How to add your own models
Follow this setup guide to add your own AI models to your workspace.
Prerequisites
Make sure that before you start configuring a model, you have ensured these prerequisites to setting up your BYOK workspace:
- You have configured the model of your choice in your provider
- You have configured the respective model key in your provider
How to add custom models to your workspace
ajan comes with prebuilt configurations for the most popular AI models. You can pick from this catalog and we fill in all the provider settings automatically. All you need to do is provide your model credentials, and we handle the rest.
Prebuilt configurations are available for the most popular completion, image generation, and embedding models.
1. Select the model
Navigate to Workspace Settings -> Models and click Add custom model.
Select a completion model from the catalog. When you choose a pre-configured model, ajan keeps all settings up to date. Pricing, context windows, and capabilities always reflect the latest from your provider.
If your model isn't in the catalog, you can Set up your model manually.
2. Configure your model
If you selected a pre-configured model, ajan has already filled in the technical settings. You only need to decide how to make it available in your workspace. If you chose manual setup, you'll configure everything yourself, from provider details to pricing and capabilities.
Open the section below that matches your setup.
ajan handles all the technical settings for you. The only decisions left are how you want to roll the model out to your workspace.
- Set the model to invisible while you test it. Make it visible once you're satisfied with how it responds. Hiding removes the model from the picker and clears it as the default model for users who had it set.
- Decide whether the model should be available when building agents.
- Mark it as a premium model if you don't want users setting it as their personal default.
- Use maintenance mode to pause usage without removing the model.
If you chose to set up the model manually, you now need to configure the technical parameters yourself. This includes general model info, connection settings, pricing, and capability flags that ajan would otherwise manage for you.
Setting up a model manually gives you full control over every configuration parameter. Keep in mind that you are responsible for keeping these settings accurate. ajan won't automatically update things like context window size or pricing when a provider makes changes.
General
- Provider: the organization that built the model (e.g. OpenAI, Anthropic).
- Provider model name: the model identifier used by your provider.
- Region: where the model is hosted. Shown to users as EU, US, or Global. Some dedicated deployments also offer CH.
- Description (EN / DE): optional, max 150 characters each.
Configure
- Context window size: the model's context window in tokens.
- Max output tokens: maximum tokens the model can generate per response.
Pricing
- Input token price (USD / 1M tokens)
- Output token price (USD / 1M tokens)
- Cache read token price (USD / 1M tokens)
- Cache write token price (USD / 1M tokens)
Capabilities
- Image analysis: enable if the model supports image analysis.
- Canvas: Enable Canvas when the model supports the Document Editor.
- Supports tools: enable if the model supports tool calling.
- Supports temperature: if disabled, the temperature parameter will not be sent to the model.
- Always show reasoning: for models that natively produce reasoning output (e.g. o3, o4 Mini).
- Use Responses API: use OpenAI's Responses API instead of Chat Completions.
3. Configure your deployment
A deployment connects your model to a specific provider endpoint using a model key. Select a key you have already configured in Model Keys, or add a new one.
- Key: select an existing model key or add a new one. Under Custom headers, add HTTP headers your provider requires for chat and embedding requests.
- Model identifier: the deployment name or model ID as defined in your provider's configuration.
- Tokens per minute (TPM): optional. Limit how many tokens per minute can be processed through this deployment.
4. Review your model
Once you have tested the model successfully, review the final settings before saving and making it available to your workspace.
You have now added your own completion model to ajan 🎉
1. Select the model
Navigate to Workspace Settings -> Models and click Add custom model.
Select an image generation model from the catalog. When you choose a pre-configured model, ajan keeps all settings up to date. Pricing, context windows, and capabilities always reflect the latest from your provider.
If your model isn't in the catalog, you can Set up your model manually.
2. Configure your model
If you selected a pre-configured model, ajan has already filled in the technical settings. You only need to decide how to make it available in your workspace. If you chose manual setup, you'll configure everything yourself, from provider details to pricing and capabilities.
Open the section below that matches your setup.
ajan handles all the technical settings for you. You only need to set the region and decide how the model is available in your workspace.
- Region: select where the model is hosted (EU, US, or Global, and CH on dedicated deployments that offer it). This is shown to users so they know where their data is processed.
- Visible to users: when enabled, this model is available to users in the workspace. When disabled, it's hidden from the model picker. Keep it off while you test it.
- Max images per user: optional. Set a limit on how many images a user can generate within a time window (e.g. 50 per 3 hours).
If you chose to set up the model manually, you now need to configure the technical parameters yourself. This includes general model info, description, usage limits, and capability flags that ajan would otherwise manage for you.
Setting up a model manually gives you full control over every configuration parameter. Keep in mind that you are responsible for keeping these settings accurate. ajan won't automatically update them when a provider makes changes.
General
- Model name: the display name shown to users.
- Provider model name: the model identifier used by your provider.
- Provider: the organization that built the model (e.g. OpenAI, Google).
- Region: where the model is hosted. Shown to users as EU, US, or Global. Some dedicated deployments also offer CH.
Visibility
- Visible to users: when enabled, this model is available to users in the workspace. When disabled, it's hidden from the model picker.
Description
- Description (EN): optional, max 150 characters.
- Description (DE): optional, max 150 characters.
Configure
- Max images per user: optional. Set a limit on how many images a user can generate within a time window (e.g. 50 per 3 hours).
Advanced
- Image generation: all image models can generate images by default.
- Image editing: enable if the model supports editing existing images.
3. Configure your deployment
A deployment connects your model to a specific provider endpoint using a model key. Select a key you have already configured in Model Keys, or add a new one.
- Key: select an existing model key or add a new one.
- Model identifier: the deployment name or model ID as defined in your provider's configuration.
- Tokens per minute (TPM): optional. Limit how many tokens per minute can be processed through this deployment.
4. Review your model
Once you have tested the model successfully, review the final settings before saving and making it available to your workspace.
You have now added your own image generation model to ajan 🎉
1. Select the model
Navigate to Workspace Settings -> Models and click Add custom model.
Select an embedding model from the catalog. When you choose a pre-configured model, ajan keeps all settings up to date. Pricing, context windows, and capabilities always reflect the latest from your provider.
If your model isn't in the catalog, you can Set up your model manually.
2. Configure your model
If you selected a pre-configured model, ajan has already filled in the technical settings. You only need to decide how to make it available in your workspace. If you chose manual setup, you'll configure everything yourself, from provider details to pricing and capabilities.
Open the section below that matches your setup.
ajan handles all the technical settings for you. The only decision left is whether to make the model available to users.
- Visible to users: when enabled, this model is available to users in the workspace. When disabled, it's hidden from the model picker. Keep it off while you test it.
If you chose to set up the model manually, you now need to configure the technical parameters yourself. This includes general model info, connection settings, and capability flags that ajan would otherwise manage for you.
Setting up a model manually gives you full control over every configuration parameter. Keep in mind that you are responsible for keeping these settings accurate. ajan won't automatically update them when a provider makes changes.
Visibility
- Visible to users: when enabled, this model is available to users in the workspace. When disabled, it's hidden from the model picker.
Configure
- Vector Similarity Threshold: minimum similarity score (0.1–1) required for embedding matches. Higher values return more relevant but fewer results. Defaults to 0.5.
3. Configure your deployment
A deployment connects your model to a specific provider endpoint using a model key. Select a key you have already configured in Model Keys, or add a new one.
- Key: select an existing model key or add a new one. Under Custom headers, add HTTP headers your provider requires for chat and embedding requests.
- Model identifier: the deployment name or model ID as defined in your provider's configuration.
4. Review your model
Once you have tested the model successfully, review the final settings before saving and making it available to your workspace.
You have now added your own embedding model to ajan 🎉