Token Intelligence
The practice of classifying every inference token by reusability before it reaches the model — so the most repeatable tokens are served from a verified per-tenant cache, and the novel ones pass through unchanged. Tokani coined the term in 2026.
The mechanism in three steps
- Classify. Every incoming prompt is bucketed by volatility — stable, semi-stable, or volatile — using normalization, embedding similarity, and signal heuristics. Volatile prompts are tagged and routed straight to the model with no cache lookup.
- Match. Stable and semi-stable prompts query a per-tenant cache (exact-match first, then semantic match within a confidence threshold). The threshold is workload-aware, so a chat product and a code-gen product use different similarity bars.
- Verify. Every cache hit is checked by a verifier model before being returned. If confidence drops below threshold, the engine falls through to a fresh model call. This is what makes cache reuse safe at production scale — no silent quality degradation.
Why a new category
Two adjacent categories already exist, and neither solves the problem token intelligence solves:
| Category | What it does | What it doesn't do |
|---|---|---|
| AI cost dashboards | Measure where your spend goes across providers | Don't reduce the bill — they only show it |
| Generic caching libraries | Hash-match or simple similarity reuse | No volatility classification → silently degrade quality on workloads with any freshness sensitivity |
| Token intelligence | Classify per-token reusability + verify every reuse | (This is the gap) |
Why it matters
Inference is now a top-3 line item for any team running production LLM workloads. Cost dashboards have made the bill visible — they haven't made it smaller. Token intelligence is the missing layer that turns visibility into reduction, without forcing the team to rewrite prompts, change providers, or trade quality for cost.
A second-order effect: serving cached responses uses orders of magnitude less compute than re-running inference. So the cost reduction token intelligence delivers also lowers each customer's AI emissions proportionally. Sustainability and savings move together — the rare case where you don't have to trade one for the other.
See your number
Tokani is the cost-intelligence layer that ships token intelligence to production LLM workloads. The calculator estimates your savings from your current usage in 30 seconds.