Why WebSockets don’t scale easily — and how AWS changes the game

2026-07-06 · Source: Towards AI - Medium · Field: Technology & Digital — Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

WebSockets present significant scalability challenges due to their linear resource consumption, with each persistent connection occupying server memory, CPU, and network buffers. For instance, a raw `ws` Node.js implementation consumes approximately 30KB per connection, while `Socket.IO` uses around 60KB. Scaling to 100,000 concurrent users typically necessitates complex distributed architectures involving Node.js clusters, Nginx load balancing, and a Redis Pub/Sub backbone for cross-instance message synchronization. This self-managed approach introduces substantial operational overhead. AWS API Gateway WebSockets offer a managed alternative, abstracting away connection management, automatic scaling, and message routing, allowing engineers to integrate with AWS Lambda for application logic and DynamoDB for connection state, thereby reducing infrastructure burden.

Key takeaway

For Software Engineers or AI Architects designing real-time applications, choosing between self-managed and AWS-managed WebSockets hinges on operational burden versus control. If your team prioritizes reducing infrastructure management and accelerating feature development, consider adopting AWS API Gateway WebSockets to offload connection scaling and routing. This allows you to focus on core application logic, though be mindful of potential cost predictability constraints at extreme scale.

Key insights

WebSocket scalability is fundamentally limited by linear resource consumption, necessitating complex distributed systems or managed services.

Principles

• Resource consumption grows linearly with connections
• CPU/network bottleneck before memory
• Distributed systems complexity replaces single-server simplicity

Method

AWS API Gateway WebSockets route client events to Lambda functions based on `request.body.action`, using DynamoDB to store `connectionId`s for targeted messaging via the API Gateway Management API.

In practice

• Use `request.body.action` for route selection
• Store `connectionId`s in DynamoDB for user mapping
• Integrate `$connect` and `$disconnect` with Lambda for lifecycle management

Topics

• WebSockets
• AWS API Gateway
• Serverless Architecture
• Real-time Messaging
• Node.js Scaling
• DynamoDB

Best for: Software Engineer, DevOps Engineer, AI Architect

Related on AIssential

• Speeding up agentic workflows with WebSockets in the Responses API — Persistent WebSocket connections drastically reduce API overhead for agentic LLM workflows by caching conversation state.
• Scaling on Demand: How Serverless Architectures Empower Agile and High-Performing Platforms — Serverless architecture offers macOS developers scalable, cost-efficient, and maintenance-free application development.
• The core of backend system design — Backend systems fundamentally rely on dual socket connections for all data flow, from client to service and service to service.
• Coinbase Postmortem Reveals How a Localized AWS Failure Triggered a Multi-Hour Trading Outage — Outages often stem from unexpected interactions between individually manageable failures and architectural assumptions.
• Day 09 of MLOps: From Localhost to Production-Ready ML Deployment on AWS — Production ML deployment on AWS requires specific tools and practices for scalability, reliability, and automation beyond localhost.
• EP196: Cloud Load Balancer Cheat Sheet — Cloud-native architectures enhance scalability and efficiency through specialized components like load balancers, CQRS, Docker, and serverless functions.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.