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Custom Providers

Custom providers allow you to create multiple instances of the same base provider, each with different configurations and access patterns. The key feature is request type control, which enables you to restrict what operations each custom provider instance can perform.

Think of custom providers as “multiple views” of the same underlying provider — you can create several custom configurations for OpenAI, Anthropic, or any other provider, each optimized for different use cases while sharing the same API keys and base infrastructure.

  • Multiple Provider Instances: Create several configurations of the same base provider (e.g., multiple OpenAI configurations)
  • Request Type Control: Restrict which operations (chat, embeddings, speech, etc.) each custom provider can perform
  • Custom Naming: Use descriptive names like “openai-production” or “openai-staging”
  • Provider Reuse: Maximize the value of your existing provider accounts

Custom providers are configured using the custom_provider_config field, which extends the standard provider configuration. The main purpose is to create multiple instances of the same base provider, each with different request type restrictions.

Important: The allowed_requests field follows a specific behavior:

  • Omitted entirely: All operations are allowed (default behavior)
  • Partially specified: Only explicitly set fields are allowed, others default to false
  • Fully specified: Only the operations you explicitly enable are allowed
  • Present but empty object ({}): All fields are set to false

Provider Configuration Interface

  1. Go to http://localhost:8080
  2. Navigate to “Providers” in the sidebar
  3. Click “Add New Provider”
  4. Choose a unique provider name (e.g., “openai-custom”)
  5. Select the base provider type (e.g., “openai”)
  6. Configure which request types are allowed
  7. Save configuration

Control which operations your custom provider can perform. The behavior is:

  • If allowed_requests is not specified: All operations are allowed by default
  • If allowed_requests is specified: Only the fields set to true are allowed, all others default to false

Available operations:

  • text_completion: Legacy text completion requests
  • text_completion_stream: Streaming text completion requests
  • chat_completion: Standard chat completion requests
  • chat_completion_stream: Streaming chat responses
  • responses: Standard responses requests
  • responses_stream: Streaming responses requests
  • embedding: Text embedding generation
  • speech: Text-to-speech conversion
  • speech_stream: Streaming text-to-speech
  • transcription: Speech-to-text conversion
  • transcription_stream: Streaming speech-to-text

Custom providers can be built on these supported providers:

  • openai - OpenAI API
  • anthropic - Anthropic Claude
  • bedrock - AWS Bedrock
  • cohere - Cohere
  • gemini - Gemini
  • replicate - Replicate

The request_path_overrides field allows you to override the default API endpoint paths for specific request types. This is useful when:

  • Connecting to custom or self-hosted model providers
  • Integrating with proxies that expect specific URL patterns
  • Using provider forks with modified API paths

The field accepts a mapping of request types to either custom paths or full URLs:

Using Paths (relative to base_url):

{
"request_path_overrides": {
"chat_completion": "/v1/chat/completions",
"chat_completion_stream": "/v1/chat/completions",
"embedding": "/v1/embeddings",
"text_completion": "/v1/completions"
}
}

Using Full URLs (bypasses base_url):

{
"request_path_overrides": {
"chat_completion": "https://specific-endpoint.com/chat",
"embedding": "http://another-service:8080/embeddings"
}
}

Example: OpenAI-Compatible Endpoint with Custom Paths

{
"custom-llm": {
"keys": [{ "name": "custom-llm-key-1", "value": "env.PROVIDER_API_KEY", "models": [], "weight": 1.0 }],
"network_config": {
"base_url": "https://your-openai-compatible-endpoint.com"
},
"custom_provider_config": {
"base_provider_type": "openai",
"allowed_requests": {
"chat_completion": true,
"chat_completion_stream": true
},
"request_path_overrides": {
"chat_completion": "/api/v2/chat",
"chat_completion_stream": "/api/v2/chat"
}
}
}
}

In this example, instead of using OpenAI’s default /v1/chat/completions path, requests will be sent to https://custom-endpoint.example.com/api/v2/chat.

TLS for Self-Signed or Internal Certificates

Section titled “TLS for Self-Signed or Internal Certificates”

When connecting to providers with HTTPS endpoints that use self-signed certificates or internal CAs (e.g., air-gapped environments, internal services), you can configure TLS in network_config:

FieldTypeDescription
insecure_skip_verifybooleanDisable TLS certificate verification. Use only for trusted internal environments. Not recommended for production.
ca_cert_pemstringPEM-encoded CA certificate to trust for provider connections. Use when the endpoint uses a custom CA.

These options are mutually exclusive. Do not set insecure_skip_verify: true together with ca_cert_pem; provider config validation rejects that combination.

Option 1: Skip verification (air-gapped / self-signed)

{
"my-air-gapped-provider": {
"keys": [{ "name": "key-1", "value": "env.API_KEY", "models": [], "weight": 1.0 }],
"network_config": {
"base_url": "https://internal-llm.example.com",
"insecure_skip_verify": true
},
"custom_provider_config": {
"base_provider_type": "openai",
"allowed_requests": {
"chat_completion": true,
"chat_completion_stream": true
}
}
}
}

Option 2: Custom CA certificate (preferred when you have the CA)

{
"my-internal-provider": {
"keys": [{ "name": "key-1", "value": "env.API_KEY", "models": [], "weight": 1.0 }],
"network_config": {
"base_url": "https://internal-llm.example.com",
"ca_cert_pem": "-----BEGIN CERTIFICATE-----\n...\n-----END CERTIFICATE-----"
},
"custom_provider_config": {
"base_provider_type": "openai",
"allowed_requests": {
"chat_completion": true,
"chat_completion_stream": true
}
}
}
}

Create different configurations for production, staging, and development environments:

{
"openai-production": {
"keys": [{ "name": "openai-prod-key-1", "value": "env.PROVIDER_API_KEY", "models": [], "weight": 1.0 }],
"custom_provider_config": {
"base_provider_type": "openai",
"allowed_requests": {
"chat_completion": true,
"chat_completion_stream": true,
"embedding": true,
"speech": true,
"speech_stream": true
}
}
},
"openai-staging": {
"keys": [{ "name": "openai-stage-key-1", "value": "env.PROVIDER_API_KEY", "models": [], "weight": 1.0 }],
"custom_provider_config": {
"base_provider_type": "openai",
"allowed_requests": {
"chat_completion": true,
"chat_completion_stream": true,
"embedding": true,
"speech": false,
"speech_stream": false
}
}
},
"openai-dev": {
"keys": [{ "name": "openai-dev-key-1", "value": "env.PROVIDER_API_KEY", "models": [], "weight": 1.0 }],
"custom_provider_config": {
"base_provider_type": "openai",
"allowed_requests": {
"chat_completion": true,
"chat_completion_stream": false,
"embedding": false,
"speech": false,
"speech_stream": false
}
}
}
}

Restrict capabilities based on user roles or team permissions. You can then create virtual keys for better management of who can access which providers, providing granular control over team permissions and resource usage. This integrates seamlessly with DeepIntShield’s governance features for comprehensive access control and monitoring:

{
"openai-developers": {
"keys": [{ "name": "openai-developers-key-1", "value": "env.PROVIDER_API_KEY", "models": [], "weight": 1.0 }],
"custom_provider_config": {
"base_provider_type": "openai",
"allowed_requests": {
"chat_completion": true,
"chat_completion_stream": true,
"embedding": true,
"text_completion": true
}
}
},
"openai-analysts": {
"keys": [{ "name": "openai-analysts-key-1", "value": "env.PROVIDER_API_KEY", "models": [], "weight": 1.0 }],
"custom_provider_config": {
"base_provider_type": "openai",
"allowed_requests": {
"chat_completion": true,
"embedding": true
}
}
},
"openai-support": {
"keys": [{ "name": "openai-support-key-1", "value": "env.PROVIDER_API_KEY", "models": [], "weight": 1.0 }],
"custom_provider_config": {
"base_provider_type": "openai",
"allowed_requests": {
"chat_completion": true,
"chat_completion_stream": false
}
}
}
}

Test new features with limited user groups:

{
"openai-beta-streaming": {
"keys": [{ "name": "openai-streaming-key-1", "value": "env.PROVIDER_API_KEY", "models": [], "weight": 1.0 }],
"custom_provider_config": {
"base_provider_type": "openai",
"allowed_requests": {
"chat_completion": true,
"chat_completion_stream": true,
"embedding": false
}
}
},
"openai-stable": {
"keys": [{ "name": "openai-stable-key-1", "value": "env.PROVIDER_API_KEY", "models": [], "weight": 1.0 }],
"custom_provider_config": {
"base_provider_type": "openai",
"allowed_requests": {
"chat_completion": true,
"chat_completion_stream": false,
"embedding": true
}
}
}
}

Use your custom provider name in requests:

Terminal window
# Request to custom provider
curl --location 'http://localhost:8080/v1/chat/completions' \
--header 'Content-Type: application/json' \
--data '{
"model": "openai-custom/gpt-4o-mini",
"messages": [
{"role": "user", "content": "Hello!"}
]
}'

Custom providers extend the standard provider configuration system. They inherit all the capabilities of their base provider while adding request type restrictions.

Learn more about provider configuration:

  • Fallbacks - Automatic failover between providers
  • Load Balancing - Intelligent API key management with weighted load balancing
  • Governance - Advanced access control and monitoring