OpenPipe: LLM Fine-Tuning for Production AI | hokai.io
OpenPipe turns production LLM logs into fine-tuned models that beat GPT-4o at 1/10th the cost. 30-day free trial. Acquired by CoreWeave 2025. ART RL open-source.
OpenPipe is an LLM fine-tuning platform founded in 2023, backed by Y Combinator and $6.7M in seed funding, and acquired by CoreWeave in September 2025. It captures production LLM API logs, trains specialist models on that data, and deploys them as drop-in API replacements. Fine-tuned Llama 3.1 models have outperformed GPT-4o on task-specific benchmarks at significantly lower per-token costs. A 30-day free trial is available; pricing is usage-based per token thereafter. ART, its GRPO-based RL library, is open-source on GitHub.
Pricing
30-day free trial. Usage-based pricing: charged per token for training and inference. Enterprise plans with custom pricing available. No permanent free tier. Contact sales for current per-token rates.
Frequently Asked Questions
What is OpenPipe and what does it do?
OpenPipe is an LLM fine-tuning and reinforcement learning platform founded in 2023 through Y Combinator and acquired by CoreWeave in September 2025. It captures production LLM API calls from GPT-4, Claude, or other frontier models, curates them into training datasets, fine-tunes smaller open models like Llama 3.1 on that data, and deploys the results as API endpoints that replace the original expensive model. Customer evaluations have shown fine-tuned models outperforming GPT-4o on task-specific benchmarks at significantly lower cost.
How much does OpenPipe cost?
OpenPipe does not have a permanent free tier but offers a 30-day free trial. After the trial, pricing is usage-based, charged per token for both training runs and inference on deployed fine-tuned models. Enterprise plans with custom pricing are available for high-volume teams. Because pricing is token-based, costs scale with both the volume of training data and the number of production inference calls on the fine-tuned endpoint. Contact OpenPipe's sales team for current per-token rates.
What are the main features of OpenPipe?
OpenPipe's core features include production log capture via a drop-in SDK, automated fine-tuning of open models (Llama 3.1, Qwen, Mistral) with custom hyperparameters, one-click API deployment of fine-tuned models, side-by-side model evaluation using LLM-as-judge scoring, automatic retraining as new production data accumulates, and the ART (Agent Reinforcement Trainer) open-source library for GRPO-based reinforcement learning on multi-step agentic tasks.
Is OpenPipe free to use?
OpenPipe is not permanently free but provides a 30-day free trial that gives access to the full platform. There is no ongoing free tier after the trial period. The ART (Agent Reinforcement Trainer) library is fully open-source and available at no cost on GitHub and PyPI, making it free for developers who want to run their own RL training infrastructure without using OpenPipe's managed platform.
What are the best alternatives to OpenPipe?
The main alternatives are Predibase (acquired by Rubrik in 2025, now focused on agentic AI governance), Together AI (pay-as-you-go fine-tuning with a large open model catalog), and Fireworks AI (combines post-training with high-performance inference). Predibase is preferred by ML platform teams managing many adapters across a model fleet. Together AI is more cost-predictable for teams with variable traffic. OpenPipe differentiates itself through production log capture as the training data source and the ART open-source RL framework.
Who is OpenPipe best for?
OpenPipe is best for AI engineering teams at Series A-C companies running GPT-4 or Claude at scale for repetitive, high-volume tasks like classification, extraction, or structured data generation, where LLM API costs have become a material budget line. It is also well-suited for ML engineers building multi-step LLM agents who need RL-based training to improve agentic task success rates beyond what supervised fine-tuning achieves. It is not practical for early-stage products with fewer than 10,000 monthly LLM calls, as the training dataset will be too small for reliable fine-tuning.
Does OpenPipe have an API?
Yes, OpenPipe deploys fine-tuned models as API endpoints that are drop-in replacements for the OpenAI or Anthropic API endpoints, requiring only a model name change in the existing client code. The OpenPipe SDK is available for Python and TypeScript and is installed via pip or npm. The ART library is also available on PyPI (openpipe-art) for programmatic reinforcement learning training. Documentation is available at docs.openpipe.ai, and the GitHub repositories at github.com/OpenPipe provide the source code for both the platform SDK and the ART library.