Model providers give your workspace access to AI models. Every application you build needs models to function, and configuring providers at the workspace level means all team members can use them across all projects.

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Bring Your Own Keys

You connect model providers using your own API keys for direct access to providers like OpenAI, Anthropic, or Google. You get full control over your credentials and billing directly through each provider. You can also configure multiple credentials per provider and set up load balancing across them.

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Configure Providers

Only workspace admins and owners can configure model providers. The process is consistent across providers:
1

Navigate to Settings → Model Providers

Access the model provider configuration in your workspace settings.
2

Select your provider

Choose from OpenAI, Anthropic, Google, Cohere, or other supported providers.
3

Add credentials

Enter your API key and any additional configuration required by the provider.
4

Test and save

Intellos validates your credentials before making the provider available to your workspace.

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Supported Providers

Large Language Models:
  • OpenAI (GPT-4, GPT-3.5-turbo)
  • Anthropic (Claude)
  • Google (Gemini)
  • Cohere
  • Local models via Ollama
Embedding Models:
  • OpenAI Embeddings
  • Cohere Embeddings
  • Azure OpenAI
  • Local embedding models
Specialized Models:
  • Image generation (DALL-E, Stable Diffusion)
  • Speech (Whisper, ElevenLabs)
  • Moderation APIs

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Provider Configuration Examples

Required: API Key from OpenAI PlatformOptional: Custom base URL for Azure OpenAI or proxies, Organization ID for organization-scoped usageAvailable Models: GPT-4, GPT-3.5-turbo, DALL-E, Whisper, Text embeddings

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Manage Model Credentials

Add multiple credentials for a model provider’s predefined and custom models, and easily switch between, delete, or modify these credentials. Here are some scenarios where adding multiple credentials is particularly helpful:
  • Environment Isolation: Configure separate model credentials for different environments, such as development, testing, and production. For example, use a rate-limited credential in the development environment for debugging, and a paid credential with stable performance and a sufficient quota in the production environment to ensure service quality.
  • Cost Optimization: Add and switch between multiple credentials from different accounts or model providers to maximize the use of free or low-cost quotas, thereby reducing application development and operational costs.
  • Model Testing: During model fine-tuning or iteration, you may create multiple model versions. By adding credentials for these different versions, you can quickly switch between them to test and evaluate their performance.
Use multiple credentials to configure load balancing for a model.
After installing a model provider and configuring the first credential, click Config in the upper-right corner to perform the following actions:
  • Add a new credential
  • Select a credential as the default for all predefined models
  • Edit a credential
  • Delete a credential
If the default credential is deleted, you must manually specify a new one.
Manage Credentials for Predefined Models

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Configure Model Load Balancing

Model providers typically enforce rate limits on API access within a specific timeframe to ensure stability and fair use. For enterprise applications, a high volume of concurrent requests from a single credential can easily trigger these limits, disrupting user access. An effective solution is load balancing, which distributes request traffic across multiple model credentials. This prevents rate limit issues and single points of failure, ensuring business continuity and faster response times for all users. Intellos employs a round-robin strategy for load balancing, sequentially routing model requests to each credential in the load balancing pool. If a credential hits a rate limit, it is temporarily removed from rotation for one minute to avoid futile retries. To configure load balancing for a model, follow these steps:
  1. In the model list, find the target model, click the corresponding Config, and select Load balancing.
  2. In the load balancing pool, click Add credential to select from existing credentials or add a new one.
    Default Config refers to the default credential currently specified for that model.
    If a credential has a higher quota or better performance, you can add it multiple times to increase its weight in the load balancing rotation, allowing it to handle a larger share of the request load.
    Add Credentials for Load Balancing
  3. Enable at least two credentials in the load balancing pool, then click Save. Models with load balancing enabled will be marked with a special icon.
    Load Balancing Icon
When you switch from load-balancing mode back to the default single-credential mode, your load-balancing configuration is preserved for future use.

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Access and Permissions

Providers bill you directly through each provider (OpenAI, Anthropic, etc.). Team access follows workspace permissions:
  • Owners/Admins can configure, modify, and remove providers
  • Editors/Members can view available providers and use them in applications
API keys are stored securely but grant workspace-wide model access. Only give admin privileges to trusted team members.

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Troubleshooting

Authentication Failed: Verify API key accuracy, check expiration, ensure sufficient credits, confirm key permissions. Model Not Available: Check provider configuration includes the model, verify API key tier access, refresh provider settings. Rate Limits: Upgrade your provider account, implement request queuing, or configure load balancing across multiple credentials.