
Hyperbolic
5.3/10Hyperbolic is a GPU cloud marketplace that rents on-demand and reserved NVIDIA H100, H200 and B200 GPUs and offers an OpenAI-compatible inference API, aimed at AI developers.
Data last updated November 2026 · Our methodology
Overview
Hyperbolic is a GPU cloud that rents capacity from a network of about 30 smaller GPU providers, often called neoclouds, through one console and API. It sells hourly on-demand instances of NVIDIA H100 SXM, H200 and B200 GPUs, with B300 listed in the docs. You reach these instances over SSH and can start one in minutes. Reserved capacity is prepaid at a discount on terms under a year. Private Cloud is single-tenant, for commitments longer than a year. The company also runs an OpenAI-compatible inference API for open-source text and vision models, billed per token from a credit balance. Managed Kubernetes (in beta) runs the control plane and installs the NVIDIA GPU and network operators. An open-source Rental Operator lets teams create rentals from Kubernetes resources. The service suits AI startups and developers who need GPUs for training, fine-tuning or inference without a long contract. Hyperbolic says more than 250,000 developers use it, including teams at Modal and Daytona. Prices start around 3.19 dollars per GPU-hour for an H100 SXM, 3.99 for an H200 and 5.99 for a B200. They are refreshed weekly based on supplier rates. Payment is by card through Stripe, and you are not charged for failed instances. The main drawback is that prices change often. Independent trackers saw the H100 rate more than double, from 1.50 to 3.19 dollars, within about ten weeks in 2026. That rate ranks in the more expensive half of the providers they track. There is no general-purpose web hosting. The docs do not mention object storage, and they do not list regions.
Our Rating
Hosting & Infrastructure5.1/10
AI Software5.4/10
Who Hyperbolic is best for
Good fit
It is built around SSH GPU instances, an OpenAI-compatible API and Kubernetes tooling for ML engineers.
Hourly billing with no minimum term suits AI startups that need GPUs on short notice, and the company names startup customers.
Less suited
Private Cloud offers single-tenant clusters, SSO and audit logging, but there is little public evidence on compliance.
Pros & Cons
Pros
- On-demand H100, H200 and B200 GPUs come with no minimum term, and failed instances are not billed.
- A single console and API pull capacity from about 30 GPU suppliers.
- The inference API is OpenAI-compatible, so it works with existing OpenAI client code.
- Managed Kubernetes and an open-source Rental Operator let teams provision GPUs as Kubernetes resources.
Cons
- Prices are volatile. The H100 rate rose from 1.50 to 3.19 USD per hour within about ten weeks in 2026.
- At 3.19 USD, the H100 rate ranked 42 of 63 providers on one tracker, well above the cheapest options.
- Managed Kubernetes and the Rental Operator are still in beta.
- Public reviews are very thin. The only user rating found was a single 1 out of 5 on SourceForge.
Key Features
Hyperbolic Pricing
On-Demand H100 SXM
- SSH access
- No minimum term
- Billed hourly on usage
- From 3.19 USD per GPU-hour
Private Cloud
- Custom quote
- SSO and audit logging
- Commitments over one year
- Single-tenant, isolated networking
Reserved GPUs
- Discounted prepaid rates
- Commitments under one year
- Dedicated resources, bare metal possible
Frequently Asked Questions
No. On-demand instances have no minimum term and are billed hourly on usage. Reserved capacity is prepaid on terms under a year.
Hyperbolic says it does not charge for failed instances and notifies you within 3 minutes if one fails.
Per token, deducted from a prepaid credit balance. The API is compatible with OpenAI's chat completions format.
The marketplace lists H100 SXM, H200 and B200, and the docs also mention B300. Availability varies by supply and region.
By credit card through Stripe.
Yes. Private Cloud provides single-tenant infrastructure with isolated networking, SSO and audit logging. On-demand instances run multi-tenant with network isolation.
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