VESSL AI Raises $12M Series A for MLOps Platform, Optimizing GPU Costs for AI Models

VESSL AI, a South Korean MLOps platform, has raised $12 million in a Series A funding round to expand its hybrid infrastructure, designed to optimize GPU costs for AI model training and deployment.

Founded in 2020, the company focuses on using a multi-cloud approach, helping enterprises reduce GPU expenses by up to 80%.

With 50 enterprise clients and strategic partnerships with Google Cloud and Oracle, VESSL AI is positioning itself as a key player in the MLOps space, particularly for businesses developing large language models and AI solutions.

Currently, VESSL AI is attempting to establish a name for itself by concentrating on GPU expense optimization using hybrid infrastructure—a combination of on-premise and cloud environments. To expedite the development of its infrastructure and cater to businesses seeking to create customized large language models (LLMs) and vertical AI agents, the startup has now raised $12 million in a Series A fundraising round.

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50 enterprise customers have already signed up with the company; these include tech startups Yanolja, Upstage, ScatterLab, and Wrtn.ai, as well as well-known brands like Hyundai, LIG Nex1, a South Korean aerospace and weapons manufacturer, and TMAP Mobility, a mobility-as-a-service joint venture between Uber and Korean telco company SK Telecom. Additionally, the business has strategic partnerships with Google Cloud and Oracle in the United States. 

CEO and co-founder Jaeman Kuss founded the company in 2020 with Jihwan Jay Chun (CTO), Intae Ryoo (CPO), and Yongseon Sean Lee (tech lead) to address a specific issue that he encountered while developing machine learning models at a previous medical tech startup. The founders had previously worked at Google, the mobile game company PUBG, and several AI startups.

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GPUs from many cloud service providers, including AWS, Google Cloud, and Lambda, can be used thanks to VESSL AI’s multi-cloud approach, according to An. “This system drastically lowers customer costs by automatically choosing the most economical and effective resources.”

The four primary features of the VESSL platform are VESSL Run, which automates the training of AI models; VESSL Serve, which facilitates real-time deployment; VESSL Pipelines, which streamlines workflows by integrating model training and data preprocessing; and VESSL Cluster, which maximizes the use of GPU resources in a cluster environment.

The company has raised a total of $16.8 million through investments from A Ventures, Ubiquoss Investment, Mirae Asset Securities, Sirius Investment, SJ Investment Partners, Wooshin Venture Investment, and Shinhan Venture Investment. These investors participated in the Series A round. The startup employs 35 people at its San Mateo, California, location and in South Korea.

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