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

# Billing & scaling

> Understanding billing and scaling mechanisms for on-demand deployments.

## Autoscaling and costs

**Q: How does autoscaling affect my costs?**

* **Scaling from 0**: No minimum cost when scaled to zero
* **Scaling up**: Each new replica adds to your total cost proportionally. For example:
  * Scaling from 1 to 2 replicas doubles your GPU costs
  * If each replica uses multiple GPUs, costs scale accordingly (e.g., scaling from 1 to 2 replicas with 2 GPUs each means paying for 4 GPUs total)

For current pricing details, please visit our [pricing page](https://fireworks.ai/pricing).

***

## Rate-limits for on-demand deployment

**Q: What are the rate limits for on-demand deployments?**

Request throughput scales with your GPU allocation. Base allocations include:

* Up to 8 A100 GPUs
* Up to 8 H100 GPUs

On-demand deployments offer several advantages:

* **Predictable pricing** based on time units, not token I/O
* **Protected latency and performance**, independent of traffic on the serverless platform
* **Choice of GPUs**, including A100s and H100s

Need more GPUs? Contact us to discuss higher allocations for your specific use case.

***

## On-demand billing

**Q: How does billing work for on-demand deployments?**

On-demand deployments come with automatic cost optimization features:

* **Default autoscaling**: Automatically scales to 0 replicas when not in use
* **Pay for what you use**: Charged only for GPU time when replicas are active
* **Flexible configuration**: Customize autoscaling behavior to match your needs

**Best practices for cost management**:

1. **Leverage default autoscaling**: The system automatically scales down deployments when not in use
2. **Customize carefully**: While you can modify autoscaling behavior using our [configuration options](https://docs.fireworks.ai/guides/ondemand-deployments#customizing-autoscaling-behavior), note that preventing scale-to-zero will result in continuous GPU charges
3. **Consider your use case**: For intermittent or low-frequency usage, serverless deployments might be more cost-effective

For detailed configuration options, see our [deployment guide](https://docs.fireworks.ai/guides/ondemand-deployments#replica-count-horizontal-scaling).

***

## Scaling structure

**Q: How does billing and scaling work for on-demand GPU deployments?**

On-demand GPU deployments have unique billing and scaling characteristics compared to serverless deployments:

**Billing**:

* Charges start when the server begins accepting requests
* **Billed by GPU-second** for each active instance
* Costs accumulate even if there are no active API calls

**Scaling options**:

* Supports **autoscaling** from 0 to multiple GPUs
* Each additional GPU **adds to the billing rate**
* Can handle unlimited requests within the GPU’s capacity

**Management requirements**:

* Not fully serverless; requires some manual management
* **Manually delete deployments** when no longer needed
* Or configure autoscaling to **scale down to 0** during inactive periods

**Cost control tips**:

* Regularly **monitor active deployments**
* **Delete unused deployments** to avoid unnecessary costs
* Consider **serverless options** for intermittent usage
* Use **autoscaling to 0** to optimize costs during low-demand times

***

## Additional resources

* **Discord Community**: [discord.gg/fireworks-ai](https://discord.gg/fireworks-ai)
* **Email Support**: [inquiries@fireworks.ai](mailto:inquiries@fireworks.ai)
* Contact our [sales team](https://fireworks.ai/company/contact-us?tab=business) for **custom pricing options**