i.large after the payment service migration, saving $3,400/month." One is a report. The other is a plan someone can execute next sprint. Full breakdown of what a proper assessment covers, phase by phase: https://lnkd.in/e_Ggdg5T

New AWS update : Launching S3 Files, making S3 buckets accessible as file systems Amazon S3 (Simple Storage Service) is AWS’s highly scalable object storage service—used for backups, data lakes, analytics, and static content. It can store virtually unlimited data with high durability and availability. Amazon EC2 (Elastic Compute Cloud), on the other hand, provides resizable compute capacity—basically virtual servers in the cloud where your applications run. 👉 Traditionally: S3 = storage (objects in buckets) EC2 = compute (VMs running workloads) But bridging them efficiently has always required extra layers, data duplication, or custom integrations. 🚨 What’s New: S3 Files (Game-Changer) AWS has introduced S3 Files — allowing you to mount S3 buckets as a file system directly on compute services like EC2, containers, and even Lambda. 💡 Key Highlights: Access S3 like a local file system (NFS-based) Perform standard file operations (read/write/update/delete) Changes sync automatically between file system ↔ S3 Multiple compute resources can share the same data without duplication No need to move or copy data between storage types 💥 Why This Matters (Big Impact) Before this: You had to choose between S3 scalability OR file system usability Teams built complex pipelines to sync object storage with file systems Now: ✅ No more trade-offs — object storage + file system in one ✅ No data duplication → cost optimization ✅ Massive performance gains with intelligent caching ✅ Seamless integration with EC2, ECS, EKS, Lambda ✅ Perfect for: AI/ML workloads Data engineering pipelines Legacy apps expecting file systems 👉 S3 is no longer just storage… it becomes a central data platform accessible everywhere. 🧠 Final Thought This update fundamentally simplifies cloud architectures. Instead of stitching services together, AWS is collapsing boundaries between storage and compute — making systems faster, cheaper, and easier to build. 📌 If you’re into AWS, DevOps, Kubernetes, and Cloud Architecture, this is a shift you should deeply understand. 👉 Follow for more updates on AWS, cloud innovations, and real-world DevOps insights. #AWS #AmazonS3 #EC2 #CloudComputing #DevOps #Kubernetes #DataEngineering #CloudArchitecture #MachineLearning #Serverless #TechUpdates #Innovation

⚡ Lambda vs. ECS vs. EKS: Stop guessing. Start choosing. I see this mistake every week: Teams running a single microservice on EKS. Teams running 20-minute video encoding in Lambda. Teams spending $500/month on something that should cost $5. Here's a framework to choose the right AWS compute service: AWS Lambda ✅ Event-driven workloads ✅ Infrequent or bursty traffic ✅ Simple APIs, data processing ⚠️ 15-minute timeout limit Amazon ECS (Fargate) ✅ Microservices, web apps ✅ Batch processing ✅ 24/7 workloads ✅ Sweet spot for most containerized apps Amazon EKS ✅ Multi-cloud strategy ✅ Existing Kubernetes investment ✅ Service mesh (Istio, Linkerd) ⚠️ $72/month minimum (control plane) The Numbers (Real Examples): Low-traffic API (10K req/day): • Lambda: $2-5/month 🏆 • ECS Fargate: $30-40 • EKS: $100-150 High-traffic API (1M req/day): • Lambda: $400-500 • ECS Fargate: $300-400 🏆 • EKS: $500-700 The Best Architects Use Multiple: Pattern: API Gateway → Lambda (auth) → ECS (processing) Pattern: EventBridge → Lambda (orchestration) → ECS (batch) Pattern: EKS (core) + Lambda (extensions) Quick Decision Tree: • Event-driven & <15 min → Lambda • Containerized & on AWS → ECS Fargate • Multi-cloud or K8s features → EKS • GPU/ML workloads → ECS EC2 or EKS I wrote a comprehensive guide with cost breakdowns, code examples, and a decision framework. Read the full article here: https://lnkd.in/ds7Cx6rD What's your default compute service on AWS? Lambda, ECS, or EKS? hashtag#AWS hashtag#Lambda hashtag#ECS hashtag#EKS hashtag#Serverless hashtag#Kubernetes hashtag#CloudArchitecture

Day 2 – AWS Compute: When NOT to use what? Yesterday, we explored when to use compute services. Today, we shift our focus to the other side of the coin. Knowing when NOT to use a service is crucial for any architect. ⚙️ EC2 – When NOT to use: ❌ For short-lived or event-driven workloads ❌ When you don’t want to manage servers ❌ For unpredictable traffic (scaling delay) 👉 Why: Requires provisioning, patching, and scaling management ⚡ Lambda – When NOT to use: ❌ Long-running tasks (>15 minutes) ❌ Heavy CPU/memory workloads ❌ Applications needing persistent connections 👉 Why: Execution limits, cold starts, and stateless nature 🐳 ECS – When NOT to use: ❌ If you need Kubernetes standard ❌ Very small/simple apps (overkill) ❌ If you want zero infrastructure management (use Fargate instead) 👉 Why: Still requires cluster and scaling decisions ☸️ EKS – When NOT to use: ❌ Small teams or beginners ❌ Simple applications ❌ When Kubernetes expertise is missing 👉 Why: High complexity and operational overhead 🚀 Fargate – When NOT to use: ❌ Need deep control over infrastructure ❌ Cost-sensitive long-running workloads ❌ Specialized compute requirements (GPU, custom OS tuning) 👉 Why: Higher cost compared to EC2 and less control 🌱 Elastic Beanstalk – When NOT to use: ❌ Complex microservices architecture ❌ Need full infrastructure customization ❌ Advanced DevOps pipelines 👉 Why: Abstracts infrastructure but limits flexibility 📦 AWS Batch – When NOT to use: ❌ Real-time processing systems ❌ Low-latency APIs ❌ Event-driven microservices 👉 Why: Designed for batch jobs, not real-time ⚖️ Architect Mindset: If it’s simple → avoid complex services (EKS, ECS) If it’s event-driven → avoid EC2 If it’s long-running → avoid Lambda If it’s cost-sensitive → evaluate Fargate carefully 🧠 Golden Rule 👉 “Just because you CAN use a service doesn’t mean you SHOULD.” More coming tomorrow 🔥 Next: Storage – When to use S3, EBS, EFS 👉 I’m planning to deep dive into each of these services with real-world architectures. 💬 Which AWS Compute service do you want me to cover in detail next? Drop it in the comments 👇 EC2 Lambda ECS EKS Fargate #AWS #CloudComputing #SolutionArchitect #SystemDesign #DevOps #LearningInPublic

🚀 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