Voice AI Cache Efficiency: How Prompt Caching Cuts Voice AI Costs

Cache efficiency in voice AI measures the percentage of processed tokens that can be reused across voice AI conversations instead of being generated fresh. Each cached token costs 90-97% less than a new token. Higher cache efficiency directly translates to lower cost per minute.

Caching reduces voice AI costs because AI providers like OpenAI charge significantly less for cached tokens -- typically 90-97% less than new tokens. The more of each request that is served from cache, the less is processed at full price. Loqui Auris uses prompt caching and measured about $0.02–0.03 per minute in July 2026, versus an estimated $0.08-0.12 for uncached implementations.

For voice AI, a higher cache efficiency rate is better: every cached token is billed at a steep discount, so the more of each request that is served from cache, the lower the cost per conversation. Loqui Auris uses prompt caching in production but does not publish a cache-efficiency percentage.

Loqui Auris uses a patent-pending architecture with three key techniques: (1) persistent context windowing that keeps system prompts and user knowledge in cache across sessions, (2) a provider-agnostic pipeline that caches at each processing stage (STT, LLM, TTS) independently, and (3) intelligent token ordering that maximizes cache hit probability by placing stable content first in the context window.

Yes — you can improve voice AI cache efficiency: start by placing stable content (system prompts, user profiles) at the beginning of your context window -- cached tokens must be contiguous from the start. Reuse conversation templates for common interactions. Implement session-level caching so returning users benefit from prior context. Finally, use a provider-agnostic architecture that enables caching at each pipeline stage rather than relying on a single API call.