# Run a Model on an Image

Roboflow exposes inference through several runtimes — the right choice depends on whether you're calling a single model or a Workflow, how much throughput you need, and where the workload runs.

This page is a brief overview. The detailed inference reference lives in the [product documentation](https://docs.roboflow.com/deploy/deployment-overview), which is part of the same docs site. Cross-links are provided where the deeper material lives.

## Inference runtimes

| Runtime                                       | Use when                                                                                             | Reference                                                                                                                                            |
| --------------------------------------------- | ---------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Serverless v2** (`serverless.roboflow.com`) | Default. Hosted, auto-scaling, supports models and Workflows.                                        | [Serverless overview](https://docs.roboflow.com/deploy/serverless)                                                                                   |
| **Dedicated Deployments**                     | You need predictable latency, high throughput, or pinned GPU type. Managed by Roboflow.              | [Dedicated Deployments](/developer/rest-api/dedicated-deployments.md) and [product overview](https://docs.roboflow.com/deploy/dedicated-deployments) |
| **Roboflow Inference** (self-hosted)          | On-prem, edge devices, air-gapped environments, or workloads that can't leave your VPC. Open source. | [inference.roboflow.com](https://inference.roboflow.com)                                                                                             |

## Calling Serverless v2

Run a model:

```bash
curl -F "file=@photo.jpg" \
  "https://serverless.roboflow.com/infer/<workspace>/<project>/<version>?api_key=$ROBOFLOW_API_KEY&confidence=0.5"
```

Run a Workflow:

```bash
curl -X POST "https://serverless.roboflow.com/infer/workflows/<workspace>/<workflow>" \
  -H "Content-Type: application/json" \
  -d '{
    "api_key": "'$ROBOFLOW_API_KEY'",
    "inputs": { "image": { "type": "url", "value": "https://example.com/photo.jpg" } }
  }'
```

The full request/response reference, including streaming, batching, and per-task response shapes, is in the [product docs deployment section](https://docs.roboflow.com/deploy/deployment-overview).

## Deprecated: Serverless v1

The legacy task-specific endpoints — `detect.roboflow.com`, `classify.roboflow.com`, `outline.roboflow.com`, `segment.roboflow.com` — are **deprecated**. They still respond for backwards compatibility but new code should use `serverless.roboflow.com` instead.

If you find a snippet pointing to a `*.roboflow.com` task host, treat it as legacy and translate it to the Serverless v2 form above.

## SDK and CLI shortcuts

If you're not building a non-Python integration, the SDK and CLI wrap these calls with proper authentication and JSON parsing:

* SDK — see [Run a Model on an Image (SDK)](/developer/python-sdk/run-a-model-on-an-image.md).
* CLI — see [Run a Model on an Image (CLI)](/developer/command-line-interface/run-a-model-on-an-image.md).

Both ultimately hit the same Serverless v2 endpoint described above.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.roboflow.com/developer/rest-api/run-a-model-on-an-image.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.