A student's demo went viral on a class WhatsApp group, a retry loop in the frontend fired on every failed request, and the month's API budget was gone in under an hour. Rate limiting your own endpoint would have prevented it.
The minimum viable setup
- Per-IP or per-user request limits on your own API, before the request ever reaches the LLM provider.
- A hard daily spend cap enforced in code, not just watched on a dashboard after the fact.
- Exponential backoff on your OWN retries, so a transient failure doesn't turn into a request storm.
Where to put it
At the API gateway or middleware layer, before your handler runs — never rely on the frontend to behave, since a bug, a bot, or a malicious user can bypass frontend logic entirely.
See LLM cost control and token budgets for the budget side of this same problem.
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.
