Architecting for Scale: Production Best Practices for AWS Lambda & API Gateway
Cold starts, connection pooling, IAM lockdowns, and event-driven architectures. Here is a battle-tested guide to scaling serverless applications.
Building serverless backends on AWS.
////AWS & Serverless
Event-driven backends on Lambda, API Gateway, SQS, and DynamoDB — with the operational detail that only starts to matter once real traffic arrives.
4+SaaS Products Shipped
35%Cloud Cost Reduction
10+M+ Traces Processed
AWSNode.jsReactNext.jsPythonTypeScriptMongoDBRedis
The serverless stack is genuinely cost-efficient at startup scale: it scales to zero when nobody is using it and scales out when they are. What separates a working serverless application from a demo is everything around the handler — where the concurrency limits are, which workloads belong in a container instead, and how a cold start budget is spent. These are the decisions I care about most on AWS.
Choosing Lambda or containers per workload
Lambda is the right default for request/response APIs, but it is the wrong tool for long-running work. When PulseGuard's monitoring fleet grew, the check workers moved from Lambda to ECS Fargate: DNS inspections and TLS handshakes are naturally persistent operations, and paying per-invocation for a process that wants to stay alive is wasteful and slower. The API layer stayed on Lambda and still does.
Cold starts, concurrency, and back-pressure
Provisioned concurrency is expensive, so it is reserved for endpoints where a cold start is user-visible — on the order of $15/month for a few critical paths rather than across the fleet. Elsewhere, reserved concurrency protects downstream databases from a Lambda fan-out, SQS acts as the shock absorber between ingestion and processing, and every queue has a dead-letter target so failed messages are inspectable instead of silently retried forever.
DynamoDB where access patterns are truly key-value
DynamoDB is excellent for session storage, caching, and write-heavy telemetry, and a poor fit for relational data with joins and foreign keys. Observyze uses both on purpose: DynamoDB for trace session storage with key-value access, and MongoDB for aggregated analytics with complex read patterns. Migrating a relational schema into DynamoDB to be "more cloud-native" is one of the most expensive mistakes available.
Decisions that show up in production
- One function per responsibility, with its own IAM role and timeout budget
- Idempotent handlers and deduplication keys, because every managed queue is at-least-once
- DynamoDB single-table design driven by access patterns rather than entity shapes
- Provisioned concurrency applied surgically to latency-critical endpoints only
- Reserved concurrency to stop one function from exhausting a shared database
- Parameter Store and Secrets Manager for configuration instead of deployed environment files
- Deployment through CDK or Serverless Framework with GitHub Actions, so no stack is hand-built
By the numbers
- Traces ingested per month
- 2M+Traces ingested per month
- Endpoints monitored in real time
- 500+Endpoints monitored in real time
- Cheaper than fixed VPS compute at startup scale
- 3–10xCheaper than fixed VPS compute at startup scale
- Monthly cost of surgical provisioned concurrency
- ~$15Monthly cost of surgical provisioned concurrency
Stack
- Lambda
- API Gateway
- DynamoDB
- SQS
- SNS
- S3
- RDS PostgreSQL
- MongoDB Atlas
- CloudWatch
- AWS SDK v3
- Node.js
- TypeScript
- Python
Related writing
AWS Cloud Migration for Startups: A Practical Guide from Real Projects
Moving from a VPS to AWS is not just a hosting change — it is an architecture decision. Here is a practical guide to migrating startups to AWS, with real cost comparisons, serverless patterns, and lessons from production.
How I Built a Real-Time Website Monitor That Detects Anomalies
Building an uptime monitor is easy; scaling one to check thousands of URLs, detect DNS drift, and parse SSL certifications dynamically with custom DNS overrides is not. Here's the engineering breakdown.
Other focus areas
Selected Work.
A showcase of my recent projects, focusing on scalable architecture, AI integration, and sleek user experiences.
AI-Powered Server & Website Monitoring
PulseGuard
A real-time monitoring platform for websites and servers featuring uptime tracking, DNS monitoring, SSL expiry alerts, and AI-driven insights for anomaly detection. Monitors 500+ endpoints with 99.9% check success and sub-second alert latency.
Node.jsPythonRedisReactAWS
Observyze
Enterprise-grade AI observability platform with a Zero-Trust/GDPR-compliant architecture. Features local in-memory PII scrubbing and direct proxy bypass. Processes 2M+ traces/month, reducing debugging cycles by 70%.
Next.jsNode.jsSecurity / ComplianceCloud Architecture
Env Secret Lock
A developer environment Secret Manager that solves the problem of secret sprawl. Secures secrets locally with zero-overhead AES-256-GCM. Active in production across 5+ engineering teams.
CLICryptographyNode.js
Cloud Cost Optimization
SubTrackHub
Analyzes cloud infrastructure usage to identify idle resources. Integrates LLMs to generate efficiency scores and recommendations, yielding an average AWS spend reduction of 35%.
Node.jsReactMongoDBLLMs
AI Video Generator
VidVerbalize
Converts YouTube videos into short, captioned clips optimized for social media using AI-based transcription and automated highlight extraction.
Node.jsAI APIsVideo Processing
Web3 Static Platform
NFT Showcase
A responsive NFT showcase website featuring curated collections. Hosted on AWS S3 with Route 53 domain routing for scalable delivery.
AWS S3HTML/CSSJavaScript