Dedicated compute for training, inference, and the workloads that scale with your product. Arc Compute helps AI startups move off cloud GPUs, reduce per-hour costs, and build infrastructure that grows with the company.
Most AI startups begin on cloud GPUs because it is the fastest way to get something running. But as training runs get longer and inference volume grows, cloud spend becomes the biggest line item on the P&L. At some point the math stops working. The per-hour cost of renting GPUs starts eating into the unit economics you need to build a real business, and the cloud bill becomes the gating constraint on how aggressively you can ship.
Train proprietary models on your own data with full control over the training environment. Dedicated infrastructure removes cloud queuing, noisy-neighbor performance variance, and the per-hour cost pressure that forces you to cut training runs short before convergence.
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Serve your models with predictable latency, high throughput, and a cost-to-serve you can actually forecast. Dedicated inference infrastructure lets you scale with users without watching margin shrink as load grows.
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Run continuous fine-tuning, RLHF, and evaluation loops on infrastructure that is always available. No waiting for spot instances, no preemptions mid-run, and no throttling when you need capacity most.
The right infrastructure model for a startup depends on where you are in your trajectory. A pre-revenue team with 18 months of runway has different constraints than a Series B company with product-market fit and growing inference demand. Arc Compute supports both ends of the spectrum and the path between them.
Purchase your GPU systems outright and deploy them in a colocation facility. You own the hardware, control the full stack, and benefit from a per-GPU-hour cost that is a fraction of cloud pricing. For teams with sustained, high-utilization workloads, ownership often pays for itself within 12 to 18 months.
Funded startups with predictable compute needs, high utilization, and a clear case for taking per-unit compute costs out of the equation. Common among teams training proprietary models or running production AI products at scale.
Access GPU infrastructure through leasing, managed services, or consumption-based models. You get dedicated performance without a large upfront capital commitment, with the flexibility to scale capacity as your product grows and your compute profile gets clearer.
Earlier-stage teams, project-based workloads, or companies that want to validate dedicated infrastructure economics before committing capital. Also a fit when the board or investors prefer operating expenses over capital outlays during a growth phase.
Most startups land on a sequence. Begin with OPEX to prove that dedicated infrastructure works for your specific workload and pricing model. Transition to owned hardware once utilization and unit economics are proven. Arc Compute supports this progression and can structure deals so the move from leasing to ownership does not disrupt the business.
Dedicated GPU infrastructure with the on-demand experience your engineering team already knows. Built for startups that want to reduce cloud spend without giving up the cloud-style UX their engineers are productive in.
Fully integrated GPU clusters built for the fastest path from cloud GPUs to dedicated infrastructure you control. Designed for startups that need production capacity in weeks, not quarters.
Individual GPU servers configured to your workload. The right option when you need specific hardware for a specific job, from a single training node to a multi-server inference fleet.