AWS Lambda vs Azure Functions: Engineer's Field Guide

You've clicked "Launch Instance" hundreds of times. But do you actually know what AWS does in those milliseconds after? I mapped every layer of EC2 — instance families, pricing models, networking, storage, lifecycle, scaling — into one complete reference. 10 years of production EC2 decisions. One article. Happy Learning! #AWS #CloudComputing #SoftwareArchitecture #DevOps

This post demonstrates how AWS Lambda Managed Instances enables memory-intensive workloads that were previously challenging to run in serverless environments, using an AI-powered customer analytics application as a practical example. Achieve cost savings of up to 33% compared to standard Lambda for predictable workloads, while eliminating the operational overhead of managing EC2 instances.

⚡ 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

Check our new AWS Lambda Managed Instances blog post. 🚀 LMI lets you run Lambda on the EC2 instance type you choose (400+ options) while AWS handles all the infrastructure ops. Predictable compute for heavy workloads, no cold starts, same serverless model. Debasis R. also put together a Monte Carlo risk simulation sample on GitHub if you want to see it in action. 👉 https://lnkd.in/g7ybM-6R #AWS #Serverless #AWSLambda #CloudComputing

Most AWS bills we audit have three line items doing 60-80% of the damage — and none of them are what engineering expects. The usual suspects nobody wants to look at: 1. Aurora I/O on write-heavy workloads. Aurora's "I/O per request" pricing is invisible in the monthly chart until you realize your ORM is doing 40 reads per page load. We've seen $18K/month Aurora bills drop to $6K after a week of query-plan review and a read-replica split. 2. NAT Gateway data processing. $0.045/GB sounds cheap until your ECS tasks pull 50TB/month of container images through it because nobody set up VPC endpoints for S3/ECR. Free fix, five-figure savings. 3. CloudWatch Logs ingestion. Verbose Lambda logs at DEBUG level on a high-traffic endpoint can outpace your compute spend. Log level is a config flag; the bill is real money. These aren't edge cases. They're the pattern. Finance doesn't catch them because they're buried under "Other" or "Data Transfer" in the cost explorer summary — not the top-line EC2/RDS/S3 rows leadership scans. If your AWS bill is growing faster than your traffic, one of these three is almost always in play. Full AWS cost optimization playbook — how we find and fix these without breaking production: https://lnkd.in/gXdW7NAY

Navigating the AWS ecosystem can be challenging, especially when deciding which compute service best fits your architecture. As an IT Engineer, I often get asked about the differences between these core services. Here is a high-level breakdown of the AWS Compute family to help you make an informed decision: 🔹 EC2 (Elastic Compute Cloud): The foundation. It provides virtual servers (instances) where you have full control over the OS and stack. Ideal for applications requiring custom configurations. 🔹 Lambda: The king of Serverless. Run code without provisioning or managing servers. You only pay for the compute time you consume. Perfect for event-driven tasks. 🔹 ECS (Elastic Container Service) & EKS (Elastic Kubernetes Service): Your go-to for containerization. ECS is AWS’s native container orchestrator (highly integrated), while EKS is the managed Kubernetes service for those who need industry-standard orchestration. 🔹 Fargate: Serverless compute for containers. It works with both ECS and EKS, removing the need to manage the underlying EC2 instances. You focus on the containers; AWS handles the rest. 🔹 AWS Batch: Designed for batch computing. It efficiently plans, schedules, and executes your batch computing workloads across the full range of AWS compute services. Key Takeaway: There is no "one size fits all." The choice depends on your need for control versus your desire for operational simplicity. What is your "go-to" compute service for new projects? #AWS #CloudComputing #ITEngineering #DevOps #Serverless #TechCommunity #CloudArchitecture

EC2 vs Lambda vs ECS vs Fargate. Every AWS architect has been asked: "Which compute should we use?" Here's the honest breakdown: 🖥️ EC2 → You need full control over the OS → Long-running workloads with predictable traffic → Legacy apps that can't be containerized Cost: Highest. You pay whether it's idle or not. ⚡ Lambda → Event-driven, short bursts of execution → You want zero infrastructure management → Unpredictable or sporadic traffic Cost: Lowest entry point. But cold starts will humble you at scale. 🐳 ECS / Fargate → Containerized workloads without managing clusters → Microservices that need more than 15 minutes to run → Teams already living in Docker Cost: Middle ground. Pay per task, not per server. The real lesson? There's no universally "best" compute on AWS. There's only the right tool for your workload, your team, and your budget. Choosing EC2 for a simple API is over-engineering. Choosing Lambda for a 30-minute batch job is a mistake waiting to happen. Know your workload first. Pick the service second. #AWS #CloudComputing #DevOps #SoftwareArchitecture #EC2 #Lambda #Serverless #Site_reliability_engineer

Being able to push Lambda Managed Instances up to 32 GB memory and 16 vCPUs is a big win if you’ve ever tried to squeeze compute-heavy workloads into old limits. This update makes it much more practical to run things like data processing pipelines, media transcoding, or batch computation on Lambda Managed Instances, while tuning the memory-to-vCPU ratio (2:1, 4:1, or 8:1) so you’re not overpaying for the wrong resource mix. Plus, you still get managed EC2 under the hood with built-in routing, load balancing, and auto-scaling, so you keep the serverless feel without giving up horsepower. Huge shoutout to AWS for pushing Lambda in a direction that actually matches how people are running modern high-throughput and low-latency workloads - worth checking out the full article for the details. #AWS #AWSLambda #Serverless #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

#Season2 #Day98 Day 98/365: The biggest trap in cloud engineering is becoming religious about your provider. I spent today deep in the GCP Architecture docs, specifically focusing on the framework for migrating workloads from Amazon Web Services (AWS) to Google Cloud. I expected it to be a dry, step-by-step manual. Instead, it was an incredibly fun architectural puzzle. Translating the primitives mapping AWS EC2 to Compute Engine, S3 to Cloud Storage, and untangling the massive differences between AWS IAM policies and GCP's Resource Hierarchy forces you to understand the absolute fundamentals of cloud computing, not just the marketing terms. Studying this triggered a massive shift in how I view my future role: To be a true Cloud Architect, you have to prioritize empathy over technology. Customers don't care about cloud wars. They care about their burning FinOps bills, their database latency, and their operational toil. Sticking blindly to one cloud limits your ability to solve those problems. You have to embrace the customer's specific requirements, even if it means ripping a workload out of the environment you are most comfortable with and rebuilding it somewhere else. This deep dive has officially motivated me to hunt for real-world migration opportunities. I don't just want to build from scratch anymore; I want to untangle the legacy and re-platform it. Status: Provider agnostic. Hunting for migrations. #Day98of365 #DevOps #CloudMigration #AWS #GCP #CloudArchitecture #SystemDesign #FinOps #Founders #TechJourney #ItsOurOps