scaleway · RoRoJ · Aug 21, 2025 · Aug 20, 2025 · Aug 21, 2025
@@ -2,20 +2,52 @@
 title: Generative APIs FAQ
 description: Get answers to the most frequently asked questions about Scaleway Generative APIs.
 dates:
-  validation: 2025-02-12
+  validation: 2025-08-20
 productIcon: GenerativeApiProductIcon
 ---
 
-## What are Scaleway Generative APIs?
+## General 
+
+### What are Scaleway Generative APIs?
 Scaleway's Generative APIs provide access to pre-configured, serverless endpoints of leading AI models, hosted in European data centers. This allows you to integrate advanced AI capabilities into your applications without managing underlying infrastructure.
 
-## Which models are supported by Generative APIs?
+### How do I get started with Generative APIs?
+To get started, explore the [Generative APIs Playground](/generative-apis/quickstart/#start-with-the-generative-apis-playground) in the Scaleway console. For application integration, refer to our [Quickstart guide](/generative-apis/quickstart/), which provides step-by-step instructions on accessing, configuring, and using a Generative APIs endpoint.
+
+### Where are the inference servers located?
+All models are currently hosted in a secure data center located in Paris, France, operated by [OPCORE](https://www.opcore.com/). This ensures low latency for European users and compliance with European data privacy regulations.
+
+### What is the difference between Generative APIs and Managed Inference?
+- **Generative APIs**: A serverless service providing access to pre-configured AI models via API, billed per token usage. 
+- **Managed Inference**: Allows deployment of curated or custom models with chosen quantization and Instances, offering predictable throughput and enhanced security features like private network isolation and access control. Managed Inference is billed by hourly usage, whether provisioned capacity is receiving traffic or not.
+
+## Models and libraries
+
+### Which models are supported by Generative APIs?
 Our Generative APIs support a range of popular models, including:
 - Chat / Text Generation models: Refer to our dedicated [documentation](/generative-apis/reference-content/supported-models/#chat-models) for a list of supported chat models.
 - Vision models: Refer to our dedicated [documentation](/generative-apis/reference-content/supported-models/#vision-models) for a list of supported vision models.
 - Embedding models: Refer to our dedicated [documentation](/generative-apis/reference-content/supported-models/#embedding-models) for a list of supported embedding models.
 
-## How does the free tier work?
+### What is the model lifecycle for Generative APIs?
+Scaleway is dedicated to updating and offering the latest versions of generative AI models, while ensuring older models remain accessible for a significant time, and also ensuring the reliability of your production applications. Learn more in our [model lifecycle policy](/generative-apis/reference-content/model-lifecycle/).
+
+### Do model licenses apply when using Generative APIs?
+Yes, you need to comply with model licenses when using Generative APIs. Applicable licenses are available for [each model in our documentation](/generative-apis/reference-content/supported-models/#vision-models) and in the console Playground.
+
+### Can I increase maximum output (completion) tokens for a model?
+No, you cannot increase maximum output tokens above [limits for each models](/generative-apis/reference-content/supported-models/) in Generative APIs.
+These limits are in place to protect you against:
+- Long generation which may be ended by an HTTP timeout. Limits are designed to ensure a model will send its HTTP response in less than 5 minutes.
+- Uncontrolled billing, as several models are known to be able to enter infinite generation loops (specific prompts can make the model generate the same sentence over and over, without stopping at all).
+If you require higher maximum output tokens, you can use [Managed Inference](https://console.scaleway.com/inference/deployments) where these limts do not apply (as your bill will be limited by the size of your deployment).
+
+### Can I use OpenAI libraries and APIs with Scaleway's Generative APIs?
+Yes, Scaleway's Generative APIs are designed to be compatible with OpenAI libraries and SDKs, including the OpenAI Python client library and LangChain SDKs. This allows for seamless integration with existing workflows.
+
+## Billing and monitoring
+
+### How does the free tier work?
 The free tier allows you to process up to 1,000,000 tokens without incurring any costs. After reaching this limit, you will be charged per million tokens processed. Free tier usage is calculated by adding all input and output tokens consumed from all models used.
 For more information, refer to our [pricing page](https://www.scaleway.com/en/pricing/model-as-a-service/#generative-apis) or access your bills by token types and models in [billing section from Scaleway Console](https://console.scaleway.com/billing/payment) (past and provisional bills for the current month).
 
@@ -46,22 +78,28 @@ Total tokens consumed: `900k`
 Total billed consumption: `6 million tokens`
 Total bill: `3.20€`
 
-Note that in this example, the first line where the free tier applies will not display in your current Scaleway bills by model but will instead be listed under `Generative APIs Free Tier - First 1M tokens for free`.
+Note that in this example, the first line where the free tier applies will not display in your current Scaleway bills by model, but will instead be listed under `Generative APIs Free Tier - First 1M tokens for free`.
 
-## What is a token and how are they counted?
+### What are tokens and how are they counted?
 A token is the minimum unit of content that is seen and processed by a model. Hence, token definitions depend on input types:
 - For text, on average, `1` token corresponds to `~4` characters, and thus `0.75` words (as words are on average five characters long)
 - For images, `1` token corresponds to a square of pixels. For example, `mistral-small-3.1-24b-instruct-2503` model image tokens of `28x28` pixels (28-pixels height, and 28-pixels width, hence `784` pixels in total).
 
 The exact token count and definition depend on [tokenizers](https://huggingface.co/learn/llm-course/en/chapter2/4) used by each model. When this difference is significant (such as for image processing), you can find detailed information in each model's documentation (for instance in [`mistral-small-3.1-24b-instruct-2503` size limit documentation](/managed-inference/reference-content/model-catalog/#mistral-small-31-24b-instruct-2503)). When the model is open, you can also find this information in the model files on platforms such as Hugging Face, usually in the `tokenizer_config.json` file.
 
-## How can I monitor my token consumption?
+### How can I monitor my token consumption?
 You can see your token consumption in [Scaleway Cockpit](/cockpit/). You can access it from the Scaleway console under the [Metrics tab](https://console.scaleway.com/generative-api/metrics).
 Note that:
-- Cockpits are isolated by Projects, hence you first need to select the right project in the Scaleway console before accessing Cockpit to see your token consumption for this Project (you can see the `project_id` in the Cockpit URL: `https://{project_id}.dashboard.obs.fr-par.scw.cloud/`.
-- Cockpit graphs can take up to 1 hour to update token consumption, see [Troubleshooting](https://www.scaleway.com/en/docs/generative-apis/troubleshooting/fixing-common-issues/#tokens-consumption-is-not-displayed-in-cockpit-metrics) for further details.
+- Cockpits are isolated by Project, hence you first need to select the right Project in the Scaleway console before accessing Cockpit to see your token consumption for this Project (you can see the `project_id` in the Cockpit URL: `https://{project_id}.dashboard.obs.fr-par.scw.cloud/`.
+- Cockpit graphs can take up to 1 hour to update token consumption, see [Troubleshooting](/generative-apis/troubleshooting/fixing-common-issues/#tokens-consumption-is-not-displayed-in-cockpit-metrics) for further details.
+
+### Can I configure a maximum billing threshold?
+Currently, you cannot configure a specific threshold after which your usage will be blocked. However:
+- You can [configure billing alerts](/billing/how-to/use-billing-alerts/) to ensure you are warned when you hit specific budget thresholds.
+- Your total billing remains limited by the amount of tokens you can consume within [rate limits](/generative-apis/reference-content/rate-limits/).
+- If you want to ensure fixed billing, you can use [Managed Inference](https://www.scaleway.com/en/inference/), which provides the same set of OpenAI-compatible APIs and a wider range of models.
 
-## How can I give access to token consumption to my users outside of Scaleway?
+### How can I give access to token consumption to my users outside of Scaleway?
 If your users do not have a Scaleway account, you can still give them access to their Generative API usage consumption by either:
 - Providing them an access to Grafana inside [Cockpit](https://console.scaleway.com/cockpit/overview). You can create dedicated [Grafana users](https://console.scaleway.com/cockpit/users) with read-only access (**Viewer** Role). Note that these users will still have access to all other Cockpit dashboards for this project. 
 - Collecting consumption data from the [Billing API](https://www.scaleway.com/en/developers/api/billing/#path-consumption-get-monthly-consumption) and exposing it to your users. Consumption can be detailed by Projects.
@@ -84,53 +122,26 @@ Note that:
 - Cockpits are isolated by Projects. You first need to select the right Project in the Scaleway console before accessing Cockpit to see your token consumption for the desired Project (you can see the `project_id` in the Cockpit URL: `https://{project_id}.dashboard.obs.fr-par.scw.cloud/`.
 - Cockpit graphs can take up to 1 hour to update token consumption, see [Troubleshooting](/generative-apis/troubleshooting/fixing-common-issues/#tokens-consumption-is-not-displayed-in-cockpit-metrics) for further details.
 
-## Can I configure a maximum billing threshold?
-Currently, you cannot configure a specific threshold after which your usage will blocked. However:
-- You can [configure billing alerts](/billing/how-to/use-billing-alerts/) to ensure you are warned when you hit specific budget thresholds.
-- Your total billing remains limited by the amount of tokens you can consume within [rate limits](/generative-apis/reference-content/rate-limits/).
-- If you want to ensure fixed billing, you can use [Managed Inference](https://www.scaleway.com/en/inference/), which provides the same set of OpenAI-compatible APIs and a wider range of models.
-
-## How can I access and use the Generative APIs?
-Access is open to all Scaleway customers. You can start by using the Generative APIs Playground in the Scaleway console to experiment with different models. For integration into applications, you can use the OpenAI-compatible APIs provided by Scaleway. Detailed instructions are available in our [Quickstart guide](/generative-apis/quickstart/).
+## Privacy, performance and rate limiting
 
-## Where are the inference servers located?
-All models are currently hosted in a secure data center located in Paris, France, operated by [OPCORE](https://www.opcore.com/). This ensures low latency for European users and compliance with European data privacy regulations.
-
-## Where can I find the privacy policy regarding Generative APIs?
+### Where can I find the privacy policy regarding Generative APIs?
 You can find the privacy policy applicable to all use of Generative APIs [here](/generative-apis/reference-content/data-privacy/).
 
-## Can I use OpenAI libraries and APIs with Scaleway's Generative APIs?
-Yes, Scaleway's Generative APIs are designed to be compatible with OpenAI libraries and SDKs, including the OpenAI Python client library and LangChain SDKs. This allows for seamless integration with existing workflows.
-
-## What is the difference between Generative APIs and Managed Inference?
-- **Generative APIs**: A serverless service providing access to pre-configured AI models via API, billed per token usage. 
-- **Managed Inference**: Allows deployment of curated or custom models with chosen quantization and instances, offering predictable throughput and enhanced security features like private network isolation and access control. Managed Inference is billed by hourly usage, whether provisioned capacity is receiving traffic or not.
+### What are the SLAs applicable to Generative APIs?
+We are currently working on defining our SLAs for Generative APIs. We will provide more information on this topic soon.
 
-## How do I get started with Generative APIs?
-To get started, explore the [Generative APIs Playground](/generative-apis/quickstart/#start-with-the-generative-apis-playground) in the Scaleway console. For application integration, refer to our [Quickstart guide](/generative-apis/quickstart/), which provides step-by-step instructions on accessing, configuring, and using a Generative APIs endpoint.
+### What are the performance guarantees (vs Managed Inference)?
+We are currently working on defining our performance guarantees for Generative APIs. We will provide more information on this topic soon.
 
-## Are there any rate limits for API usage?
+### Are there any rate limits for API usage?
 Yes, API rate limits define the maximum number of requests a user can make within a specific time frame to ensure fair access and resource allocation between users. If you require increased rate limits we recommend either:
 - Using [Managed Inference](https://console.scaleway.com/inference/deployments), which provides dedicated capacity and doesn't enforce rate limits (you remain limited by the total provisioned capacity)
 - Contacting your existing Scaleway account manager or our Sales team to discuss volume commitment for specific models that will allow us to increase your quota proportionally.
 
 Refer to our dedicated [documentation](/generative-apis/reference-content/rate-limits/) for more information on rate limits.
 
-## Can I increase maximum output (completion) tokens for a model?
-No, you cannot increase maximum output tokens above [limits for each models](https://www.scaleway.com/en/docs/generative-apis/reference-content/supported-models/) in Generative APIs.
-These limits are in place to protect you against:
-- Long generation which may be ended by an HTTP timeout. Limits are designed to ensure a model will send its HTTP response in less than 5 minutes.
-- Uncontrolled billing, as several models are known to be able to enter infinite generation loops (specific prompts can make the model generate the same sentence over and over, without stopping at all).
-If you require higher maximum output tokens, you can use [Managed Inference](https://console.scaleway.com/inference/deployments) where these limts do not apply (as your bill will be limited by the size of your deployment).
 
-## What is the model lifecycle for Generative APIs?
-Scaleway is dedicated to updating and offering the latest versions of generative AI models, while ensuring older models remain accessible for a significant time, and also ensuring the reliability of your production applications. Learn more in our [model lifecycle policy](/generative-apis/reference-content/model-lifecycle/).
 
-## What are the SLAs applicable to Generative APIs?
-We are currently working on defining our SLAs for Generative APIs. We will provide more information on this topic soon.
 
-## What are the performance guarantees (vs Managed Inference)?
-We are currently working on defining our performance guarantees for Generative APIs. We will provide more information on this topic soon.
 
-## Do model licenses apply when using Generative APIs?
-Yes, you need to comply with model licenses when using Generative APIs. Applicable licenses are available for [each model in our documentation](/generative-apis/reference-content/supported-models/#vision-models) and in Console Playground.
+
@@ -3,7 +3,7 @@ title: Adding AI to IntelliJ IDEA using Continue and Generative APIs
 description: Learn how to integrate AI-powered code models into IntelliJ IDEA with Continue and Scaleway's Generative APIs.
 tags: generative-apis ai machine-learning language-models code-assistance intellij-idea continue
 dates:
-  validation: 2025-02-14
+  validation: 2025-08-20
   posted: 2025-02-14
 ---
 import Requirements from '@macros/iam/requirements.mdx'
@@ -20,8 +20,6 @@ This guide will help you integrate AI-powered code models into JetBrain's Intell
 - A valid [API key](/iam/how-to/create-api-keys/) for API authentication
 - Installed [IntelliJ IDEA](https://www.jetbrains.com/idea/) on your local machine.
 
-
-
 You can install Continue from the [JetBrains marketplace](https://plugins.jetbrains.com/plugin/22707-continue):
 
 1. Open IntelliJ IDEA and go to **Preferences/Settings** (`Ctrl+Alt+S` on Windows/Linux, `Cmd+,` on macOS).
@@ -149,7 +147,6 @@ Alternatively, a `config.json` file can be used with the following format. Note
 }
 ```
 
-
 <Message type="tip">
   For more details on configuring `config.yaml`, refer to the [official Continue documentation](https://docs.continue.dev/reference).
   If you want to limit access to a specific Scaleway Project, you should add the field `"apiBase": "https://api.scaleway.ai/###PROJECT_ID###/v1/"` for each model (ie. `models`, `embeddingsProvider` and `tabAutocompleteModel`) since the default URL `https://api.scaleway.ai/v1/` can only be used with the `default` project.

@@ -3,7 +3,7 @@ title: Adding AI to VS Code using Continue and Generative APIs
 description: Learn how to integrate AI-powered code models into VS Code with Continue and Scaleway's Generative APIs.
 tags: generative-apis ai machine-learning language-models code-assistance vs-code continue
 dates:
-  validation: 2025-02-14
+  validation: 2025-08-20
   posted: 2025-02-14
 ---
 import Requirements from '@macros/iam/requirements.mdx'

@@ -3,7 +3,7 @@ title: Supported models
 description: This page lists which open-source chat or embedding models Scaleway is currently hosting
 tags: generative-apis ai-data supported-models
 dates:
-  validation: 2025-02-14
+  validation: 2025-08-20
   posted: 2024-09-02
 ---
 

@@ -3,7 +3,7 @@ title: How to create and manage ACLs
 description: Discover how to create and manage ACLs for Scaleway Load Balancers. Improve security, manage traffic efficiently, and optimize your network setup easily.
 tags: acl load-balancer acls access-control-list access control HTTP-header filter allow reject redirect HTTPSacl load-balancer access-control access-control-list 
 dates:
-  validation: 2025-02-13
+  validation: 2025-08-20
   posted: 2022-02-04
 ---
 import Requirements from '@macros/iam/requirements.mdx'