Most WordPress chatbots that report on token usage stop at raw counts. You see that a conversation used 4,287 input tokens and 1,124 output tokens, and then you have to mentally multiply by whichever rate your provider charges, divided by a million, in the right currency, accounting for the model you picked. By the time you've done that for every conversation in a week, you've wasted an hour and still don't trust the total because you might have used the wrong rate.

SleekAI does the math for you. The plugin ships with rate cards for OpenAI, Anthropic, Google, and OpenRouter that are kept current with each plugin update. Every conversation turn logs both the token count and the dollar cost computed using the rate for the exact model that handled it. The admin dashboard groups costs by bot, by model, by date, and by originating page, so you can see at a glance which chatbots are expensive and which pages drive the most spend.

Generic platforms report total monthly cost as a single number on the invoice, which gives you no signal about where the spend is concentrated. Self-hosted bots without cost tracking leave you guessing entirely. SleekAI brings the granularity of provider dashboards into your own admin, with the WordPress-specific context (which page, which bot, which user role) that the providers themselves can never know about your traffic.

AI spend grows nonlinearly with usage because longer conversations consume disproportionately more tokens. A chat that runs five turns at 1,000 tokens each does not cost five times a single-turn chat. Once context windows fill, costs jump.

Without granular tracking, this growth is invisible until the invoice lands and someone asks why the line item tripled. By then it's too late to intervene for the month. With per-turn cost data in your own admin, you see expensive conversations within hours of them happening.

Configuration changes that reduce cost (switching to a cheaper model on a high-volume bot, shortening system prompts, capping context window size) become testable. You can run a model change for a day, compare cost-per-conversation before and after, and decide based on data instead of intuition. The product feedback loop tightens.

Bots that surprise you in cost become bots you redesign with explicit budgets in mind. Bots that come in cheaper than expected become candidates to upgrade to a smarter model. None of this is possible when cost is a single number on a monthly invoice that arrives weeks after the spend happened.