> ## 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 built for teams training, fine-tuning, and serving AI models at scale. The platform gives startups, research labs, and enterprise AI teams fast access to high-performance GPU capacity across On-Demand, Reserved, and Private Cloud infrastructure, helping them move from experimentation to production without long procurement cycles or rigid upfront commitments.

## Choose Your Solution

Hyperbolic gives AI teams fast access to GPU infrastructure and inference services for training, fine-tuning, serving, and scaling AI workloads. Start with on-demand GPU instances or inference APIs, then move into reserved capacity or private cloud infrastructure when your workloads require predictable access, stronger isolation, or dedicated environments.

### 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 →](/on-demand/overview)

### Reserved Capacity

**Best for:** Sustained GPU demand, production workloads, predictable usage, and teams scaling beyond ad hoc compute.

Reserved Capacity gives teams access to dedicated GPU infrastructure when usage becomes predictable. Teams can move from flexible on-demand usage into reserved environments without rebuilding their compute strategy or managing multiple providers. It is designed for teams that need custom cluster configurations, predictable GPU access for production workloads, pricing aligned to sustained usage, and direct support for infrastructure planning and capacity scaling.

[Learn more about Reserved Capacity →](/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 →](https://calendly.com/d/cq79-jyv-jg4/hyperbolic-sales-demo)

### Serverless Inference

**Best for:** API-based inference, model serving, prototypes, production APIs, and teams building AI applications.

Hyperbolic Inference helps teams serve AI models through an API without managing GPU infrastructure directly. Use it when you need a faster path to model access, application development, or production inference workflows.

[Learn more about Inference →](/inference/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 Capacity** | Sustained workloads and predictable production demand             | Reserved GPU capacity      | Contract-based, usually under a year | Dedicated capacity with support        |
| **Private Cloud**     | Enterprise workloads, sensitive use cases, dedicated environments | Private GPU infrastructure | Custom                               | Highest level of control and isolation |
| **Inference**         | API-based model serving and application development               | API access                 | Usage-based                          | No instance management required        |

<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 GPU options, or [contact our team](mailto:sales@hyperbolic.ai) for Reserved Capacity and 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, inference, 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.

***

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