> ## Documentation Index
> Fetch the complete documentation index at: https://docs.hyperbolic.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Welcome to Hyperbolic

Hyperbolic is an AI cloud platform for training, fine-tuning, and serving AI models at scale. Teams use Hyperbolic to access high-performance GPU infrastructure across On-Demand, Reserved, and Private Cloud, helping them move from experimentation to production without long procurement cycles or rigid upfront commitments.

## Choose Your Solution

Start with the product that matches your workload. Use On-Demand GPUs for flexible compute, Reserved for predictable capacity at a lower rate, Private Cloud for dedicated infrastructure, or Inference API for serving models through an OpenAI-compatible endpoint.

### On-Demand GPUs

**Best for:** Experimentation, model training, fine-tuning, custom deployments, and flexible GPU access.

Launch high-performance GPU instances when you need capacity without committing before your workload is proven. On-Demand GPUs give teams flexible access to GPU infrastructure for testing, training, fine-tuning, benchmarking, and early production workloads.

[Learn more about On-Demand GPUs →](/docs/on-demand/overview)

### Reserved

**Best for:** Sustained GPU demand, production workloads, predictable usage, and teams that want a lower rate than on-demand.

Reserved gives you the same GPU capacity you would use on-demand, reserved for a fixed term at a discounted rate. It runs on the same platform and the same hardware — you simply commit to the capacity up front for a lower price. Reserved is self-serve in the app, and larger commitments can also be arranged with our team.

[Learn more about Reserved →](/docs/reserved/overview)

### Private Cloud

**Best for:** Enterprise AI teams, sensitive workloads, dedicated environments, and teams that need stronger isolation and operational control.

Private Cloud gives organizations dedicated GPU infrastructure for production-scale AI workloads. It is designed for teams that need predictable capacity, stronger isolation, custom infrastructure requirements, and a long-term partner for AI compute planning.

[Learn more about Private Cloud →](/docs/private-cloud/overview)

## Quick Comparison

| Solution           | Best For                                                          | Access Model                           | Commitment                | Infrastructure Control                 |
| ------------------ | ----------------------------------------------------------------- | -------------------------------------- | ------------------------- | -------------------------------------- |
| **On-Demand GPUs** | Experiments, training, fine-tuning, flexible compute              | Self-serve GPU instances               | None                      | Full instance-level control            |
| **Reserved**       | Sustained, predictable workloads                                  | Reserve on-demand capacity, self-serve | Fixed term, paid up front | Same platform and control as on-demand |
| **Private Cloud**  | Enterprise workloads, sensitive use cases, dedicated environments | Private GPU infrastructure             | Custom                    | Highest level of control and isolation |

<Note>
  **GPU availability note**

  Hyperbolic provides access to high-performance GPU capacity, including H100, H200, and B200 infrastructure. Availability may vary by product, supply, region, and configuration. Check the [Hyperbolic console](https://app.hyperbolic.ai/gpus) for current On-Demand and Reserved GPU options, or [contact our team](mailto:sales@hyperbolic.ai) for Private Cloud requirements.
</Note>

## Who Uses Hyperbolic?

### AI-Natives

For teams building AI products where compute access directly affects product velocity. Start on demand, test quickly, and move into reserved infrastructure as usage grows.

### Research Teams and AI Labs

For teams running experiments, benchmarking models, fine-tuning workloads, and scaling research infrastructure without waiting through long procurement cycles.

### Infrastructure and Engineering Leaders

For teams responsible for sourcing, validating, and scaling GPU infrastructure across training, fine-tuning, and production workloads.

### Enterprise AI Teams

For organizations that need predictable capacity, dedicated environments, stronger isolation, and a path from pilot projects to production AI infrastructure.

## Get Started

<CardGroup cols={2}>
  <Card title="Get Started Guide" icon="rocket" href="/docs/overview/quickstart">
    Set up your account and launch your first workload in minutes
  </Card>
</CardGroup>

***

> **Need Help Choosing?**
>
> Not sure which service is right for you? Check our [detailed comparison guide](/docs/overview/platform-comparison) or [contact our team](mailto:support@hyperbolic.ai) for personalized recommendations.