Wisp Compute uses AI to automatically select and provision the optimal GPU, CPU, memory, and storage configuration for AI workloads across public clouds and on‑premise environments. A single CLI command or web dashboard handles provisioning, driver setup, environment transfer, and real‑time resource right‑sizing to reduce cost and operational overhead for data science and ML teams.
Funding
Funding not disclosed
Founders
Product
Problem
Data teams often struggle to select the optimal compute resources for AI workloads across multiple cloud providers and private infrastructure, leading to over‑provisioning, high costs, and delayed execution. Manual provisioning and configuration also introduce operational overhead and risk of misconfiguration.
Solution
Wisp Compute uses AI to analyze a workload’s requirements and predict the most suitable GPU, CPU, memory, storage, and interconnect configuration. With a single command, the platform automatically provisions the selected resources on the chosen cloud or on‑premise environment, configures drivers and dependencies, and transfers the execution environment. Real‑time utilization data is continuously collected to right‑size resources during runtime, reducing waste while maintaining performance. The system supports multi‑cloud brokering, allowing workloads to run on the most cost‑effective or contract‑bound resources across providers. Users interact via a CLI and a web dashboard that displays provisioning status, performance metrics, and cost insights, enabling teams to focus on code rather than infrastructure.
Target Audience
Primary customers are data science, machine learning, and AI engineering teams in enterprises and fast‑moving startups that run compute‑intensive workloads across public clouds or hybrid environments.
Features
- AI‑driven analysis (`wisp analyze -c`) that recommends exact GPU type, VRAM, driver version, vCPU count, memory latency, and storage for each workload
- Automated provisioning and configuration across public clouds (AWS, Azure, GCP) and private infrastructure with a single CLI command (`wisp run`)
- Real‑time utilization monitoring that continuously adjusts resource allocation to minimize idle capacity
- Multi‑cloud broker that selects resources based on price, committed spend, enterprise discount programs (EDP), or credits
- End‑to‑end workflow automation including environment transfer, SSH setup, and execution monitoring
- Web dashboard showing provisioning progress, performance metrics, and cost analytics
- Compatibility with a wide range of hardware generations and cloud regions, supporting both GPU‑intensive and CPU‑intensive jobs