Serverless APIs: Why We Use Go Over Node.js
Cold starts, memory usage, and type safety — the practical reasons we reach for Go on AWS Lambda for production APIs.
The serverless tax
Every Lambda function has a cold start. For Node.js, that’s typically 200-600ms. For Go, it’s 50-100ms. When your API has 50+ endpoints and users are on mobile networks in India, those milliseconds compound into a noticeably sluggish experience.
Go compiles to a single binary
No node_modules. No bundling step. No "works on my machine" deployment surprises. A Go Lambda function is a single binary that runs on Amazon Linux. The deploy artifact is 5-15MB instead of 50-200MB for a Node.js function with dependencies.
Memory efficiency translates to cost savings
Go functions run comfortably at 128MB memory allocation. Node.js functions typically need 256-512MB to avoid OOM issues on moderate payloads. At 50+ functions running thousands of invocations per day, the cost difference is real.
Type safety without the ceremony
Go’s type system is simpler than TypeScript’s — no generics gymnastics, no `any` escape hatch temptation. When modelling DynamoDB requests, Go structs map cleanly to JSON with struct tags. The compiler catches mismatches before deployment.
When we still use Node.js/TypeScript
Firebase Cloud Functions (2nd gen) run on Node.js — when the client is already on Firebase, we use TypeScript with Zod for runtime validation. If the team is primarily frontend developers who need to maintain the backend, TypeScript lowers the barrier. We pick Go when performance and cost are priorities, and TypeScript when team velocity is.
Our Go Lambda pattern
Each Lambda function is a standalone main.go with its own Dockerfile. We deploy via ECR (container images) for consistent builds. DynamoDB is our default database (single-table design), with structured logging and CloudWatch alarms for 5XX monitoring. No frameworks — just the AWS SDK and standard library.