VESSL AI Recognized as a Global Leader in Model Deployment & Serving by CB Insights

Global recognition of us is more than prestige; it signals a rising foothold in the infrastructure behind next-gen AI agents. In its latest mapping of 135+ promising companies building the AI agent tech stack, CB Insights highlighted VESSL AI alongside Korean peers Dnotitia and Upstage.

What CB Insights saw in VESSL AI: Deployment & Serving with usage-based GPUs

CB Insights places VESSL AI in Model Deployment & Serving under “AI Foundation Models & Infrastructure,” while Dnotitia appears in Vector Databases and Upstage in LLM Data Preparation (Context).

Crucially, their commentary captures our current focus:

“VESSL AI and Modal are winning with pay-per-use GPU compute models.”

In other words, the deployment layer is being won by teams that excel at GPUOps (operational efficiency) and GPUaaS (usage-based access to GPU compute).

What we’re doubling down on: GPUOps & GPUaaS

• Usage-based GPU orchestration across multi-cloud and on-prem with clear cost visibility—driving down cost per inference. (Peers’ public pricing shows the same directional shift to pay-as-you-go.)
• SLO-aware autoscaling to thousands of GPUs and back to zero, balancing latency and reliability under bursty loads.
• A unified train→fine-tune→serve pipeline with declarative specs, checkpointing, rollbacks, and built-in recovery.
• Enterprise-grade private deployments for security-sensitive industries.

The broader field & where it’s heading

CB Insights’ category also features Databricks, Baseten, Together AI, and Modal, reflecting the industry’s shift toward serverless inference, usage-based pricing, and developer-friendly abstractions.

What’s next

CB Insights projects that platforms combining efficient compute, intelligent orchestration, and developer-friendly abstractions will build defensible moats—and become prime acquisition targets as categories converge. The Korean press has likewise reported the inclusion of Dnotitia, VESSL AI, and Upstage in the global list and summarized these themes.

If you’re scaling enterprise AI deployment/serving, and want the speed and cost control of GPUOps & GPUaaS, let’s talk.