Model providers

Clearly AI supports Bring Your Own Key (BYOK) for LLM providers. Organizations can bring their own provider credentials from OpenAI, OpenRouter, Anthropic, Google Generative AI, Google Vertex AI, Azure OpenAI, and AWS Bedrock, giving you control over which models power reviews, chats, cost allocation, and data residency.

Model data handling and contractual terms depend on your organization's deployment, selected provider, and credential configuration. Confirm the retention and BAA terms that apply to your organization before sending regulated data. If you configure BYOK, Clearly AI sends model requests through the provider configuration saved for your organization.

Saved keys are encrypted at rest and are not displayed after saving.

Feature assignments

Use Feature Assignments in Settings > Organization > Models to choose which provider and model Clearly AI uses for specific chat and review workflows.

Assignments can use Clearly AI's managed provider defaults or an enabled BYOK provider. Administrators can assign models independently to review generation, supporting analysis tasks, and chat workflows.

When a selected model supports reasoning controls, you can set the reasoning effort for that feature.

OpenAI

What you need: An API key from platform.openai.com/api-keys.

1. In Settings > Organization > Models, find the OpenAI card.
2. Paste your API key.
3. Click Test to verify the connection, then Save.

Data residency: OpenAI data residency is configured in your OpenAI project or account. If OpenAI gives you a regional API endpoint, set base_url in the Advanced Options JSON:

{
  "base_url": "https://eu.api.openai.com/v1"
}

Your tenant's default OpenAI endpoint is configured during onboarding. US data residency tenants use https://us.api.openai.com/v1. Only HTTPS URLs are accepted.

Main options:

OpenRouter

What you need: An API key from openrouter.ai/settings/keys.

1. In Settings > Organization > Models, find the OpenRouter card.
2. Paste your API key.
3. Optionally set HTTP Referer and App Title.
4. Click Test, then Save.

Main options:

Anthropic

What you need: An API key from console.anthropic.com/settings/keys.

1. In Settings > Organization > Models, find the Anthropic card.
2. Paste your API key.
3. Click Test, then Save.

Main options:

Google Generative AI

What you need: A Gemini API key from aistudio.google.com/app/apikey.

1. In Settings > Organization > Models, find the Google Generative AI card.
2. Paste your API key.
3. Click Test, then Save.

Google Vertex AI

What you need: A Google Cloud project with Vertex AI access, a target location, and either a Vertex API key for Express Mode or application credentials.

1. In Settings > Organization > Models, find the Google Vertex AI card.
2. Enter the Project ID, Location, and Model.
3. Choose Vertex API key (Express Mode) or Application credentials.
4. Paste the API key or credentials.
5. Click Test, then Save.

Main options:

Azure OpenAI

What you need: An Azure OpenAI API key, endpoint, deployment name, and API version.

1. In Settings > Organization > Models, find the Azure OpenAI card.
2. Paste your API key.
3. Enter the Endpoint, Deployment Name, and API Version.
4. Click Test, then Save.

Main options:

AWS Bedrock

What you need: An IAM access key pair with Bedrock permissions, plus a target AWS region.

Creating AWS credentials for Bedrock

1. Open the IAM console and create a new IAM user (e.g., clearly-bedrock).
2. Attach a permissions policy. Recommended starting policy:

   {
     "Version": "2012-10-17",
     "Statement": [
       {
         "Sid": "BedrockInvoke",
         "Effect": "Allow",
         "Action": [
           "bedrock:InvokeModel",
           "bedrock:InvokeModelWithResponseStream"
         ],
         "Resource": [
           "arn:aws:bedrock:*::foundation-model/*",
           "arn:aws:bedrock:*:*:inference-profile/*",
           "arn:aws:bedrock:*:*:application-inference-profile/*"
         ]
       },
       {
         "Sid": "MarketplaceAccess",
         "Effect": "Allow",
         "Action": [
           "aws-marketplace:ViewSubscriptions",
           "aws-marketplace:Subscribe"
         ],
         "Resource": "*"
       }
     ]
   }

   This covers foundation models, cross-region inference profiles, and marketplace model subscriptions. Review and harden as needed: scope Resource ARNs to specific regions or model IDs you actually use, and remove the MarketplaceAccess statement if your models are already subscribed.
3. Go to Security credentials for the user, click Create access key, and select Third-party service.
4. Copy the Access Key ID and Secret Access Key. The secret is shown only once.
5. In the Bedrock console, navigate to Model access and request access to the models you plan to use. Access is per-region.

Connecting in Clearly AI

1. In Settings > Organization > Models, find the AWS Bedrock card.
2. Enter your Access Key ID and Secret Access Key.
3. Select your AWS Region from the dropdown.
4. Optionally enter a model identifier in Test Model to validate inference access.
5. Click Test, then Save.

Example model identifiers:

Model availability varies by region. Bedrock also supports Meta Llama and other foundation models; use the model ID shown in the AWS Bedrock console.

Supported regions: us-east-1, us-east-2, us-west-2, eu-west-1, eu-central-1, eu-central-2, eu-west-3, ap-northeast-1, ap-northeast-2, ap-south-1, ap-southeast-1, ap-southeast-2, ap-southeast-3, ca-central-1, sa-east-1.

Main options (via inference_configuration):

Bedrock wraps inference parameters inside an inference_configuration object:

{
  "inference_configuration": {
    "temperature": 0.2,
    "max_tokens": 4096,
    "top_p": 0.9
  }
}

For model-specific parameters (e.g., enabling extended thinking on Claude), use additional_model_request_fields:

{
  "additional_model_request_fields": {
    "thinking": { "type": "enabled", "budget_tokens": 1024 }
  }
}

Requests use the AWS Region you select. If you enter a cross-region or global inference profile, AWS may route inference according to that profile. Use region-specific model IDs or inference profiles when residency boundaries matter. For VPC-private access, set endpoint_url in the Advanced Options to your VPC endpoint.

Advanced options

Each provider card includes an Advanced Options (JSON) field. These parameters are passed to the model provider client at inference time. Leave as {} for defaults. See the provider-specific tables above for common options.

Important notes

• Enable providers before assignment. Feature assignments that use BYOK require the provider configuration to be enabled.
• Test before saving. Any change to credentials, model, or advanced options requires a successful connection test before you can save.
• Keys are encrypted at rest. Saved keys are never displayed in the UI.
• Removing a configuration removes that saved provider setup. Update any feature assignments that used the provider, or use Clearly AI's managed defaults.