LLM Cost Controls for AI Assistants

Great AI answers are only sustainable if you keep LLM spend predictable. These controls deliver consistency without degrading quality.

1. Tiered models

• Default to cost-efficient models (Gemini) for standard plans.
• Allow enterprise upgrades to higher tiers (e.g., GPT-4) for specific contexts.
• Document model selection per tenant in the admin UI.

2. Per-tenant quotas

• Track messages, tokens, and crawl minutes per tenant.
• Disable or degrade functionality (e.g., turn off follow-up questions) once quotas are hit.
• Send proactive alerts (email, Chat) before enforcement kicks in.

3. Caching and reuse

• Cache recent Q&A pairs with TTL; serve instantly when the same question repeats.
• Use semantic deduplication to avoid re-answering duplicates inside a short window.
• Record cache hits vs misses to justify model costs.

4. Retries and fallbacks

• Limit retries to avoid runaway costs when providers fail.
• When failover occurs, log provider usage and token counts for billing reconciliation.
• Consider low-cost fallback responses (“I’m checking on that…”) when both providers fail.

5. Observability

• Log token usage per tenant and per provider; expose dashboards.
• Compare token consumption against plan allowances and actual invoices.
• Use alerts when usage deviates from forecast (±20 percent).

CrawlBot practices

CrawlBot’s billing service enforces quotas, tracks token usage, and exposes per-tenant dashboards. Adopt similar controls to keep AI assistants profitable.***