How voice agent component costs and estimates work

Learn what drives costs across the transcriber, model, and voice.

The cost to run a voice agent mainly depends on the costs of the transcriber, LLM, and voice. Each component uses a different billing unit, so we’ve made some assumptions in order to provide a way for you to compare estimated cost per minute.

The Cost number within Performance Metrics provide estimates for comparison rather than exact quotes. Use them to compare models consistently and understand what affects cost. See Vapi pricing for current pricing. For contracted customers, certain pricing may depend on your agreement, please contact your account team for contract-specific rates.

Key concepts

TermDefinition
Input tokens per minuteTokens sent to the model each minute based on your prompt size, tool-definition size, and a typical request rate.
Effective input rateThe per-token input rate blended between standard and cached rates when the model supports prompt caching.
Output tokens per minuteTokens the model generates each minute based on typical Vapi call data.

Component billing units

Each component uses a different billing unit.

ComponentBilling unit
TranscriberMinutes of audio
Model1 million input and output tokens
VoiceCharacters of spoken text

Transcriber cost

Transcription is billed per minute of audio. Vapi transcribes audio from both the caller and the assistant. The estimate therefore includes two audio channels. This cost scales with call duration rather than your prompt or configuration.

Voice cost

The Voice model is billed per character of spoken text. The estimate uses the number of characters spoken in a typical minute. Cost increases when the assistant speaks more.

LLM cost

Model cost varies more than transcriber or voice cost because it heavily depends on the assistant’s configuration. Vapi calculates the estimate from the assistant’s actual prompt and tool definitions, so two assistants using the same LLM model can show different cost estimates.

The estimate uses each provider’s input and output token rates.

CalculationFormula
Input costInput tokens per minute × effective input rate
Output costOutput tokens per minute × output rate
Estimated model cost per minuteInput cost + output cost

Input tokens per minute

Input tokens per minute is the largest cost factor you can control. Vapi calculates it from your system prompt, tool definitions, and a typical number of model requests per minute. Longer prompts and more tools increase the estimate, while leaner configurations reduce it.

Vapi estimates 5 requests per turn. We use number of requests rather than conversational turns, since a single turn can trigger multiple requests, and providers bill each request.

Effective input rate

Effective input rate accounts for prompt caching. Cached input is stable prompt content that providers reuse across requests. Many providers offer a discounted rate for this input.

When a model supports caching, Vapi blends its standard and cached rates based on an assumed 50% cache hit rate. Otherwise, Vapi uses the standard rate. The calculation uses the actual rates for each model and provider.

Output tokens per minute

Vapi estimates 150 output tokens per minute based on typical generated speech in production call data. It prices those tokens at the model’s output rate.

Estimate limitations

Two assumptions can cause actual model cost to differ from the estimate.

ScenarioTypical effectReason
Large prompts with caching supportedActual cost is often lowerActual cache use is usually higher than the estimate assumes
Long callsActual cost is often higherGrowing conversation history is sent again with each request

These deliberate simplifications keep estimates comparable across models. Vapi may refine them over time.

Reduce cost

You can reduce cost in four ways.

  • Shorten your system prompt and tool definitions. This often has the largest effect on model cost.
  • Use models that support prompt caching for large, stable prompts. Providers charge less for prompt content reused from cache, which lowers model cost.
  • Choose a lower-cost model when the use case allows. The Cost Saver preset optimizes components for the lowest cost per minute.
  • Keep calls focused. Shorter, more focused conversations cost less and tend to provide a better experience.