How do you make sure your API can handle lots of traffic?

Scaling APIs requires addressing bottlenecks at each layer and planning for growth.

Caching is the biggest win. Cache at the CDN/edge for public content. Use Redis or Memcached for application-level caching of expensive computations or database queries. Set appropriate cache headers for HTTP caching. The best request is one you don't have to process.

Horizontal scaling runs multiple instances of your API behind a load balancer. This requires stateless design—no local session storage, no local file state. Store sessions in Redis, files in S3, and any instance can handle any request.

Database optimization often becomes the bottleneck. Add indexes for common queries, use read replicas to spread read traffic, implement connection pooling, and consider caching frequently-accessed data. Monitor slow queries and optimize them.

Rate limiting protects against abuse and ensures fair usage. Implement per-user or per-IP limits. Return 429 Too Many Requests with Retry-After headers. Use sliding window or token bucket algorithms.

Async processing moves slow work out of request handlers. Return 202 Accepted and process in the background with job queues (BullMQ, SQS). Users get fast responses; heavy work happens asynchronously.

Monitor and measure. You can't optimize what you don't measure. Track response times, error rates, and resource usage. Set up alerts for anomalies. Performance regressions should be caught before users notice.