# Billing Models

Krutrim Cloud offers two flexible billing models to support a wide range of user needs—from rapid, on-demand provisioning to long-term enterprise-scale deployments. These models are designed to provide transparency, cost-efficiency, and flexibility for both individual developers and large organizations.

***

### 1. Pay-as-You-Go (On-Demand Billing)

The **Pay-as-You-Go** model allows you to provision and use cloud services instantly, with billing based on actual usage time.

#### Key Highlights:

* **No upfront commitment**
* **Instant provisioning** of compute, storage, and other services
* **Per-unit pricing** clearly visible on the Krutrim Cloud Console
* **Billing is time-based**, typically metered by the hour or per GB (depending on service)

You are charged only for the duration your resources are active. Once you stop or delete the resource, billing stops automatically.

{% hint style="info" %}
This model is ideal for developers, startups, or teams running short-term, burstable workloads.
{% endhint %}

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### 2. Enterprise Usage (Committed Billing)

The **Enterprise Usage** model is tailored for organizations with predictable or large-scale requirements. It allows you to commit to a defined volume of usage over time in exchange for custom pricing and additional benefits.

#### Key Highlights:

* **Custom bundles and rates** based on your usage profile
* **Dedicated support** and technical onboarding
* **Works well for production workloads, research teams, and AI labs**

To explore enterprise pricing, reach out to our team at ***<sales@olakrutrim.com>***

We will work with you to understand your workloads and provide a tailored quote that maximizes performance and cost-efficiency.

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### Which Model Should You Choose?

| Use Case                                    | Recommended Model |
| ------------------------------------------- | ----------------- |
| Experimenting with VMs or AI models         | Pay-as-You-Go     |
| Unpredictable or bursty workloads           | Pay-as-You-Go     |
| Production deployment of AI pipelines       | Enterprise Usage  |
| Dedicated GPU clusters or multi-team access | Enterprise Usage  |


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