> For the complete documentation index, see [llms.txt](https://docs.cloud.olakrutrim.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.cloud.olakrutrim.com/basics/ai-studio/ai-jobs/deployment.md).

# Deployment

## Deployment

Once a model is finalized—whether from the Catalog or after fine-tuning—it can be deployed as an always-available endpoint for inference.

AI Studio supports two deployment modes:

* **On-Demand**: Instant, serverless access. Best for quick experiments and lightweight use cases.
* **Dedicated**: Persistent deployment on dedicated infrastructure. Recommended for production-grade workloads.

***

### 1. Why Deploy?

Deploying a model creates a stable, callable API that can be integrated into downstream systems and user-facing products. It ensures:

* Predictable performance
* Repeatable results
* Centralized monitoring
* Easy access via standard APIs

For high-availability, real-time applications, deploying the model is essential.

***

### 2. On-Demand vs Dedicated Deployments

| Mode          | Description                                              | Use Case                                 |
| ------------- | -------------------------------------------------------- | ---------------------------------------- |
| **On-Demand** | Serverless, ephemeral deployment managed by the platform | Ad-hoc testing, internal tools           |
| **Dedicated** | Persistent instance with reserved GPU                    | Production systems, high-throughput APIs |

***

### 3. Why Use Dedicated Deployment?

Dedicated deployments offer significant benefits over serverless access:

* **Guaranteed Throughput**: Your model runs on a dedicated GPU (e.g., NVIDIA H100), delivering consistent latency and handling concurrent requests reliably.
* **Data Security**: Inference runs in an isolated environment, reducing risk of data leakage. Suitable for enterprise, healthcare, and financial use cases.
* **Stable Endpoint**: Model versioning is locked, making it easy to debug, monitor, and iterate. Ideal for applications with audit or compliance needs.
* **Fine-tuned Model Hosting**: Use dedicated deployments to host your own custom fine-tuned models with controlled rollout.

***

### 4. Creating a Deployment

To deploy a model from Model Catalog:

1. Navigate to the deployment tab
2. Click New Deployment
3. Select desired model from drop down
4. Provide unique deplyment name
5. Click on deploy and the system provisions a dedicated instance and exposes an OpenAI-compatible endpoint

To deploy a fine-tuned model:

1. Navigate to the completed fine-tuning job
2. Click **Deploy**
3. Provide a unique deployment name
4. Click on deploy and the system provisions a dedicated instance and exposes an OpenAI-compatible endpoint

Deployment is complete within minutes and ready for use across your application stack.

***

### 5. Managing Deployments

The **Deployments** tab provides:

* **Status Monitoring**: Running, stopped, or failed state
* **Usage Tracking**: Requests, token throughput, and basic logs
* **Deployment Controls**: Start, stop, or redeploy models
* **Checkpoint Selection**: Redeploy older fine-tuning checkpoints if needed

Stopping a deployment releases its GPU and suspends the associated endpoint.

***

### 6. Next Steps

* Run Inference using the deployed model
* Evaluate latency and runtime performance post-deployment
* Fine-Tune to improve domain alignment before deployment
* Refer to the API Reference to integrate your deployment endpoint