When should a Node.js app use worker_threads versus the cluster module, or both? What trade-offs should developers be aware of?

For CPU-intensive tasks or multi-core scaling, Node.js offers two built in options. This Q&A helps clarify which to use when, with real world trade off guidance.

A: • Use cluster when you need to scale an HTTP server across multiple CPU cores. Each forked process handles incoming requests independently and provides crash isolation. This is great for stateless API workloads. • Use worker_threads when you have CPU-bound tasks—image processing, crypto, large JSON parsing, mathematical computations—that would block the event loop. Worker threads let you offload those tasks asynchronously without flooding the main thread. • In high-demand backends, a combination is often best: a clustered master handles incoming requests, while each process offloads long-running work to worker threads. Trade offs: • Clustering duplicates memory per process and adds process-level overhead. It provides crash isolation and works well for scaling I/O-heavy services. • Worker threads share process memory (via SharedArrayBuffer) and have lower overhead for CPU-bound work—but they introduce complexity (e.g. race conditions, threadpool sizing, message passing). • If your workload is heavily CPU bound, worker threads are better—but if it's mostly I/O-bound and you need more concurrency, clustering is simpler and effective. Community echo: “Excel parsing … took more time … moving to Worker threads … blazing fast.” — node dev on Reddit, reporting dramatic performance gains by using thread pools.  And cluster + PIscinia (worker pool lib) is used in production for ETL, PDF generation, etc.  Tags: Node.js, worker_threads, cluster, CPU bound, scaling, threadpool