Scaleway
LiteLLM supports all models available on Scaleway Generative APIs ↗.
Usage with LiteLLM Python SDK
import os
from litellm import completion
os.environ["SCW_SECRET_KEY"] = "your-scaleway-secret-key"
messages = [{"role": "user", "content": "Write a short poem"}]
response = completion(model="scaleway/qwen3-235b-a22b-instruct-2507", messages=messages)
print(response)
Usage with LiteLLM Proxy
1. Set Scaleway models in config.yaml
model_list:
- model_name: scaleway-model
litellm_params:
model: scaleway/qwen3-235b-a22b-instruct-2507
api_key: "os.environ/SCW_SECRET_KEY" # ensure you have `SCW_SECRET_KEY` in your .env
2. Start proxy
litellm --config config.yaml
3. Query proxy
Assuming the proxy is running on http://localhost:4000:
curl http://localhost:4000/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_LITELLM_MASTER_KEY" \
-d '{
"model": "scaleway-model",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "Write a short poem"
}
]
}'
-H "Authorization: Bearer YOUR_LITELLM_MASTER_KEY" is only required if you have set a LiteLLM master key
Supported features
Scaleway provider supports all features in Generative APIs reference documentation ↗, such as streaming, structured outputs and tool calling.
Audio transcription
Scaleway's /audio/transcriptions endpoint is OpenAI-compatible and works with Whisper models.
Python SDK
import os
from litellm import transcription
os.environ["SCW_SECRET_KEY"] = "your-scaleway-secret-key"
with open("speech.mp3", "rb") as audio_file:
response = transcription(
model="scaleway/whisper-large-v3",
file=audio_file,
)
print(response.text)
Proxy config
model_list:
- model_name: scaleway-whisper
litellm_params:
model: scaleway/whisper-large-v3
api_key: "os.environ/SCW_SECRET_KEY"
Proxy request
curl http://localhost:4000/v1/audio/transcriptions \
-H "Authorization: Bearer YOUR_LITELLM_MASTER_KEY" \
-F model="scaleway-whisper" \
-F file="@speech.mp3"
Supported optional params: language, prompt, response_format, temperature, timestamp_granularities.