SF Compute

Market position

SF Compute presents itself as a marketplace-based alternative to GPU clouds that require long fixed-price commitments, aiming to de-risk GPU offtake contracts and introduce liquidity and price discovery to compute. Press coverage describes it as a San Francisco-based startup in the AI compute marketplace segment; an investor quoted in that coverage frames the market as historically rigid and hard to access. Company commentary compares its model to providers such as CoreWeave, which it characterizes as focused on locked-in long-term contracts, and it named Modal, Together, and DigitalOcean in discussing GPU cloud economics.

The company contrasts itself with providers that require long fixed-price contracts by letting customers exit commitments through resale on its order book, and with brokers by operating the clusters directly on its own virtualization and bare-metal stack. It sells slivers of time on large interconnected clusters (for example a month of a 1,024-GPU H100 cluster rather than a year-long commitment) and allows tenants to earn revenue on idle capacity.

Technology

The company operates GPU clusters and the cloud layer above them, offering virtual machines, bare metal, and managed Slurm and Kubernetes on its own virtualization and bare-metal stack. A scheduler and order book match buy and sell orders for compute time, including standing orders and auto-scaling that buys and sells time as fleets scale. Users interact through an API and the "sf" CLI, which manages pools, orders, SKUs, images, and instances, with SSH access to instances. Clusters offer InfiniBand networking (for example 1,024 H100s with 3.2Tb/s InfiniBand) and petabyte-scale, high-speed storage suited to large-model training.

Go-to-market

Self-serve signup with CLI and API access for virtual machines, plus a demo request and direct sales engagement for bare-metal and custom cluster deployments; the company also publishes pricing and cluster availability on its site and markets through its blog and podcast appearances. It is hiring supply-chain-focused account executive, customer support, HPC/GPU cluster architect, design engineering, legal, and people operations roles.

AI and infrastructure teams that need large-scale training compute without multi-year commitments, including small startups that cannot underwrite long take-or-pay contracts, as well as enterprises and data centers with excess capacity to sell. The company notes its InfiniBand- and storage-heavy clusters suit large-model training more than steady-state inference workloads.

Geography

Headquartered in San Francisco, with cluster SKUs listed in documentation across regions including Amsterdam, Ashburn, Chicago, Seattle, and Tokyo.

History

Founded in 2023. An early blog post announced the company selling short-duration H100 training cluster time, with its first cluster, Angel Island, online and a second, Bay Bridge, due within months. CEO Evan Conrad discussed the marketplace model publicly in an April 2025 podcast. In late 2025 the company hired Eric Park, former CEO of AI cloud provider Voltage Park, as CTO, along with executives from Sun Microsystems and Lambda, and raised a $40 million Series A announced in December 2025.

Risks & controversies

The company's own materials note that GPU cluster financing carries thin margins and high volumes, that risk is typically pushed onto customers through fixed-price long-term contracts, and that unmitigated customer risk implies a bubble. It also states its InfiniBand- and storage-heavy clusters are a poor fit for steady inference demand, and that resale prices depend on market conditions, with orders cancelled if bid rates are too low or availability is absent.