Serverless Architecture

One of the biggest misconceptions I hear about serverless is this: "It’s just a spaghetti of Lambda functions calling each other." Yes, I’ve seen that happen. When it does, it’s ugly, and it usually comes from inexperience and lack of design. But that’s not what serverless architectures are supposed to be. It’s like saying "cats are animals that poop on your bed", sure, accidents happen (…probably 😹), but that’s not the norm or the expected behaviour! So, what does a well-designed serverless architecture actually look like? From a bird’s-eye view, pretty much the same as what you'd build with containers or EC2: • Separate accounts per team/workload • System is decomposed into independent services • Every service owns its own data (no shared DBs) • Services are loosely coupled through events • Centralised logging and observability Whether I have an API (synchronous communication) or use events (asynchronous communication) does not depend on whether I use Lambda vs. containers. A serverless architecture doesn't have to be event-driven. Equally, an event-driven architecture can run on containers or EC2. Those are orthogonal architectural choices. Inside each service, I use the serverless-first mindset to decide on my tech stack, e.g. • Prefer DynamoDB over RDS • Prefer API Gateway of ALB • Prefer Lambda functions over containers or EC2 • Prefer EventBridge over Kafka The guiding principle is simple: pick the service that does the most heavy lifting. And with serverless technologies like Lambda, you get built-in multi-AZ redundancy; scalability; reduced attack surface; no need to manage infrastructure; simplified deployment; and, pay-per-use pricing. So no, you don’t expose "a bunch of Lambdas" as your service boundary. That’s not the goal. That’s just a mistake.

Just published: "Serverless MCP: Stateless Execution for Enterprise AI Tools" Most teams build MCP servers with persistent connections and session state. For enterprise workflows—where tools orchestrate across Salesforce, Stripe, and other systems of record—there's a better way. What serverless architecture eliminates: - Server affinity and connection limits - Session state synchronization - Cache staleness and stale reads - Complex failure recovery (no connection state to reconstruct) What stateless execution forces: - Backend systems as source of truth (your CRM, ERP, payments—not cached copies) - Idempotent operations by design (no duplicate charges, no duplicate records) - Self-contained requests (any worker handles any call) - Cleaner separation between protocol and execution layers The article explains: - The three architectural choices that define serverless MCP - When stateless execution matters (and when it doesn't) - Server architecture comparison (side-by-side) - How to decide which pattern fits your system Includes a complete open-source reference implementation (Dewy Resort sample app) demonstrating the patterns. Read it here: https://lnkd.in/gTKSDg6d Understanding the tradeoffs matters more than following trends.

𝐖𝐡𝐚𝐭 𝐃𝐨𝐞𝐬 𝐚 𝐒𝐞𝐫𝐯𝐞𝐫 𝐥𝐞𝐬𝐬 𝐄𝐯𝐞𝐧𝐭-𝐃𝐫𝐢𝐯𝐞𝐧 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐑𝐞𝐚𝐥𝐥𝐲 𝐋𝐨𝐨𝐤 𝐋𝐢𝐤𝐞? Let’s break it down using a real-world scenario: an e-commerce platform. Traditional monoliths or tightly coupled services often struggle with scalability and flexibility. A server less event-driven setup solves that by breaking the system into modular micro services that only run when triggered. 𝐇𝐞𝐫𝐞 𝐢𝐬 𝐡𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬, 𝐬𝐭𝐞𝐩 𝐛𝐲 𝐬𝐭𝐞𝐩: - The user interacts with the frontend. All requests are routed through API Gateway - Each business function - product management, basket operations, order processing - runs independently on AWS Lambda - Data is persisted in Dynamo DB, a fully managed, server less database - When the user completes a checkout, a Checkout Completed event is published to Amazon Event Bridge - Event Bridge evaluates routing rules and triggers downstream systems - like order fulfilment or analytics - No polling. No idle servers. Everything responds in real time 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: - Micro services are fully decoupled and independently deployable - System scales automatically with load - no manual provisioning required - Costs stay low since compute runs only when needed - Teams can move faster and ship features independently This is not just a shift in technology. It is a shift in how we think about building software: reactive, modular, and cloud-native by design. Would you design your next platform this way? Let’s discuss.

𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀: 𝗦𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀 𝗗𝗲𝗹𝘁𝗮 𝗟𝗮𝗸𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗼𝗻 𝗔𝗪𝗦 =========================================== Imagine you have data in your company's local servers (on-premises) and want to: 1. Move this data to AWS 2. Analyze it without managing servers 3. Use an event-driven approach Here's how TrueBlue, a company facing this challenge, solved it using AWS services: 𝟭. 𝗗𝗮𝘁𝗮 𝗠𝗶𝗴𝗿𝗮𝘁𝗶𝗼𝗻 ----------------- • Used AWS Database Migration Service to copy data from local databases to Amazon S3 • Ensures up-to-date information for jobs, job requests, and workers • Enables accurate job matching 𝟮. 𝗘𝘃𝗲𝗻𝘁-𝗗𝗿𝗶𝘃𝗲𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 ------------------------------ • Set up S3 event notifications when new data arrives • Used Amazon SQS (Simple Queue Service) to capture these events • Created 3 SQS queues for different update frequencies:  - 10-minute updates  - 60-minute updates  - 3-hour updates • AWS EventBridge rules trigger Step Functions based on these time intervals • Step Functions orchestrate AWS Glue jobs for data processing 𝟯. 𝗦𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 -------------------------- • Chose AWS Glue over Amazon EMR (Elastic MapReduce) for serverless data processing • Reasons for choosing Glue:  - Team's expertise in serverless development  - Easier to manage and debug  - Achieves similar results to EMR without server management • Glue jobs transform and load data into the Delta Lake format 𝟰. 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 ------------ • Data scientists use PySpark SQL to query the Delta Lake • Delta Lake has three tiers:  1. Bronze: Raw data from source systems  2. Silver: Cleaned and joined data from bronze tier  3. Gold: Prepared data for machine learning (feature store) • Glue jobs keep the Delta Lake up-to-date with reliable upserts (updates and inserts) • Enables data scientists to:  - Perform accurate job matches  - Extract datasets for analysis  - Build and train machine learning models 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗼𝗳 𝘁𝗵𝗶𝘀 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: ------------------------------ 1. Serverless: No need to manage infrastructure 2. Scalable: Can handle increasing data volumes 3. Cost-effective: Pay only for resources used 4. Real-time: Event-driven updates keep data fresh 5. Flexible: Supports various data processing needs This architecture showcases how to build a modern, serverless data lake using AWS services, enabling efficient data migration, processing, and analytics without the complexity of managing servers. #dataengineer #dataengineering #deltalake #aws

We built a whole system to fix our serverless cold start latency. Here’s how: Our initial architecture was embarrassingly simple - Make everything generic. Run it all in serverless. We thought that would give us flexibility. What it actually gave us was massive cold starts. Cold start time is almost entirely dictated by image size. And when you’re naïvely bundling your entire toolchain into the runtime, you’re basically guaranteeing slow boots. So we tore the system apart and rebuilt it: - The serverless runtime is now ultra-minimal - basically just the bare execution environment. - The actual tool code is bundled on demand and shipped to the function at runtime. - Each bundle is tiny, fully-compiled, versioned, and executes instantly. Just: Small images = fast cold starts. Dynamic bundles = fast iteration. We changed what we shipped to serverless. And that one architectural decision eliminated the latency bottleneck we kept pretending was “just how serverless works.”

𝐋𝐞𝐬𝐬𝐨𝐧𝐬 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐀𝐖𝐒 𝐮𝐬-𝐞𝐚𝐬𝐭-𝟏 𝐎𝐮𝐭𝐚𝐠𝐞: 𝐃𝐞𝐬𝐢𝐠𝐧𝐢𝐧𝐠 𝐚 𝐌𝐮𝐥𝐭𝐢-𝐂𝐥𝐨𝐮𝐝 𝐒𝐞𝐫𝐯𝐞𝐫𝐥𝐞𝐬𝐬 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐟𝐨𝐫 𝐑𝐞𝐬𝐢𝐥𝐢𝐞𝐧𝐜𝐞 When the AWS us-east-1 outage disrupted major global platforms last year, it was a wake-up call for every architect and engineer — no single cloud can guarantee 100% uptime. That incident underscored the need for multi-cloud resilience, where systems can shift workloads intelligently between providers like AWS and Azure without impacting end-user experience. In response, we designed a multi-cloud, serverless, GitOps-driven architecture that embodies the Well-Architected Framework principles — balancing reliability, performance efficiency, cost optimization, and operational excellence across clouds. 𝐃𝐚𝐭𝐚𝐟𝐥𝐨𝐰: The user’s app connects seamlessly from any source to our gateway app, which distributes requests equally between Azure and AWS. This dual-cloud setup ensures both robustness and availability, with all responses routed through an API Manager gateway for a unified and smooth experience. 𝐓𝐡𝐞 𝐒𝐞𝐫𝐯𝐞𝐫𝐥𝐞𝐬𝐬 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤: At the core of this architecture is the Serverless Framework. It abstracts infrastructure complexity, automates deployments, and supports GitOps-driven workflows — enabling a truly multi-cloud serverless deployment model that’s scalable and cloud-agnostic. 𝐂𝐈/𝐂𝐃 𝐰𝐢𝐭𝐡 𝐆𝐢𝐭𝐎𝐩𝐬: The CI/CD pipeline is built around GitOps principles, automating build, test, and deploy stages across multiple cloud providers. It ensures that code changes flow securely and reliably, maintaining consistency and compliance throughout the delivery process. 𝐏𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬: Build cloud-agnostic APIs for client applications running across environments. Deploy microservices to multiple cloud platforms with a single manifest file. Maintain cross-cloud redundancy to prevent downtime during regional failures. Run serverless functions in the most cost-efficient or lowest-latency region dynamically. 𝐁𝐥𝐮𝐞-𝐆𝐫𝐞𝐞𝐧 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭: Each cloud platform hosts two duplicate sets of microservices — creating active-passive environments that allow instant failover. This approach ensures continuous availability and low-risk deployments across cloud regions and providers. In today’s world, multi-cloud is not just a choice — it’s a necessity for businesses aiming to stay resilient, cost-optimized, and future-ready. The Serverless Framework, combined with GitOps and Well-Architected principles, helps achieve just that. 💡 Follow me for upcoming posts where I’ll share new, innovative architecture blueprints — real-world examples showing how to design well-architected, reliable, and cost-efficient infrastructure for your business platforms. #cloudcomputing #aws #azure #cloudarchitecture #serverless #gitops #multicloud #devops #wellarchitected

Serverless has one fundamental problem: State. Platforms like Cloud Run and Lambda are fantastic at compute. But they don't manage execution state. Here's how companies waste huge $$$: 𝐒𝐭𝐚𝐫𝐭 𝐬𝐢𝐦𝐩𝐥𝐞 Lambdas, Cloud Run, etc. Stateless handlers. HTTP in, HTTP out. Life is simple. 𝐀𝐝𝐝 𝐪𝐮𝐞𝐮𝐞𝐬 "Some requests are getting lost." You add a queue (e.g., SQS) to buffer work. Now you have at-least-once delivery and basic flow control. But you also now have two systems to reason about: compute and messaging. -> You’ve introduced distributed coordination. 𝐀𝐝𝐝 𝐫𝐞𝐭𝐫𝐲 𝐥𝐨𝐠𝐢𝐜 "Sometimes the container disappears mid-request." Cold starts, OOM kills, rolling deployments, provider preemption. So you add retries at the application level, usually retrying the whole request. -> You are now duplicating work and entering the space of consistency anomalies. 𝐀𝐝𝐝 𝐚 𝐃𝐋𝐐 "Some retries never succeed." Flaky dependencies, bad inputs, partial failures. You can't drop the request, so you route it to a Dead Letter Queue and build tooling to monitor and reprocess it. -> More custom infrastructure, more operational surface area. 𝐀𝐝𝐝 𝐢𝐝𝐞𝐦𝐩𝐨𝐭𝐞𝐧𝐜𝐲 𝐤𝐞𝐲𝐬 "Some customers were charged twice." A queue lease expired. A function timed out. A client retried. So you implement idempotency keys, deduplication tables, and cleanup jobs. -> State now leaks into every boundary of the system. What started as "just put SQS in front" becomes weeks of careful distributed systems engineering. You realize you are re-implementing parts of reliability theory in application code. 𝐀𝐝𝐝 𝐚𝐧 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐨𝐫 "This is getting hard to reason about." You introduce Step Functions, Airflow, Temporal, or a custom DAG engine. Now you have multiple sources of truth: the application, the workflow engine, and a very upset Head of Finances. Keeping them consistent becomes a permanent concern. The result? You didn't build "serverless", but: a message broker, a retry framework, a dedup system, a saga engine, a workflow scheduler, a recovery pipeline. All glued together :) Durable execution makes your program itself the state machine: every step is checkpointed transactionally in the database, so crashes, restarts, and redeploys resume execution exactly where it left off. Instead of rebuilding reliability with queues, retries, and deduplication, the runtime guarantees exactly-once progress and deterministic replay by construction. Don't take my word for it and try out this guide on deploying a DBOS app on Google Cloudrun.

Frontend Devs, Stop Waiting on Backend Teams—Go Serverless 🚀 Remember when frontend devs had to wait on backend teams for every new endpoint? Those days are over. Serverless lets us build full-scale apps without managing servers. Here’s how my team leverages AWS to move fast and scale effortlessly: 1️⃣ API Gateway + Lambda = Frontend Freedom ✅ Cut API development time by 60% using this combo. ✅ TypeScript + Lambda = type safety across your stack. 💡 “Lambda’s auto-scaling saved us during a 10x traffic spike last Black Friday.” — E-commerce CTO 2️⃣ Amplify: The Frontend Developer’s Cheat Code ✅ Deploy React/Angular/Vue apps with CI/CD in minutes. ✅ Auth, Storage, APIs—minimal config, max speed. 💡 Our junior devs shipped features 3x faster after adopting Amplify. 3️⃣ DynamoDB: NoSQL That Actually Makes Sense ✅ Single-digit ms response times—even at scale. ✅ Pay-per-request pricing saved us 42% vs. traditional DBs. 💡 “Migrating from MongoDB to DynamoDB cut our DB costs in half.” — FinTech Lead 4️⃣ CloudFront + S3: The Performance Power Duo ⚡ ✅ 40% faster LCP after implementing this setup. ✅ Global CDN = happy users worldwide. 💡 Hosting our app on S3 costs less than $5/month for 100k visitors. 5️⃣ EventBridge: The Glue That Connects Everything ✅ Event-driven architectures without managing queues. ✅ Replaced 3 microservices with simple EventBridge rules. 💡 “EventBridge simplified our architecture dramatically.” — SaaS Founder The REAL Challenges No One Talks About: ❌ Cold starts can hurt → Fix: Provisioned Concurrency. ❌ Local development is clunky → Use AWS SAM + LocalStack. ❌ CloudFormation is a headache → Switch to CDK. 💡 Bottom Line: Serverless isn’t just for backend devs anymore. Frontend engineers who master these tools become full-stack force multipliers. What’s your biggest serverless challenge? Drop it below—we’ll solve it together! 👇 #Serverless #AWS #CloudComputing #FrontendDevelopment #WebDev #Scalability

𝗦𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀 𝗙𝗶𝗿𝘀𝘁: 𝗜𝘀 𝗜𝘁 𝗔𝗹𝘄𝗮𝘆𝘀 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗖𝗵𝗼𝗶𝗰𝗲? The cloud world is buzzing about “Serverless First” strategies. But is it the 𝗯𝗲𝘀𝘁 𝗽𝗮𝘁𝗵 𝗳𝗼𝗿 𝗲𝘃𝗲𝗿𝘆 𝘄𝗼𝗿𝗸𝗹𝗼𝗮𝗱? Let’s compare serverless and containerized approaches with actionable criteria to help you decide. 𝗦𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀 𝘃𝘀. 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝘀: 𝗞𝗲𝘆 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 1. 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: • Serverless: Auto-scales to zero. Perfect for unpredictable traffic (e.g., APIs, event-driven tasks). • Containers: Manual or cluster-based scaling. Better for steady, high-volume workloads (e.g., microservices, data pipelines). 2. 𝗖𝗼𝘀𝘁: • Serverless: Pay-per-execution. It is cost-effective for sporadic use but can spike with scale. • Containers: Fixed costs for reserved resources. Economical for consistent, long-running processes. 3. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗢𝘃𝗲𝗿𝗵𝗲𝗮𝗱: • Serverless: No infrastructure management. Focus on code. • Containers: Requires orchestration (Kubernetes, ECS) but offers granular control. 4. 𝗖𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Serverless: Limited to the provider’s runtime/config. Containers: Full control over OS, libraries, and dependencies. 5. 𝗩𝗲𝗻𝗱𝗼𝗿 𝗟𝗼𝗰𝗸-𝗜𝗻: Serverless: Higher dependency on a cloud provider (AWS Lambda, Azure Functions, Google Cloud Functions). Containers: Portable across platforms if built agnostically. 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴 𝗖𝗵𝗲𝗰𝗸𝗹𝗶𝘀𝘁 𝗖𝗵𝗼𝗼𝘀𝗲 𝗦𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀 𝗜𝗳: • Your workload is event-driven or has erratic traffic. • You want to minimize DevOps overhead. • Short-lived tasks (e.g., image processing, CRON jobs). 𝗖𝗵𝗼𝗼𝘀𝗲 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝘀 𝗜𝗳: • Predictable, high-performance needs (e.g., gaming backends). • Complex apps requiring custom environments. • Avoiding vendor lock-in is a priority. 💡 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 “Serverless First” isn’t a one-size-fits-all mantra. It’s about 𝗺𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝘁𝗵𝗲 𝘁𝗼𝗼𝗹 𝘁𝗼 𝘁𝗵𝗲 𝗷𝗼𝗯.  Use serverless for agility and cost efficiency in the right scenarios.  Opt for containers when control, portability, and performance are non-negotiable. 𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝘁𝗮𝗸𝗲? Have you faced a “serverless vs container” dilemma? #AWS #awscommunity