How we avoided burning through an AI API after a viral launch
AI CareerBot saw viral growth, and along with the traffic, GigaChat API costs spiked sharply. Here’s how we introduced budgeting, limits, caching, and real-time metrics.
With AI CareerBot, we got that very specific kind of “good problem”: the product took off, and along with the traffic, costs for the GigaChat API exploded exponentially. Classic 2026: you’re happy about the metrics, then you open billing — and suddenly you want a stronger coffee.
What we did: — introduced budgeting for spend; — set per-user limits, so one enthusiastic user wouldn’t eat the entire monthly budget in an evening; — added caching for common FAQ-like requests; — turned on real-time spend metrics.
The most important part was graceful degradation: when the limit is near, the bot doesn’t “die” — it warns the user honestly instead of failing outright. The UX stays alive, and so do the finances.
In practice, in the age of AI hype, the winner isn’t only the one with the smarter model, but the one whose cost control is not at the “we’ll deal with it later” level.