Add external models - Writer AI Studio

How to Add and Manage External AI Models in AI Studio

This guide covers how to add and manage external AI models in AI Studio. After configuring external models, your organization can use models from providers like AWS Bedrock, Microsoft Azure (Azure OpenAI), and NVIDIA NIM alongside Writer’s Palmyra models when building agents.

External models are available on enterprise plans. Org admins, IT admins, and users with AI Studio full access roles can add and manage models.

AI Studio supports text generation and embedding models from external providers. Video, audio, and image generation models are not supported.

How external models work

The Models page in AI Studio provides a unified governance layer for managing AI models across your organization. You can add models from external providers and control which teams have access to use them when building agents.

The Models page also displays Writer’s Palmyra models. Palmyra models are always available to all teams and don’t require configuration. Select any Palmyra model to view its health status and details.

External model requests flow through the following steps:

1. Credential configuration: Administrators add provider credentials (API keys or IAM roles) to AI Studio.
2. Model selection: Administrators choose which models from the provider to enable.
3. Access control: Administrators assign model access to all teams or specific teams.
4. Agent building: Developers with access can select the model when building agents.
5. Request routing: When agents run, requests route through the configured provider credentials.

External Provider(AWS Bedrock, Microsoft Azure, etc.)Credential StoreAI StudioAgent BuilderExternal Provider(AWS Bedrock, Microsoft Azure, etc.)Credential StoreAI StudioAgent BuilderSelect model for agentRetrieve provider credentialsReturn credentialsForward request with authReturn model responseDeliver response to agent

Available providers

AI Studio supports external models from the following providers. Select a provider to view detailed configuration instructions.

AWS Bedrock models are currently supported in the following regions: us-east-1, us-west-1, us-west-2, and eu-west-1. Additional regions may be added in future releases.

Add an external model

Add external models in AI Studio under Models & Guardrails > Models.

Add a model in AI Studio

For reusability and easier rotation, create named credentials before adding a model. You can also enter credentials inline during the Add Model flow (see Enter credentials directly).

1. Navigate to Models & Guardrails > Models in AI Studio.
2. Select + Add model.
3. Choose your provider (for example, AWS Bedrock or Microsoft Azure).
4. Enter your credentials.
5. Choose which models to enable from the available list.
6. Configure team access (all teams or specific teams).
7. Select Add model to complete the setup.

You can reuse credentials across multiple models. See Manage credentials for details.

Manage team access

Control which teams can use external models when building agents.

Configure model availability

When adding a model, you can set access to:

• All teams: The model is immediately available to everyone with builder access.
• Specific teams: Restrict the model to selected teams.

Update team access

To view or update which teams can access a model:

1. Navigate to Models & Guardrails > Models in AI Studio.
2. View the current team access in the Team Access column.
3. Select the menu icon and choose Edit to update team access.

Credentials are configured at the model level, not the team level. Team access controls who can use the model, but all authorized users share the same underlying provider credentials. Team members with access can use the models but cannot view the credentials.

Monitor model health

AI Studio automatically monitors the health of your configured models every 5 minutes and displays their status in the Models list.

View model health details

Select any model in the Models list to view detailed health information:

• Health status: Current health state (Healthy or Unhealthy).
• Last checked: Timestamp of the most recent health check.
• Model details: Provider, model name, and model ID.
• Team access: Which teams can use this model.

To manually refresh the health status, select the Refresh button in the model details panel. This immediately checks the model’s availability instead of waiting for the next automatic check.

Automatic recovery

When a model becomes unhealthy, AI Studio temporarily removes it from the available models pool. After a cooldown period, the system automatically retries the model. If the underlying issue is resolved, the model returns to a healthy status without any manual intervention. You don’t need to delete or reconfigure a model that shows an unhealthy status due to transient issues like temporary provider outages. The system handles recovery automatically.

Troubleshoot persistent issues

If a model remains unhealthy, check the following:

• Credential validity: Credentials may have expired or been revoked. Update credentials if needed.
• Provider status: The provider service may be experiencing extended downtime. Check the provider’s status page.
• Regional availability: The model may not be available in the configured region.
• Permission changes: IAM policies may have been modified. Verify your IAM user or role still has the required permissions. For Microsoft Azure, confirm API keys are still valid and deployments were not removed or renamed.
• Model deprecation: The provider may have deprecated the model. Check the provider’s documentation for model availability.

Manage credentials

AI Studio stores provider credentials as named credential sets that you can reuse across multiple models. You can manage credentials from the dedicated LLM Credentials page or create them when adding a model.

Create credentials from the LLM Credentials page

To create credentials before adding models:

1. Navigate to Models & Guardrails > LLM Credentials in AI Studio.
2. Select Add credentials.
3. Enter a Credential name (for example, production-nvidia, dev-aws-credentials, or prod-azure-openai).
4. Select the Provider (for example, Bedrock, Microsoft Azure, or NVIDIA NIM).
5. Enter the provider-specific authentication details.
6. Select Save.

Enter credentials directly when adding a model

When adding a model, you can enter credentials directly in the Add Model form instead of selecting existing credentials. Credentials entered this way are stored with the model but are not saved as a named credential set for reuse with other models. To create reusable credentials that you can share across multiple models, use the LLM Credentials page instead.

Reuse credentials across models

When adding additional models from the same provider:

1. Select your existing credentials from the Credentials name dropdown.
2. The stored authentication details are automatically applied.
3. You don’t need to re-enter access keys or other sensitive values.

This approach simplifies management when you have multiple models from the same provider—update credentials in one place and all associated models use the updated values.

Update credentials

How you update expired or rotated credentials depends on how you originally configured them:

Named credentials (created on the LLM Credentials page):

1. Navigate to Models & Guardrails > LLM Credentials in AI Studio.
2. Locate the credential in the list.
3. Select the menu icon and choose Edit credentials.
4. Update the authentication details and save.

All models using this credential automatically use the updated values.

Inline credentials (entered directly when adding a model):

1. Navigate to Models & Guardrails > Models in AI Studio.
2. Locate the model in the list.
3. Select the menu icon and choose Edit.
4. Update the credential values and save.

For easier credential management, use named credentials from the LLM Credentials page. Named credentials can be updated in one place and automatically apply to all models using them.

Delete credentials

To delete a named credential:

1. Navigate to Models & Guardrails > LLM Credentials in AI Studio.
2. Locate the credential in the list.
3. Select the menu icon and choose Delete.

Before deleting credentials, verify that no models are using them. Models using deleted credentials will fail to authenticate with the provider.

Security best practices

Follow these practices when managing provider credentials:

• Use dedicated service accounts rather than personal credentials.
• Rotate credentials on a regular schedule (for example, every 90 days).
• Use descriptive credential names that indicate environment or purpose (for example, prod-bedrock-us-east).
• For AWS, prefer IAM Role ARN over access keys when possible.

Edit a model

To update an external model’s configuration, including credentials and team access:

1. Navigate to Models & Guardrails > Models in AI Studio.
2. Locate the model in the list.
3. Select the menu icon and choose Edit.
4. Update the configuration and save.

Delete a model

To remove an external model from AI Studio:

1. Navigate to Models & Guardrails > Models in AI Studio.
2. Locate the model in the list.
3. Select the menu icon and choose Delete.

Before deleting a model, verify that no active agents depend on it. Agents that reference a deleted model will return a “model not found” error when they run. You’ll need to update the agent to use a different model before it can run successfully.

Use external models

Once you add an external model, it’s available to use the same way as Palmyra models.

Agent Builder and no-code apps

In Agent Builder and no-code chat apps, external models appear in the Model dropdown alongside Palmyra models. Select any model your team has access to.

API usage

External models use the same Writer API as Palmyra models. Use the List models endpoint to see all available models and their IDs, then pass the model ID to any endpoint that supports the model parameter.

Code Examples

cURL

curl https://api.writer.com/v1/chat/completions \
  -H "Authorization: Bearer $WRITER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "anthropic.claude-3-sonnet-20240229-v1:0",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Python

from writerai import Writer

client = Writer()

response = client.chat.chat(
    model="anthropic.claude-3-sonnet-20240229-v1:0",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)

JavaScript

import Writer from "writer-sdk";

const client = new Writer();

const response = await client.chat.chat({
  model: "anthropic.claude-3-sonnet-20240229-v1:0",
  messages: [{ role: "user", content: "Hello!" }]
});
console.log(response.choices[0].message.content);

Next steps

• Configure AWS Bedrock: Set up AWS credentials and add Bedrock models.
• Configure Microsoft Azure: Set up Azure OpenAI and add Microsoft Azure models.
• Configure NVIDIA NIM: Set up NVIDIA credentials and add NIM models.
• Choose a model: Compare Palmyra models with external provider models.
• Palmyra models: Explore Writer’s Palmyra model capabilities.