About

OpenPipe is an LLM fine-tuning platform that helps developers replace expensive large model API calls with smaller, fine-tuned models that match or exceed the quality of GPT-4 on specific tasks at a fraction of the cost. The platform captures production logs from OpenAI and other providers, uses them as training data to fine-tune smaller open-source models, and provides a drop-in API replacement for seamless migration. OpenPipe is built for engineering teams looking to reduce LLM inference costs by 10-100x while maintaining output quality.

Tool Details Freemium

Pricing Freemium, from $0.50/1K training rows
Free Plan Yes
API Available Yes
4.6 1 vote

AI Reviews

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4.6 /5
OpenPipe is a compelling LLM fine-tuning platform that streamlines the process of creating custom models tailored to specific use cases. The platform excels at making fine-tuning accessible " users can capture production logs from OpenAI-compatible APIs, then use that data to train smaller, faster, and cheaper models that replicate the behavior of larger ones like GPT-4. This 'distillation' workflow is particularly powerful for teams looking to reduce inference costs without sacrificing quality. The API compatibility with OpenAI's format makes integration nearly seamless, requiring minimal code changes. The freemium pricing starting at $0.50 per 1K training rows is reasonable, and the potential cost savings on inference can be substantial. The built-in evaluation tools help users compare fine-tuned models against their original prompts, which is a thoughtful touch. Limitations include a somewhat narrower model selection compared to some competitors, and advanced customization options could be more extensive. Overall, OpenPipe delivers excellent value for production teams seeking to optimize their LLM costs while maintaining output quality.

Category Ratings

LLM Fine-Tuning
4.6
Feb 15, 2026
AI-Generated Review Generated via Anthropic API. This is an automated evaluation, not a consumer review. Learn more
OpenPipe Screenshot

Added: Feb 15, 2026

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