VESSL AI
VESSL AI provides an MLOps platform designed for high-performance machine learning teams. The platform offers a suite of features including VESSL Hub for discovering and deploying open-source models with zero setup, VESSL Run for training models with a single command and flexible cloud deployment, VESSL Service for serverless model deployment with persistent endpoints and real-time monitoring, VESSL Cluster for a unified interface to manage compute resources across different infrastructures, and VESSL Pipeline for automating AI workflows with CI/CD. The company emphasizes enabling teams to operationalize full-spectrum AI and LLMs, offering features like A100 GPUs for $1.8/h, cost efficiency through spot instances, and streamlined data pipelines that can reduce model deployment time significantly. VESSL AI aims to empower ambitious AI projects by simplifying complex infrastructure management and accelerating the machine learning lifecycle.
VESSL AI
VESSL AI provides an MLOps platform designed for high-performance machine learning teams. The platform offers a suite of features including VESSL Hub for discovering and deploying open-source models with zero setup, VESSL Run for training models with a single command and flexible cloud deployment, VESSL Service for serverless model deployment with persistent endpoints and real-time monitoring, VESSL Cluster for a unified interface to manage compute resources across different infrastructures, and VESSL Pipeline for automating AI workflows with CI/CD. The company emphasizes enabling teams to operationalize full-spectrum AI and LLMs, offering features like A100 GPUs for $1.8/h, cost efficiency through spot instances, and streamlined data pipelines that can reduce model deployment time significantly. VESSL AI aims to empower ambitious AI projects by simplifying complex infrastructure management and accelerating the machine learning lifecycle.
TL;DR
What they do: End-to-end MLOps platform and persistent GPU cloud for building, training, and deploying ML/LLM workloads
Founded: 2020
Team size (approx.): 42 employees
Funding: Raised Series A $12M (total funding reported ~$16.8M)
Tech focus: Multi-cloud GPU access (H100/A100/H200/B200), orchestration, CI/CD pipelines, serverless model deployment
Company Overview
Problem Domain
Operationalizing machine learning and LLMs at scale, reducing GPU cost and infrastructure complexity.
Founded
2020
Industry
MLOps / AI Infrastructure
Tech Stack
Funding Track Record
12000000.00
Round reported to bring total funding to approximately $16.8M
Investor Signal
“Includes venture investors such as A Ventures, Ubiquoss, Mirae Asset, Sirius Investment, SJ Investment Partners, Wooshin Venture Investment”