How to Cut LLM Costs with Helicone Caching and Alerts

Most teams add Helicone for logging, then leave it read-only. The savings come from acting on the data: caching repeat prompts, alerting on spikes, and attributing cost to features. This guide is for teams already logging who want the bill down.

Where does LLM spend usually leak?

Three places. Redundant prompts that return the same answer, retry loops that fire on a transient error and never stop, and one heavy feature that quietly dominates total cost. Logging exposes the third; caching and guards fix the first two.

How do you turn on caching?

1. Enable caching in the Helicone proxy settings for the routes that are safe to cache.
2. Send identical prompts twice and confirm the second shows as a cache hit with near-zero cost.
3. Add a cache-bypass header to anything personalised or time-sensitive.

Cache-friendly prompts are usually classification, extraction, and fixed system-plus-few-shot calls. Chat with per-user context is not cacheable and should not be.

How do you attribute cost to a feature?

Tag requests with custom properties — feature name, customer tier, environment — when you send them. Then filter the dashboard by property to see which feature costs most per request. This is how you find the expensive prompt hiding behind a friendly UI, and it takes one extra header at call time.

How do you set alerts that matter?

• Alert on daily spend crossing a threshold — not on total, which only grows.
• Alert on error-rate spikes, which usually precede retry-driven cost spikes.
• Alert on p95 latency jumps after a model or prompt change.

Route alerts somewhere humans look. A dashboard nobody opens is not monitoring.

What can go wrong with caching?

Stale answers. If a prompt embeds a date or a user ID but you cached it anyway, users get the wrong result and you rarely notice. Audit cacheable routes quarterly, and keep bypass headers on anything that touches personal data. Also remember the proxy sees prompts, so scrub secrets before logging.

Quick Summary

• Spend leaks through redundant prompts, retry loops and one dominant feature.
• Enable caching for classification and extraction routes; bypass it for personalised calls.
• Tag requests by feature and tier to attribute cost.
• Alert on daily spend, error spikes and p95 latency, routed to where people look.
• Caching risks stale answers — audit cacheable routes and scrub secrets.