---
title: "AI Endpoints - Catalog API"
description: "Learn how to use the Catalog API to browse the OVHcloud AI Endpoints catalog and inspect the metadata and supported features of each model"
url: https://docs.ovhcloud.com/pt/guides/public-cloud/ai-machine-learning/ai-endpoints-catalog-api
lang: pt
lastUpdated: 2026-07-31
---
# AI Endpoints - Catalog API

:::info
AI Endpoints is covered by the **[OVHcloud AI Endpoints Conditions](https://storage.gra.cloud.ovh.net/v1/AUTH_325716a587c64897acbef9a4a4726e38/contracts/48743bf-AI_Endpoints-ALL-1.1.pdf)** and the **[OVHcloud Public Cloud Special Conditions](https://storage.gra.cloud.ovh.net/v1/AUTH_325716a587c64897acbef9a4a4726e38/contracts/d2a208c-Conditions_particulieres_OVH_Stack-WE-9.0.pdf)**.
:::

## Introduction

`https://oai.endpoints.kepler.ai.cloud.ovh.net/v1` is the base endpoint used to call AI Endpoints models (see the [AI Endpoints - Getting started](/pt/guides/public-cloud/ai-machine-learning/ai-endpoints-getting-started.md) and [AI Endpoints - Responses API](/pt/guides/public-cloud/ai-machine-learning/ai-endpoints-responses-api.md) guides). This article does not cover that endpoint; instead, it covers the separate **Catalog API**, a discovery service for browsing available models and their metadata.

The catalog URL `https://catalog.endpoints.ai.ovh.net/rest/v1/models_v2` provides information and metadata about AI Endpoints models.
This API allows you to list models, supported features, pricing, and metadata specific to each model.

:::warning
The Catalog API is a discovery endpoint, not a text-generation endpoint. It returns metadata for models and capabilities rather than generated content.

This API may also change, including through breaking changes.
:::

## Objective

This documentation shows how to query the Catalog API, read model metadata, and filter the response client-side, with examples in **Python**, **JavaScript**, and **cURL**.

## Requirements

The examples in this guide can be used with any of the following environments:


**Python**

A [Python](https://www.python.org/) environment with the [requests](https://pypi.org/project/requests/) library.
```bash
pip install requests
```


**JavaScript**

A [Node.js](https://nodejs.org/en) environment with native `fetch` support or a compatible HTTP client.
```bash
npm install node-fetch
```


**cURL**

A standard terminal, with [cURL](https://curl.se/) installed on the system.


## Authentication

Unlike the `v1/responses` and `v1/chat/completions` routes, the catalog endpoint is public and does **not** require an API key. You can query it directly, without an `Authorization` header.

If you also plan to call the models it lists, follow the [AI Endpoints - Getting started](/pt/guides/public-cloud/ai-machine-learning/ai-endpoints-getting-started.md) guide to obtain an API key for those calls.

## Listing all models

A single `GET` returns the whole catalog as a JSON array. The endpoint takes no query parameters.


**Python**

```python
import requests

url = "https://catalog.endpoints.ai.ovh.net/rest/v1/models_v2"

response = requests.get(url)
response.raise_for_status()

catalog = response.json()
print(catalog)
```


**JavaScript**

```javascript
import fetch from "node-fetch";

const url = "https://catalog.endpoints.ai.ovh.net/rest/v1/models_v2";

const response = await fetch(url);

const catalog = await response.json();
console.log(catalog);
```


**cURL**

```bash
curl "https://catalog.endpoints.ai.ovh.net/rest/v1/models_v2"
```


## Model metadata

Each entry in the array exposes the following fields:

- `id`, `name`, `description`
- `available`: whether the model can currently be called. Some entries are listed with `available: false`, so check this field before routing traffic to a model.
- `category`: the model family, one of `Large Language Models (LLM)`, `Visual LLM`, `Reasoning LLM`, `Code LLM`, `Embeddings`, `Image Generation`, `LLM Guard`, `Speech To Text`, or `Text To Speech`
- `tags`: short labels such as `Reasoning`, `Multimodal`, `Code Assistant`, or `Moderation`
- `metadata.model_specs.capabilities`: `input_modality` and `output_modality`, arrays such as `["text", "image"]`, plus the `reasoning`, `function_calling`, and `streaming` booleans
- `metadata.publishing_information`: `publisher`, `series`, `licence_name`, `model_publication_date`
- `metadata.usage_information.pricing`: price and unit for prompt and completion usage
- `metadata.usage_information.rate_limit`: request-rate limits, split into `rate_limit_api` (with an API key), `rate_limit_api_anonymous` (without one), and `rate_limit_playground`

Use the capability fields to pick a model before calling the `v1/responses` route: an `input_modality` containing `image` for vision use cases, `function_calling` for tool calling, and `reasoning` for reasoning modes.

## Filtering the catalog

Every call returns the full catalog, so filtering, searching, and sorting happen on the returned array. The examples below keep only vision-capable models, then look up a single entry by `id`.


**Python**

```python
import requests

url = "https://catalog.endpoints.ai.ovh.net/rest/v1/models_v2"

response = requests.get(url)
response.raise_for_status()
catalog = response.json()

vision_models = [
    model for model in catalog
    if "image" in model["metadata"]["model_specs"]["capabilities"]["input_modality"]
]
print(vision_models)

model = next((m for m in catalog if m["id"] == "gpt-oss-20b"), None)
print(model)
```


**JavaScript**

```javascript
import fetch from "node-fetch";

const url = "https://catalog.endpoints.ai.ovh.net/rest/v1/models_v2";

const response = await fetch(url);
const catalog = await response.json();

const visionModels = catalog.filter((model) =>
  model.metadata.model_specs.capabilities.input_modality.includes("image")
);
console.log(visionModels);

const model = catalog.find((m) => m.id === "gpt-oss-20b");
console.log(model);
```


**cURL**

```bash
curl -s "https://catalog.endpoints.ai.ovh.net/rest/v1/models_v2" | \
  jq '[.[] | select(.metadata.model_specs.capabilities.input_modality | index("image"))]'

curl -s "https://catalog.endpoints.ai.ovh.net/rest/v1/models_v2" | \
  jq '.[] | select(.id == "gpt-oss-20b")'
```


## Conclusion

Use the Catalog API to discover the available models and check their capabilities, features, pricing, and rate limits before calling the runtime API.

## Go further

Using the Catalog API is required in [Deploy Langfuse on OVHcloud MKS for LLM observability and AI cost tracking](https://blog.ovhcloud.com/en/posts/deploy-langfuse-ovhcloud-mks-llm-observability/). That article pulls the `metadata.usage_information.pricing` block from the catalog to register each model's price with **Langfuse**, which then computes the cost of your AI Endpoints calls automatically.

For training or technical assistance implementing our solutions, contact your sales representative or visit our [Professional Services](https://www.ovhcloud.com/pt/professional-services/) page to request a quote and have your project analyzed by our experts.

## Feedback

Please send us your questions, feedback, and suggestions to improve the service:

- On the OVHcloud [Discord server](https://discord.gg/ovhcloud).