Question 1

How do I avoid hitting API rate limits?

Accepted Answer

Strategies: (1) Request queuing — add all requests to a queue, process at controlled rate with exponential backoff on 429s; (2) Caching — cache identical responses, especially read endpoints; (3) Request batching — combine multiple operations into one API call when available; (4) Rate limiter libraries — use Bottleneck (Node.js), ratelimiter (Python); (5) Distribute load across API keys (check ToS first).

Question 2

What happens when you exceed API rate limits?

Accepted Answer

APIs return HTTP 429 Too Many Requests with a Retry-After header indicating when to retry. Without proper handling: unhandled errors in production, failed user requests, cascading failures if your service doesn't implement backoff. Implement exponential backoff: wait 1s, then 2s, 4s, 8s, 16s before giving up. Use jitter (random 0-100ms) to prevent thundering herd.

Question 3

How do I calculate my API rate limit needs?

Accepted Answer

Formula: Required RPM = Concurrent Users × Requests Per User Action × Actions Per Minute. Example: 100 users, 5 API calls per search, 2 searches/minute = 1,000 RPM needed. Add 30% buffer for spikes. Compare to plan limit and choose the tier that supports 1.3x your expected peak load. Monitor actual usage in production and set alerts at 80% of limit.

Question 4

When should I self-host to avoid rate limits?

Accepted Answer

Consider self-hosting when: monthly API costs exceed $500-1,000, rate limits consistently constrain growth, you need more than 10,000 RPM, or data privacy requires on-premise processing. For LLMs: run Llama 3.1 70B on 2x A100s (~$3,000/month) vs OpenAI API at similar volume costing $5,000-10,000/month. For Postgres: RDS vs Neon, break-even typically at 100GB+ data or 10M+ queries/month.

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