---
title: OpenAI API compatibility
description: Scaleway Generative APIs provide compatibility with parts of the OpenAI API, to help connect existing applications.
tags: ai-data chat embeddings
dates:
  validation: 2026-04-24
  posted: 2024-05-06
---

import ChatCompVsResponsesApi from '@macros/ai/chat-comp-vs-responses-api.mdx'

You can use any of the OpenAI [official libraries](https://platform.openai.com/docs/libraries/), for example, the [OpenAI Python client library](https://github.com/openai/openai-python) to interact with Generative APIs.
This feature is especially beneficial for those looking to seamlessly transition applications already utilizing OpenAI.

## Chat Completions API or Responses API?

<ChatCompVsResponsesApi />

### cURL

To invoke the Generative APIs - Dedicated Deployment OpenAI-compatible Chat API, simply edit your dedicated endpoints with a suffix `/v1/chat/completions`:
```
https://<Deployment UUID>.ifr.fr-par.scaleway.com/v1/chat/completions
```

### OpenAI Python client library

Use OpenAI's SDK how you normally would.

```
from openai import OpenAI

client = OpenAI(
    base_url='https://<Deployment UUID>.ifr.fr-par.scaleway.com/v1/',
    api_key='<IAM API key>'
)

chat_completion = client.chat.completions.create(
    messages=[
        {   "role": "system",
            "content": "You are a helpful assistant."
        },
        {
            "role": "user",
            "content": "Sing me a song about Scaleway"
        }
    ],
    model='<Model name>' #e.g 'meta/llama-3.1-8b-instruct:fp8'
)

print(chat_completion.choices[0].message.content)
```

<Message type="note">
  More OpenAI-like APIs (e.g., audio) will be released step-by-step once related models are supported.
</Message>

### Supported parameters

- `messages` (required)
- `model` (required)
- `max_tokens`
- `temperature` (default 0.7)
- `top_p` (default 1)
- `presence_penalty`
- `response_format`
- `logprobs`
- `stop`
- `seed`
- `stream`
- `tools`
- `tool_choice`

### Unsupported parameters

Currently, the following options are not supported:

- `frequency_penalty`
- `n`
- `top_logprobs`
- `logit_bias`
- `user`

If you have a use case requiring one of these unsupported features, [contact us via Slack](https://slack.scaleway.com/).

## Embeddings API

The Embeddings API is designed to get a vector representation of an input that can be easily consumed by other machine learning models.

### cURL

Use your dedicated endpoints as follows:
```
https://<Deployment UUID>.ifr.fr-par.scaleway.com/v1/embeddings
```

```
curl https://<Deployment UUID>.ifr.fr-par.scaleway.com/v1/embeddings \
  -H "Authorization: Bearer $SCW_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "Embeddings can represent text in a numerical format.",
    "model": "$MODEL_NAME"
  }'
  # model e.g 'sentence-transformers/sentence-t5-xxl:fp32'
```

### OpenAI Python client library

```
from openai import OpenAI

client = OpenAI(
    base_url='https://<Deployment UUID>.ifr.fr-par.scaleway.com/v1/',
    api_key='<IAM API key>'
)

embedding = client.embeddings.create(
    input=["Embeddings can represent text in a numerical format.","Machine learning models use embeddings for various tasks."]
    model='<Model name>' #e.g 'sentence-transformers/sentence-t5-xxl:fp32'
)

print(embedding)
```

### Supported parameters

- `input` (required) in **string** or **array of strings**
- `model` (required)

### Unsupported parameters

- encoding_format (default float)
- dimensions

## Models API

The Models API returns the model(s) available for inferencing.

In the context of a dedicated Generative API deployment, it returns the name of the current model being served.

```
https://<Deployment UUID>.ifr.fr-par.scaleway.com/v1/models
```

```
curl https://<Deployment UUID>.ifr.fr-par.scaleway.com/v1/models \
  -H "Authorization: Bearer $SCW_API_KEY" \
  -H "Content-Type: application/json"
```

## Differences

### Token usage stats

OpenAI API doesn't return usage stats (number of tokens in prompt and completion) for streaming responses.

Scaleway Generative APIs - Dedicated Deployment endpoints return usage stats for both streaming and non-streaming responses.

For streaming responses, the usage field is incremented in each chunk, and completed in the very last chunk of the response. For example:

```
data: {...,"choices":[{"index":0,"delta":{"content":" Hello","role":"assistant","name":""},"finish_reason":null}],...,"usage":{"prompt_tokens":9,"completion_tokens":1,"total_tokens":10}}

data: {...,"choices":[{"index":0,"delta":{"content":"!","role":"assistant","name":""},"finish_reason":null}],...,"usage":{"prompt_tokens":9,"completion_tokens":2,"total_tokens":11}}

data: {...,"choices":[{"index":0,"delta":{"content":"","role":"assistant","name":""},"finish_reason":"stop"}],...,"usage":{"prompt_tokens":9,"completion_tokens":2,"total_tokens":11}}

data: [DONE]
```

## Future developments

This documentation covers the initial phase of experimental support for the OpenAI API.
Gradually, we plan to introduce additional APIs such as:

- Audio API
- Images API

<Message type="note">
  We will progressively roll out more OpenAI-like APIs as we expand model support.
</Message>

If you have a use case requiring one of these unsupported APIs, [contact us via Slack](https://slack.scaleway.com/).