On December 3, OpenAI announced that it has signed a final acquisition agreement with the experimental management startup Neptune, with the transaction amount undisclosed. Neptune will phase out its external services, and its monitoring and debugging tools will be deeply integrated into OpenAI's training technology stack in the coming months, providing more granular real-time visibility for model iterations.

Jakub Pachocki, OpenAI's Chief Scientist, said that Neptune's "fast and accurate system" can help researchers analyze complex training processes. Both parties plan to jointly iterate, embedding capabilities such as experiment tracking, comparison, and automated alerts into their self-developed infrastructure. Over the past year, OpenAI has used Neptune dashboards to conduct multiple rounds of hyperparameter experiments. This acquisition aims to productize these successful experiences, lowering the barrier to large model training.

Piotr Niedźwiedź, CEO of Neptune, stated that the team will continue to serve existing customers during the transition period and then fully shift to internal support. The company had previously raised approximately $18 million in funding, with investors including Almaz Capital and TDJ Pitango Ventures.

Industry analysis points out that as the parameter scale of the GPT series continues to expand, experimental traceability and training stability have become core bottlenecks. Neptune's combination of "real-time monitoring + automated alerts + seamless CI/CD integration" is expected to shorten OpenAI's model iteration cycle and reduce computing power waste. After the transaction, the Neptune toolchain may open APIs for enterprise customers, further strengthening OpenAI's ecological advantages in training infrastructure.