hyperbolic.ai

{"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://www.hyperbolic.ai/#organization","name":"Hyperbolic","url":"https://www.hyperbolic.ai","logo":"https://www.hyperbolic.ai/open-graph.png","description":"Hyperbolic is the Open-Access AI Cloud, providing on-demand GPU compute and an OpenAI-compatible inference API for AI teams."},{"@type":"WebSite","@id":"https://www.hyperbolic.ai/#website","name":"Hyperbolic","url":"https://www.hyperbolic.ai","publisher":{"@id":"https://www.hyperbolic.ai/#organization"}},{"@type":"FAQPage","@id":"https://www.hyperbolic.ai/#faq","mainEntity":[{"@type":"Question","name":"What is Hyperbolic?","acceptedAnswer":{"@type":"Answer","text":"Hyperbolic is the Open-Access AI Cloud for teams that need fast, flexible access to GPU compute. The platform gives startups, researchers, AI labs, and enterprises one place to launch on-demand GPUs, reserve dedicated capacity, or deploy private GPU infrastructure for training, fine-tuning, inference, and production AI workloads. Instead of waiting on legacy cloud quotas, long procurement cycles, or rigid contracts, teams can start with self-serve GPU instances and scale into reserved or private infrastructure as their workload requirements grow."}},{"@type":"Question","name":"How quickly can I start?","acceptedAnswer":{"@type":"Answer","text":"You can launch an on-demand GPU instance in minutes. Create an account, add a payment method, choose your GPU configuration, and connect over SSH. Hyperbolic supports workloads ranging from single-GPU experimentation to larger multi-GPU and multi-node deployments. For teams that need guaranteed access to specific GPUs such as H100s, H200s, or B200s, Reserved and Private Cloud options are available."}},{"@type":"Question","name":"Do I need a long-term contract?","acceptedAnswer":{"@type":"Answer","text":"No. On-demand GPU instances are pay-as-you-go, so you only pay for the compute you run. There are no long-term commitments required to get started. For teams running persistent training, fine-tuning, inference, or production workloads, Reserved capacity provides dedicated GPU access at discounted rates. This gives you more predictable availability and pricing without forcing you into the multi-year commitments often required by legacy cloud providers."}},{"@type":"Question","name":"How does GPU pricing work?","acceptedAnswer":{"@type":"Answer","text":"Hyperbolic pricing depends on the compute model you choose. On-demand GPUs are usage-based and billed by the hour, making them a good fit for experimentation, burst workloads, short training runs, evaluation jobs, and flexible inference needs. Reserved GPUs are priced at discounted prepaid rates for teams that need dedicated, always-on capacity. This is usually the better fit when your workload is predictable, production-critical, or capacity-sensitive. Private Cloud pricing is customized based on GPU type, cluster size, networking, isolation, compliance, and operational requirements."}},{"@type":"Question","name":"What is the difference between On-Demand, Reserved, and Private Cloud?","acceptedAnswer":{"@type":"Answer","text":"Hyperbolic offers three ways to access GPU capacity. On-Demand is best when you need GPUs right away with no commitment. It is the most flexible option and is billed based on usage. Reserved is best for steady production workloads that need dedicated capacity at a discounted rate. It requires a shorter-term commitment, usually under one year. Private Cloud is best for teams scaling long term. It provides dedicated infrastructure, more control, and the lowest pricing through longer-term agreements."}},{"@type":"Question","name":"Is my data and workload isolated?","acceptedAnswer":{"@type":"Answer","text":"Yes. Hyperbolic is built for secure GPU workloads, with isolation controls that vary by infrastructure tier. On-Demand GPU instances run on secure multi-tenant infrastructure designed for fast, flexible access. Workloads are isolated from other users, with network isolation and instance cleanup between sessions. Reserved capacity gives your team dedicated GPU resources for more predictable performance and stronger workload control. Depending on the configuration, this can include bare-metal access, direct SSH, and dedicated infrastructure for your workloads. Private Cloud provides the strongest isolation model for teams with stricter security, compliance, or enterprise requirements. It can include single-tenant infrastructure, isolated networking, SSO, audit logging, and compliance support. For sensitive workloads involving proprietary models, regulated data, or strict internal security requirements, Reserved or Private Cloud is usually the better fit."}}]}]}