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For AI Startups

AI Startup
Financial Tools

Calculate LLM costs, plan infrastructure capacity, and model unit economics for AI-native products. Don't let API costs destroy your margins.

Current LLM Pricing (2025)

ProviderInputOutput
OpenAI GPT-4 Turbo$10/M tokens$30/M tokens
OpenAI GPT-4o$2.50/M tokens$10/M tokens
Claude 3.5 Sonnet$3/M tokens$15/M tokens
Claude 3 Opus$15/M tokens$75/M tokens
Gemini 1.5 Pro$1.25/M tokens$5/M tokens
Llama 3 (self-hosted)Compute cost~$0.50-2/M*

* Self-hosted costs vary by hardware. Prices as of Jan 2025.

AI Startup Financial Challenges

Challenge: API costs scale with usage

Solution: Model per-query costs and pass-through to customers or optimize prompts

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Challenge: Inference costs eat margins

Solution: Calculate true unit economics including compute costs

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Challenge: GPU/compute is expensive

Solution: Plan infrastructure capacity and right-size resources

Calculate →

Challenge: High burn rate pre-revenue

Solution: Track runway obsessively, raise with buffer

Calculate →

Unit Economics for AI Products

The key challenge: Inference costs are variable. Unlike traditional SaaS where compute is relatively fixed, AI products have costs that scale with each API call.

Calculate per-query cost:

Cost per query = (Input tokens × Input price) + (Output tokens × Output price)

Example (GPT-4 Turbo):
- 500 input tokens × $0.00001 = $0.005
- 200 output tokens × $0.00003 = $0.006
- Total: $0.011 per query

If your average customer makes 1,000 queries/month and pays $50/month, your cost is $11/customer, leaving $39 gross margin (78%).

Track closely: As usage grows, costs can surprise you. Model different usage scenarios in your financial projections.

Model Your AI Economics

Understand costs before you scale. Optimize margins before you raise.

Start Calculating