With the rapid development of artificial intelligence technology, enterprises have an increasing demand for machine learning operating platforms (MLOps). Many enterprises are looking for efficient and cost-effective solutions to build, test and deploy machine learning models. South Korean AI company VESSL AI has taken a different approach, focusing on optimizing GPU costs and providing enterprises with a more cost-effective MLOps platform, and has achieved significant success. The editor of Downcodes will explain VESSL AI and its unique positioning in the AI market in detail.
As artificial intelligence becomes more and more integrated into enterprise workflows and products, the demand for machine learning operations platforms (MLOps) is also rising. Such platforms help enterprises create, test and deploy machine learning models more easily. However, although there are many competitors in the market, such as startups such as InfuseAIComet, as well as large companies such as Google Cloud, Azure and AWS, South Korea's VESSL AI hopes to find its own development space by focusing on the optimization of GPU costs.
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Recently, VESSL AI successfully completed a $12 million Series A round of financing, aiming to accelerate the development of its infrastructure and mainly serve enterprises that want to develop customized large language models (LLMs) and vertical AI agents. The company currently has 50 corporate customers, including well-known companies such as Hyundai Motor, LIG Nex1 (a South Korean aviation and weapons manufacturer), and TMAP Mobility (a joint venture between Uber and SK Telecom). In addition, VESSL AI has also established strategic cooperation with companies in the United States such as Oracle and Google Cloud.
The founding team of VESSL AI consists of Jaeman Kuss An (CEO), Jihwan Jay Chun (Chief Technology Officer), Intae Ryoo (Chief Product Officer) and Yongseon Sean Lee (Technical Lead). Before setting up the company, they worked at Google, PUBG famous companies and some AI startups. When founder An developed machine learning models at his previous medical technology company, he found the process cumbersome and resource-consuming, so they decided to leverage hybrid infrastructure to increase efficiency and reduce costs.
VESSL AI's MLOps platform adopts a multi-cloud strategy to help enterprises reduce GPU expenses by up to 80% by using GPUs from different cloud service providers. This approach not only solves the problem of GPU shortage, but also optimizes the training, deployment and operation of AI models, especially the management of large language models. An mentioned that the system can automatically select the most cost-effective and efficient resources to save customers money.
VESSL's products have four core functions, including: VESSL Run (automated AI model training), VESSL Serve (supporting real-time deployment), VESSL Pipelines (integrating model training and data preprocessing to simplify workflow), and VESSL Cluster (optimizing clusters) GPU resource usage in the environment). After this round of financing, VESSL AI's total financing has reached $16.8 million, and the company has 35 employees in South Korea and San Mateo, the United States.
Highlight:
VESSL AI completed US$12 million in Series A financing and is committed to optimizing enterprise GPU costs.
? Currently has 50 corporate customers, including well-known companies such as Hyundai Motor and LIG Nex1.
The platform reduces GPU costs by up to 80% through a multi-cloud strategy and provides multiple core functions.
All in all, VESSL AI provides enterprises with an efficient and economical MLOps platform through its unique GPU cost optimization strategy and has carved a niche in the market. Its successful financing and many high-profile customers also prove the value and market potential of its technology. In the future, the development of VESSL AI deserves continued attention.