VESSL AI Secures $12M Series A to Disrupt MLOps with Hybrid Cloud Efficiency
As artificial intelligence adoption surges across industries, enterprises face mounting challenges in developing and deploying machine learning models efficiently. Enter VESSL AI, a South Korean MLOps startup that just secured $12 million in Series A funding to accelerate its mission of slashing GPU costs by up to 80% through innovative hybrid infrastructure solutions.
The Growing MLOps Landscape
The MLOps (Machine Learning Operations) market has become increasingly competitive, with players like:
- InfuseAI
- Comet
- Arrikto
- Arize
- Galileo
- Tecton
- Diveplane
Not to mention cloud giants like Google Cloud, Azure, and AWS offering their own solutions. VESSL AI differentiates itself by focusing on cost optimization and hybrid infrastructure that blends on-premise and cloud environments.
Funding and Strategic Growth
The \(12 million investment brings VESSL AI's total funding to **\)16.8 million**. The round was led by:
- A Ventures
- Ubiquoss Investment
- Mirae Asset Securities
- Sirius Investment
- SJ Investment Partners
- Wooshin Venture Investment
- Shinhan Venture Investment
With 35 employees across South Korea and San Mateo, California, the startup is well-positioned for global expansion.
Enterprise Adoption and Key Partnerships
VESSL AI already boasts an impressive roster of 50 enterprise customers, including:
- Hyundai
- LIG Nex1 (aerospace/defense)
- TMAP Mobility (Uber/SK Telecom joint venture)
- Tech innovators Yanolja, Upstage, ScatterLab, and Wrtn.ai
The company has also formed strategic partnerships with Oracle and Google Cloud, serving over 2,000 users worldwide.
The VESSL AI Advantage: Cutting Costs Without Compromise
Founded in 2020 by ex-Google, PUBG, and AI startup veterans, VESSL AI addresses a critical pain point: the exorbitant costs and operational complexity of developing ML models. Their solution leverages:
- Multi-cloud strategy: Dynamically selects the most cost-effective GPUs across providers (AWS, Google Cloud, Lambda)
- Spot instances: Maximizes cost efficiency
- Hybrid infrastructure: Combines on-premise and cloud resources
“Our system automatically selects the most efficient resources, reducing customer costs while maintaining performance,” explains CEO Jaeman Kuss An.
Platform Capabilities
VESSL AI’s comprehensive MLOps platform offers:
- VESSL Run: Automated AI model training
- VESSL Serve: Real-time deployment support
- VESSL Pipelines: Integrated model training and data preprocessing
- VESSL Cluster: Optimized GPU resource management
These features address the full ML lifecycle while solving two critical industry challenges: GPU shortages and skyrocketing cloud costs.
The Future of Efficient AI Development
As enterprises increasingly develop custom LLMs and vertical AI agents, VESSL AI’s solution provides a compelling value proposition. By dramatically reducing infrastructure costs while streamlining workflows, the startup is well-positioned to become a key player in the global MLOps market.
“Enterprise companies find MLOps critical for reliability and performance” - TechCrunch
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