Daytona reached $1M forward revenue run rate in under three months. Six weeks later, it doubled.

The speed reflects enterprise urgency around a fundamental infrastructure gap: current cloud platforms were built for production workloads—stateless, immutable systems optimized to run the same code the same way every time. AI agents need the opposite: persistent, stateful environments where they can experiment, branch execution paths, and recover from failures at massive scale.

The Infrastructure Mismatch

Enterprise adoption of AI agents is hitting a hard infrastructure bottleneck. While agents can reason through complex workflows and make decisions, they break when deployment infrastructure treats every workload like a web server.

SF Compute’s $40 million Series A addresses a $11.76 billion infrastructure bottleneck: AI startups locked into 12-36 month GPU contracts while serving customers with sporadic usage patterns. The San Francisco-based startup has created a marketplace allowing companies to resell unused compute capacity, managing over $100 million in hardware across several thousand GPUs.

The funding round, led by DCVC and Wing Venture Capital with participation from Electric Capital and Alt Capital, values the company at $300 million. This represents a growing recognition that AI infrastructure financing models have created hidden systemic risks across the ecosystem.