OpenAI API compatibility
You can use any of the OpenAI official libraries, for example, the OpenAI Python client library to interact with your Scaleway Managed Inference deployment. This feature is especially beneficial for those looking to seamlessly transition applications already utilizing OpenAI.
Chat Completions API or Responses API?
Both the Chat Completions API and the Responses API are OpenAI-compatible REST APIs that can be used for generating and manipulating conversations. The Chat Completions API is focused on generating conversational responses, while the Responses API is a more general REST API for chat, structured outputs, tool use, and multimodal inputs.
The Chat Completions API was released in 2023, and is an industry standard for building AI applications, being specifically designed for handling multi-turn conversations. It is stateless, but allows users to manage conversation history by appending each new message to the ongoing conversation. Messages in the conversation can include text, images and audio extracts. The API supports function
tool-calling, allowing developers to define functions that the model can choose to call. If it does so, it returns the function name and arguments, which the developer's code must execute and feed back into the conversation.
The Responses API was released in 2025, and is designed to combine the simplicity of Chat Completions with the ability to do more agentic tasks and reasoning. It supports statefulness, being able to maintain context without needing to resend the entire conversation history. It offers tool-calling by built-in tools (e.g. web or file search) that the model is able to execute itself while generating a response.
Most supported Generative API models can be used with both Chat Completions and Responses API. For the **gtp-oss-120b
model, use of the Responses API is recommended, as it will allow you to access all of its features, especially tool-calling.
For full details on the differences between these APIs, see the official OpenAI documentation.
CURL
To invoke Scaleway Managed Inference's 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)
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, please contact us via Slack.
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 stringsmodel
(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 Scaleway Managed Inference 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 Managed Inference 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
If you have a use case requiring one of these unsupported APIs, please contact us via Slack.