# EigenPrompt

> EigenPrompt is a prompt optimization platform that uses multi-objective optimization to help LLM engineers reduce API costs and deploy AI features with confidence. It optimizes a prompt for each target model against your evaluation data, then surfaces the Pareto-optimal prompt-and-model pairings that cut cost without compromising quality.

Key facts: bring-your-own-keys (BYOK) for OpenAI, Anthropic, Groq and 100+ models; optimization runs are scored against your labelled evaluation dataset; if no better prompt is found, no credit is spent.

## Product

- [Home](https://eigenprompt.ai/): what EigenPrompt does and who it is for, including a 21-question FAQ
- [Guide](https://eigenprompt.ai/guide): practical guide to automated prompt optimization — how runs work, evaluation types, task suitability
- [Pricing](https://eigenprompt.ai/pricing): plans, credits, and the improvement guarantee
- [About](https://eigenprompt.ai/about): why EigenPrompt exists and the principles behind it
- [Contact](https://eigenprompt.ai/contact): product questions and enterprise inquiries

## Blog

- [Blog index](https://eigenprompt.ai/blog): evals, model trade-offs, and reproducible prompt-optimization case studies
- [Prompt optimization glossary](https://eigenprompt.ai/blog/prompt-optimization-glossary): definitions of core terms (Pareto frontier, eval set, LLM judge, …)

## Policies

- [Privacy Policy](https://eigenprompt.ai/privacy)
- [Terms of Service](https://eigenprompt.ai/terms)
- [Security](https://eigenprompt.ai/security)