AI Project Scope & Token Cost Estimator

Estimate token consumption and API costs for AI projects based on model selection, prompt complexity, and usage volume.

AI Model

Average Prompt (Input) Tokens per Request Include system prompt + user message. ~750 words ≈ 1,000 tokens.

Average Completion (Output) Tokens per Request Typical response length. ~750 words ≈ 1,000 tokens.

Requests per Day

Project Duration (Months)

Fine-Tuning Training Tokens (optional, 0 if none) Total tokens in your fine-tuning dataset. Fine-tuning pricing varies by model.

Embedding Tokens per Day (optional, 0 if none) For RAG/vector search pipelines. Uses text-embedding-3-small at $0.02/1M tokens.

Infrastructure & Overhead Buffer (%) Add buffer for retries, testing, prompt iteration, and unexpected spikes.

Fill in the fields above and click Calculate.

Daily Input Tokens = Avg Prompt Tokens × Requests per Day

Daily Output Tokens = Avg Completion Tokens × Requests per Day

Total Inference Tokens = (Daily Input + Daily Output) × (Duration Months × 30.44 days)

Input Cost = (Total Input Tokens ÷ 1,000,000) × Model Input Price per 1M

Output Cost = (Total Output Tokens ÷ 1,000,000) × Model Output Price per 1M

Embedding Cost = (Embedding Tokens/Day × Days) ÷ 1,000,000 × $0.02

Fine-Tuning Cost = (Fine-Tuning Tokens ÷ 1,000,000) × Model Fine-Tuning Price per 1M

Total Cost = (Input Cost + Output Cost + Embedding Cost + Fine-Tuning Cost) × (1 + Overhead% ÷ 100)

Monthly Cost = Total Cost ÷ Duration Months

Embedding cost uses OpenAI text-embedding-3-small at $0.02/1M tokens as a baseline.

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