How does NumStack calculate AI token costs?

NumStack uses real published pricing from OpenAI, Anthropic, Google, and other AI providers. Enter your typical usage (input tokens, output tokens, requests per day) to see projected monthly costs across all models.

Why do different AI models have different token prices?

Different models have different computational costs. Smaller models like GPT-4o mini or Haiku are cheaper but less capable. Larger models like GPT-4o or Claude Sonnet are more powerful but cost more per token.

How can I reduce my AI API costs?

Use smaller models for simple tasks, cache repeated prompts, trim output length (output tokens cost 3-5x more than input), and batch requests instead of real-time when latency allows.

Training Budget Estimator

Is fine-tuning worth it? Find out in 60 seconds

Compare training costs vs. off-the-shelf model inference over time. See your payback period and ROI.

Tell us about your project

This takes under 2 minutes

Is fine-tuning worth it?

Fine-tuning a model costs $500–$5,000 upfront but can save you thousands per year on inference. However, if you're only calling the model a few times, that training investment never pays back.

This calculator tells you the break-even point: how many inferences before your fine-tuned model becomes cheaper than using a general-purpose model. See payback period, ROI, and whether fine-tuning makes financial sense for your workload.

When fine-tuning wins

✓High inference volume: 1M+ inferences/month → payback in weeks
✓Domain-specific tasks: Customer service, coding, analysis in your industry
✓Latency-critical apps: Smaller fine-tuned models run faster locally
✓Quality gaps: General models are 70% accurate, fine-tuned hits 95%

When it doesn't make sense

✗Low volume: <100k inferences/month
✗General-purpose tasks: chatbots, summarization, Q&A where accuracy is 80%+
✗One-off projects: training data takes weeks to collect and label
✗New problem domains: you don't have training data yet

💰 Rule of thumb: If payback period is under 6 months, fine-tuning is probably worth exploring.