Provider Configuration

Configure multiple AI providers for custom concurrency, queue sizes, proxy settings, and more.

Multi-Provider Setup

Configure multiple providers to seamlessly switch between them. This example shows how to configure OpenAI, Anthropic, and Mistral providers.

Using Web UI

1. Go to http://localhost:8080
2. Navigate to “Model Providers” in the sidebar
3. Select provider and configure keys

Using API

# Add OpenAI provider
curl --location 'http://localhost:8080/api/providers' \
--header 'Content-Type: application/json' \
--data '{
    "provider": "openai",
    "keys": [
        {
            "name": "openai-key-1",
            "value": "env.OPENAI_API_KEY",
            "models": ["*"],
            "weight": 1.0
        }
    ]
}'

# Add Anthropic provider
curl --location 'http://localhost:8080/api/providers' \
--header 'Content-Type: application/json' \
--data '{
    "provider": "anthropic",
    "keys": [
        {
            "name": "anthropic-key-1",
            "value": "env.ANTHROPIC_API_KEY",
            "models": ["*"],
            "weight": 1.0
        }
    ]
}'

# Add vLLM (self-hosted OpenAI-compatible server)
curl --location 'http://localhost:8080/api/providers' \
--header 'Content-Type: application/json' \
--data '{
    "provider": "vllm-local",
    "keys": [
        {
            "name": "vllm-key-1",
            "value": "dummy",
            "models": ["*"],
            "weight": 1.0
        }
    ],
    "network_config": {
        "base_url": "http://vllm-endpoint:8000",
        "default_request_timeout_in_seconds": 60
    },
    "custom_provider_config": {
        "base_provider_type": "openai",
        "allowed_requests": {
            "chat_completion": true,
            "chat_completion_stream": true
        }
    }
}'

Using config.json

Each key in a provider needs to have a unique name.

{
    "providers": {
        "openai": {
            "keys": [
                {
                    "name": "openai-key",
                    "value": "env.OPENAI_API_KEY",
                    "models": ["*"],
                    "weight": 1.0
                }
            ]
        },
        "anthropic": {
            "keys": [
                {
                    "name": "anthropic-key",
                    "value": "env.ANTHROPIC_API_KEY",
                    "models": ["*"],
                    "weight": 1.0
                }
            ]
        },
        "vllm-local": {
            "keys": [
                {
                    "name": "vllm-key",
                    "value": "dummy",
                    "models": ["*"],
                    "weight": 1.0
                }
            ],
            "network_config": {
                "base_url": "http://vllm-endpoint:8000",
                "default_request_timeout_in_seconds": 60
            },
            "custom_provider_config": {
                "base_provider_type": "openai",
                "allowed_requests": {
                    "chat_completion": true,
                    "chat_completion_stream": true
                }
            }
        }
    }
}

Kubernetes DNS (only for custom endpoint): When running in Kubernetes, use fully qualified domain names (FQDN) like http://<service>.<namespace>.svc.cluster.local:8000 for cross-namespace custom endpoints. Short names like http://<service>:8000 only work within the same namespace.

Air-gapped or self-signed certificates: If your custom provider uses HTTPS with a self-signed or internal CA certificate, add "insecure_skip_verify": true or "ca_cert_pem": "-----BEGIN CERTIFICATE-----\n...\n-----END CERTIFICATE-----" to network_config. See Custom Providers - TLS for details.

Making Requests

Once providers are configured, you can make requests to any specific provider. This example shows how to send a request directly to OpenAI’s GPT-4o Mini model. Bifrost handles the provider-specific API formatting automatically.

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

Environment Variables

Set up your API keys for the providers you want to use. Bifrost supports both direct key values and environment variable references with the env. prefix:

export OPENAI_API_KEY="your-openai-api-key"
export ANTHROPIC_API_KEY="your-anthropic-api-key"
export MISTRAL_API_KEY="your-mistral-api-key"
export CEREBRAS_API_KEY="your-cerebras-api-key"
export GROQ_API_KEY="your-groq-api-key"
export COHERE_API_KEY="your-cohere-api-key"

Environment Variable Handling:

• Use "value": "env.VARIABLE_NAME" to reference environment variables
• Use "value": "sk-proj-xxxxxxxxx" to pass keys directly
• All sensitive data is automatically redacted in GET requests and UI responses for security

Advanced Configuration

Weighted Load Balancing

Distribute requests across multiple API keys or providers based on custom weights. This example shows how to split traffic 70/30 between two OpenAI keys, useful for managing rate limits or costs across different accounts.

Using Web UI

1. Navigate to “Model Providers” → “Configurations” → “OpenAI”
2. Click “Add Key” to add multiple keys
3. Set weight values (0.7 and 0.3)
4. Save configuration

Using API

curl --location 'http://localhost:8080/api/providers' \
--header 'Content-Type: application/json' \
--data '{
    "provider": "openai",
    "keys": [
        {
            "name": "openai-key-1",
            "value": "env.OPENAI_API_KEY_1",
            "models": ["*"],
            "weight": 0.7
        },
        {
            "name": "openai-key-2",
            "value": "env.OPENAI_API_KEY_2", 
            "models": ["*"],
            "weight": 0.3
        }
    ]
}'

Using config.json

{
    "providers": {
        "openai": {
            "keys": [
                {
                    "name": "openai-key-1",
                    "value": "env.OPENAI_API_KEY_1",
                    "models": ["*"],
                    "weight": 0.7
                },
                {
                    "name": "openai-key-2",
                    "value": "env.OPENAI_API_KEY_2",
                    "models": ["*"],
                    "weight": 0.3
                }
            ]
        }
    }
}

Model-Specific Keys

Use different API keys for specific models, allowing you to manage access controls and billing separately. This example uses a premium key for advanced reasoning models (o1-preview, o1-mini) and a standard key for regular GPT models.

Using Web UI

1. Navigate to “Model Providers” → “Configurations” → “OpenAI”
2. Add first key with models: ["gpt-4o", "gpt-4o-mini"]
3. Add premium key with models: ["o1-preview", "o1-mini"]
4. Save configuration

Using API

curl --location 'http://localhost:8080/api/providers' \
--header 'Content-Type: application/json' \
--data '{
    "provider": "openai",
    "keys": [
        {
            "name": "openai-key-1",
            "value": "env.OPENAI_API_KEY",
            "models": ["gpt-4o", "gpt-4o-mini"],
            "weight": 1.0
        },
        {
            "name": "openai-key-2",
            "value": "env.OPENAI_API_KEY_PREMIUM",
            "models": ["o1-preview", "o1-mini"],
            "weight": 1.0
        }
    ]
}'

Using config.json

{
    "providers": {
        "openai": {
            "keys": [
                {
                    "name": "openai-key-1",
                    "value": "env.OPENAI_API_KEY",
                    "models": ["gpt-4o", "gpt-4o-mini"],
                    "weight": 1.0
                },
                {
                    "name": "openai-key-2",
                    "value": "env.OPENAI_API_KEY_PREMIUM",
                    "models": ["o1-preview", "o1-mini"],
                    "weight": 1.0
                }
            ]
        }
    }
}

Custom Base URL

Override the default API endpoint for a provider. This is useful for connecting to self-hosted models, local development servers, or OpenAI-compatible APIs like vLLM, Ollama, or LiteLLM.

Using Web UI

1. Navigate to “Model Providers” → “Configurations” → “OpenAI” → “Provider level configuration” → “Network config”
2. Set Base URL: http://localhost:8000/v1
3. Save configuration

Using API

curl --location 'http://localhost:8080/api/providers' \
--header 'Content-Type: application/json' \
--data '{
    "provider": "openai",
    "keys": [
        {
            "name": "openai-key-1",
            "value": "env.OPENAI_API_KEY",
            "models": ["*"],
            "weight": 1.0
        }
    ],
    "network_config": {
        "base_url": "http://localhost:8000/v1"
    }
}'

Using config.json

{
    "providers": {
        "openai": {
            "keys": [
                {
                    "name": "openai-key-1",
                    "value": "env.OPENAI_API_KEY",
                    "models": ["*"],
                    "weight": 1.0
                }
            ],
            "network_config": {
                "base_url": "http://localhost:8000/v1"
            }
        }
    }
}

For self-hosted providers like Ollama and SGL, base_url is required. For standard providers, it’s optional and overrides the default endpoint.

Managing Retries

Configure retry behavior for handling temporary failures and rate limits. This example sets up exponential backoff with up to 5 retries, starting with 1ms delay and capping at 10 seconds - ideal for handling transient network issues.

Using Web UI

1. Navigate to “Model Providers” → “Configurations” → “OpenAI” → “Provider level configuration” → “Network config”
2. Set Max Retries: 5
3. Set Initial Backoff: 1 ms
4. Set Max Backoff: 10000 ms
5. Save configuration

Using API

curl --location 'http://localhost:8080/api/providers' \
--header 'Content-Type: application/json' \
--data '{
    "provider": "openai",
    "keys": [
        {
            "name": "openai-key-1",
            "value": "env.OPENAI_API_KEY",
            "models": ["*"],
            "weight": 1.0
        }
    ],
    "network_config": {
        "max_retries": 5,
        "retry_backoff_initial_ms": 1,
        "retry_backoff_max_ms": 10000
    }
}'

Using config.json

{
    "providers": {
        "openai": {
            "keys": [
                {
                    "name": "openai-key-1",
                    "value": "env.OPENAI_API_KEY",
                    "models": ["*"],
                    "weight": 1.0
                }
            ],
            "network_config": {
                "max_retries": 5,
                "retry_backoff_initial_ms": 1,
                "retry_backoff_max_ms": 10000
            }
        }
    }
}

Custom Concurrency and Buffer Size

Fine-tune performance by adjusting worker concurrency and queue sizes per provider (defaults are 1000 workers and 5000 queue size). This example gives OpenAI higher limits (100 workers, 500 queue) for high throughput, while Anthropic gets conservative limits to respect their rate limits.

Using Web UI

1. Navigate to “Model Providers” → “Configurations” → → “Provider level configuration” → “Performance tuning”
2. Set Concurrency: Worker count (100 for OpenAI, 25 for Anthropic)
3. Set Buffer Size: Queue size (500 for OpenAI, 100 for Anthropic)
4. Save configuration

Using API

# OpenAI with high throughput settings
curl --location 'http://localhost:8080/api/providers' \
--header 'Content-Type: application/json' \
--data '{
    "provider": "openai",
    "keys": [
        {
            "name": "openai-key-1",
            "value": "env.OPENAI_API_KEY",
            "models": ["*"],
            "weight": 1.0
        }
    ],
    "concurrency_and_buffer_size": {
        "concurrency": 100,
        "buffer_size": 500
    }
}'

# Anthropic with conservative settings
curl --location 'http://localhost:8080/api/providers' \
--header 'Content-Type: application/json' \
--data '{
    "provider": "anthropic", 
    "keys": [
        {
            "name": "openai-key-1",
            "value": "env.ANTHROPIC_API_KEY",
            "models": ["*"],
            "weight": 1.0
        }
    ],
    "concurrency_and_buffer_size": {
        "concurrency": 25,
        "buffer_size": 100
    }
}'

Using config.json

{
    "providers": {
        "openai": {
            "keys": [
                {
                    "name": "openai-key-1",
                    "value": "env.OPENAI_API_KEY",
                    "models": ["*"],
                    "weight": 1.0
                }
            ],
            "concurrency_and_buffer_size": {
                "concurrency": 100,
                "buffer_size": 500
            }
        },
        "anthropic": {
            "keys": [
                {
                    "name": "anthropic-key-1",
                    "value": "env.ANTHROPIC_API_KEY",
                    "models": ["*"],
                    "weight": 1.0
                }
            ],
            "concurrency_and_buffer_size": {
                "concurrency": 25,
                "buffer_size": 100
            }
        }
    }
}

Custom Headers

Bifrost supports two ways to add custom headers to provider requests: static headers configured at the provider level, and dynamic headers passed per-request.

Static Headers (Provider Level)

Configure headers that are automatically included in every request to a specific provider. This is useful for provider-specific requirements, API versioning, or organizational metadata.

Using Web UI

1. Navigate to “Model Providers” → “Configurations” → “OpenAI” → “Provider level configuration” → “Network config”
2. Add headers in the “Extra Headers” section
3. Save configuration

Using API

curl --location 'http://localhost:8080/api/providers' \
--header 'Content-Type: application/json' \
--data '{
    "provider": "openai",
    "keys": [
        {
            "name": "openai-key-1",
            "value": "env.OPENAI_API_KEY",
            "models": ["*"],
            "weight": 1.0
        }
    ],
    "network_config": {
        "extra_headers": {
            "x-custom-org": "my-organization",
            "x-environment": "production"
        }
    }
}'

Using config.json

{
    "providers": {
        "openai": {
            "keys": [
                {
                    "name": "openai-key-1",
                    "value": "env.OPENAI_API_KEY",
                    "models": ["*"],
                    "weight": 1.0
                }
            ],
            "network_config": {
                "extra_headers": {
                    "x-custom-org": "my-organization",
                    "x-environment": "production"
                }
            }
        }
    }
}

Dynamic Headers (Per Request)

Send custom headers with individual requests using the x-bf-eh-* prefix. Headers are automatically propagated to the provider after stripping the prefix. This is useful for request-specific metadata, user identification, or custom tracking information.

curl --location 'http://localhost:8080/v1/chat/completions' \
--header 'Content-Type: application/json' \
--header 'x-bf-eh-user-id: user-123' \
--header 'x-bf-eh-tracking-id: trace-456' \
--data '{
    "model": "openai/gpt-4o-mini",
    "messages": [
        {"role": "user", "content": "Hello!"}
    ]
}'

The x-bf-eh- prefix is stripped before forwarding, so x-bf-eh-user-id becomes user-id in the request to the provider.

Example use cases:

• User identification: x-bf-eh-user-id, x-bf-eh-tenant-id
• Request tracking: x-bf-eh-correlation-id, x-bf-eh-trace-id
• Custom metadata: x-bf-eh-department, x-bf-eh-cost-center
• A/B testing: x-bf-eh-experiment-id, x-bf-eh-variant

Security Denylist

Bifrost maintains a security denylist of headers that are never forwarded to providers, regardless of configuration:

denylist := map[string]bool{
    "proxy-authorization": true,
    "cookie":              true,
    "host":                true,
    "content-length":      true,
    "connection":          true,
    "transfer-encoding":   true,

    // prevent auth/key overrides via x-bf-eh-*
    "x-api-key":      true,
    "x-goog-api-key": true,
    "x-bf-api-key":   true,
    "x-bf-vk":        true,
}

This denylist is applied to both static and dynamic headers to prevent security vulnerabilities.

Beta Header Overrides

For Anthropic-family providers (Anthropic, Vertex, Bedrock, Azure), Bifrost maintains a default support matrix for anthropic-beta headers. You can override these defaults per provider when upstream support changes before Bifrost updates its defaults.

Using Web UI

1. Navigate to “Model Providers” → “Configurations” → select your provider → “Beta Headers” tab
2. Each known beta header shows its default support status
3. Use the override dropdown to set Enabled, Disabled, or Default (use built-in defaults)
4. Save configuration

Using API

curl --location --request PUT 'http://localhost:8080/api/providers/vertex' \
--header 'Content-Type: application/json' \
--data '{
    "network_config": {
        "beta_header_overrides": {
            "redact-thinking-": true,
            "fast-mode-": false
        }
    }
}'

Using config.json

{
    "providers": {
        "vertex": {
            "network_config": {
                "beta_header_overrides": {
                    "redact-thinking-": true,
                    "fast-mode-": false
                }
            }
        }
    }
}

Override keys are beta header prefixes (e.g. "redact-thinking-"), not full header values. This ensures overrides apply regardless of date-versioned header bumps. See the full support matrix in the Anthropic provider docs.

Setting Up a Proxy

Route requests through proxies for compliance, security, or geographic requirements. This example shows both HTTP proxy for OpenAI and authenticated SOCKS5 proxy for Anthropic, useful for corporate environments or regional access.

Using Web UI

1. Navigate to “Model Providers” → “Configurations” → → “Provider level configuration” → “Proxy config”
2. Select Proxy Type: HTTP or SOCKS5
3. Set Proxy URL: http://localhost:8000
4. Add credentials if needed (username/password)
5. Save configuration

Using API

# HTTP proxy for OpenAI
curl --location 'http://localhost:8080/api/providers' \
--header 'Content-Type: application/json' \
--data '{
    "provider": "openai",
    "keys": [
        {
            "name": "openai-key-1",
            "value": "env.OPENAI_API_KEY",
            "models": ["*"],
            "weight": 1.0
        }
    ],
    "proxy_config": {
        "type": "http",
        "url": "http://localhost:8000"
    }
}'

# SOCKS5 proxy with authentication for Anthropic
curl --location 'http://localhost:8080/api/providers' \
--header 'Content-Type: application/json' \
--data '{
    "provider": "anthropic",
    "keys": [
        {
            "name": "anthropic-key-1",
            "value": "env.ANTHROPIC_API_KEY",
            "models": ["*"],
            "weight": 1.0
        }
    ],
    "proxy_config": {
        "type": "socks5",
        "url": "http://localhost:8000",
        "username": "user",
        "password": "password"
    }
}'

Using config.json

{
    "providers": {
        "openai": {
            "keys": [
                {
                    "name": "openai-key-1",
                    "value": "env.OPENAI_API_KEY",
                    "models": ["*"],
                    "weight": 1.0
                }
            ],
            "proxy_config": {
                "type": "http",
                "url": "http://localhost:8000"
            }
        },
        "anthropic": {
            "keys": [
                {
                    "name": "anthropic-key-1",
                    "value": "env.ANTHROPIC_API_KEY",
                    "models": ["*"],
                    "weight": 1.0
                }
            ],
            "proxy_config": {
                "type": "socks5",
                "url": "http://localhost:8000",
                "username": "user",
                "password": "password"
            }
        }
    }
}

Send Back Raw Response

Include the original provider response alongside Bifrost’s standardized response format. Useful for debugging and accessing provider-specific metadata.

Using Web UI

1. Navigate to “Model Providers” → “Configurations” → → “Provider level configuration” → “Performance tuning”
2. Toggle “Include Raw Response” to enabled
3. Save configuration

Using API

curl --location 'http://localhost:8080/api/providers' \
--header 'Content-Type: application/json' \
--data '{
    "provider": "openai",
    "keys": [
        {
            "name": "openai-key-1",
            "value": "env.OPENAI_API_KEY",
            "models": ["*"],
            "weight": 1.0
        }
    ],
    "send_back_raw_response": true
}'

Using config.json

{
    "providers": {
        "openai": {
            "keys": [
                {
                    "name": "openai-key-1",
                    "value": "env.OPENAI_API_KEY",
                    "models": ["*"],
                    "weight": 1.0
                }
            ],
            "send_back_raw_response": true
        }
    }
}

When enabled, the raw provider response appears in extra_fields.raw_response:

{
    "choices": [...],
    "usage": {...},
    "extra_fields": {
        "provider": "openai",
        "raw_response": {
            // Original OpenAI response here
        }
    }
}

Send Back Raw Request

Include the original request sent to the provider alongside Bifrost’s response. Useful for debugging request transformations and verifying what was actually sent to the provider.

Using Web UI

1. Navigate to “Model Providers” → “Configurations” → → “Provider level configuration” → “Performance tuning”
2. Toggle “Include Raw Request” to enabled
3. Save configuration

Using API

curl --location 'http://localhost:8080/api/providers' \
--header 'Content-Type: application/json' \
--data '{
    "provider": "openai",
    "keys": [
        {
            "name": "openai-key-1",
            "value": "env.OPENAI_API_KEY",
            "models": ["*"],
            "weight": 1.0
        }
    ],
    "send_back_raw_request": true
}'

Using config.json

{
    "providers": {
        "openai": {
            "keys": [
                {
                    "name": "openai-key-1",
                    "value": "env.OPENAI_API_KEY",
                    "models": ["*"],
                    "weight": 1.0
                }
            ],
            "send_back_raw_request": true
        }
    }
}

When enabled, the raw provider request appears in extra_fields.raw_request:

{
    "choices": [...],
    "usage": {...},
    "extra_fields": {
        "provider": "openai",
        "raw_request": {
            // Original request sent to OpenAI here
        }
    }
}

You can enable both send_back_raw_request and send_back_raw_response together to see the complete request-response cycle for debugging purposes.

Passthrough Extra Parameters

Enable passthrough mode for extra parameters. When enabled, any parameters in the extra_params field (or provider-specific extra parameter fields) will be merged directly into the request sent to the provider, bypassing Bifrost’s parameter filtering.

curl --location 'http://localhost:8080/v1/chat/completions' \
--header 'Content-Type: application/json' \
--header 'x-bf-passthrough-extra-params: true' \
--data '{
    "model": "openai/gpt-4o-mini",
    "messages": [
        {"role": "user", "content": "Hello!"}
    ],
    "extra_params": {
        "custom_param": "value",
        "another_param": 123,
        "nested_param": {
            "nested_key": "nested_value"
        }
    }
}'

When enabled, the extra parameters are merged into the JSON request body sent to the provider. This allows you to pass provider-specific parameters that Bifrost doesn’t natively support.

• This feature only works for JSON requests, not multipart/form-data requests
• Parameters already handled by Bifrost (like addWatermark, enhancePrompt) are not duplicated - they appear in their proper location
• Nested parameters (e.g., parameters.custom_field) are merged recursively with existing nested structures
• See Supported Headers for a complete list of all Bifrost headers

Provider-Specific Authentication

Enterprise cloud providers require additional configuration beyond API keys. Configure Azure, AWS Bedrock, and Google Vertex with platform-specific authentication details.

Azure

Azure supports three authentication methods: Managed Identity (DefaultAzureCredential), Entra ID (Service Principal), and Direct (API Key).

Managed Identity / DefaultAzureCredential

Leave API key and Entra ID credentials empty. Bifrost uses DefaultAzureCredential, which auto-detects managed identity on Azure VMs, App Service, AKS, and similar environments. Provide only endpoint, deployments, and optionally api_version.

Azure Entra ID (Service Principal)

Using Web UI

1. Navigate to “Model Providers” → “Configurations” → “Azure”
2. Leave API Key empty for Service Principal auth
3. Set Client ID: Your Azure Entra ID client ID
4. Set Client Secret: Your Azure Entra ID client secret
5. Set Tenant ID: Your Azure Entra ID tenant ID
6. Set Endpoint: Your Azure endpoint URL
7. Configure Deployments: Map model names to deployment names
8. Set API Version: e.g., 2024-08-01-preview
9. Save configuration

Using API

curl --location 'http://localhost:8080/api/providers' \
--header 'Content-Type: application/json' \
--data '{
    "provider": "azure",
    "keys": [
        {
            "name": "azure-key-1",
            "value": "",
            "models": ["gpt-4o", "gpt-4o-mini"],
            "weight": 1.0,
            "azure_key_config": {
                "endpoint": "env.AZURE_ENDPOINT",
                "client_id": "env.AZURE_CLIENT_ID",
                "client_secret": "env.AZURE_CLIENT_SECRET",
                "tenant_id": "env.AZURE_TENANT_ID",
                "scopes": ["https://cognitiveservices.azure.com/.default"],
                "deployments": {
                    "gpt-4o": "gpt-4o-deployment",
                    "gpt-4o-mini": "gpt-4o-mini-deployment"
                },
                "api_version": "2024-08-01-preview"
            }
        }
    ]
}'

Using config.json

{
    "providers": {
        "azure": {
            "keys": [
                {
                    "name": "azure-key-1",
                    "value": "",
                    "models": ["gpt-4o", "gpt-4o-mini"],
                    "weight": 1.0,
                    "azure_key_config": {
                        "endpoint": "env.AZURE_ENDPOINT",
                        "client_id": "env.AZURE_CLIENT_ID",
                        "client_secret": "env.AZURE_CLIENT_SECRET",
                        "tenant_id": "env.AZURE_TENANT_ID",
                        "scopes": ["https://cognitiveservices.azure.com/.default"],
                        "deployments": {
                            "gpt-4o": "gpt-4o-deployment",
                            "gpt-4o-mini": "gpt-4o-mini-deployment"
                        },
                        "api_version": "2024-08-01-preview"
                    }
                }
            ]
        }
    }
}

Direct Authentication

For simpler use cases, provide the authentication credential directly in the value field:

Using Web UI

1. Navigate to “Model Providers” → “Configurations” → “Azure”
2. Set API Key: Your Azure API key
3. Set Endpoint: Your Azure endpoint URL
4. Configure Deployments: Map model names to deployment names
5. Set API Version: e.g., 2024-08-01-preview
6. Save configuration

Using API

curl --location 'http://localhost:8080/api/providers' \
--header 'Content-Type: application/json' \
--data '{
    "provider": "azure",
    "keys": [
        {
            "name": "azure-key-1",
            "value": "env.AZURE_API_KEY",
            "models": ["gpt-4o", "gpt-4o-mini"],
            "weight": 1.0,
            "azure_key_config": {
                "endpoint": "env.AZURE_ENDPOINT",
                "deployments": {
                    "gpt-4o": "gpt-4o-deployment",
                    "gpt-4o-mini": "gpt-4o-mini-deployment"
                },
                "api_version": "2024-08-01-preview"
            }
        }
    ]
}'

Using config.json

{
    "providers": {
        "azure": {
            "keys": [
                {
                    "name": "azure-key-1",
                    "value": "env.AZURE_API_KEY",
                    "models": ["gpt-4o", "gpt-4o-mini"],
                    "weight": 1.0,
                    "azure_key_config": {
                        "endpoint": "env.AZURE_ENDPOINT",
                        "deployments": {
                            "gpt-4o": "gpt-4o-deployment",
                            "gpt-4o-mini": "gpt-4o-mini-deployment"
                        },
                        "api_version": "2024-08-01-preview"
                    }
                }
            ]
        }
    }
}

Authentication precedence: (1) Entra ID if client_id, client_secret, and tenant_id are set; (2) API key if value is set; (3) DefaultAzureCredential (managed identity) if neither is provided.

AWS Bedrock

AWS Bedrock supports both explicit credentials and IAM role authentication:

Using Web UI

1. Navigate to “Model Providers” → “Configurations” → “AWS Bedrock”
2. Set API Key: AWS API Key (or leave empty if using IAM role authentication)
3. Set Access Key: AWS Access Key ID (or leave empty to use IAM in environment)
4. Set Secret Key: AWS Secret Access Key (or leave empty to use IAM in environment)
5. Set Region: e.g., us-east-1
6. Configure Deployments: Map model names to inference profiles
7. Set ARN: Required for deployments mapping
8. Save configuration

Using API

curl --location 'http://localhost:8080/api/providers' \
--header 'Content-Type: application/json' \
--data '{
    "provider": "bedrock",
    "keys": [
        {
            "name": "bedrock-key-1",
            "models": ["anthropic.claude-3-sonnet-20240229-v1:0", "anthropic.claude-v2:1"],
            "weight": 1.0,
            "bedrock_key_config": {
                "access_key": "env.AWS_ACCESS_KEY_ID",
                "secret_key": "env.AWS_SECRET_ACCESS_KEY",
                "session_token": "env.AWS_SESSION_TOKEN",
                "region": "us-east-1",
                "deployments": {
                    "claude-3-sonnet": "us.anthropic.claude-3-sonnet-20240229-v1:0"
                },
                "arn": "arn:aws:bedrock:us-east-1:123456789012:inference-profile"
            }
        }
    ]
}'

Using config.json

{
    "providers": {
        "bedrock": {
            "keys": [
                {
                    "name": "bedrock-key-1",
                    "models": ["anthropic.claude-3-sonnet-20240229-v1:0", "anthropic.claude-v2:1"],
                    "weight": 1.0,
                    "bedrock_key_config": {
                        "access_key": "env.AWS_ACCESS_KEY_ID",
                        "secret_key": "env.AWS_SECRET_ACCESS_KEY",
                        "session_token": "env.AWS_SESSION_TOKEN",
                        "region": "us-east-1",
                        "deployments": {
                            "claude-3-sonnet": "us.anthropic.claude-3-sonnet-20240229-v1:0"
                        },
                        "arn": "arn:aws:bedrock:us-east-1:123456789012:inference-profile"
                    }
                }
            ]
        }
    }
}

Notes:

• If using API Key authentication, set value field to the API key, else leave it empty for IAM role authentication.
• In IAM role authentication, if both access_key and secret_key are empty, Bifrost uses IAM role authentication from the environment.
• arn is required for URL formation - deployments mapping is ignored without it.
• When using arn + deployments, Bifrost uses model profiles; otherwise forms path with incoming model name directly.
• ARN vs deployments: Put the ARN prefix in arn and the model/inference profile resource ID only in deployments — never the full ARN in deployments. See How to Use ARNs and Application Inference Profiles for details.

Google Vertex

Google Vertex requires project configuration and authentication credentials:

Using Web UI

1. Navigate to “Model Providers” → “Configurations” → “Google Vertex”
2. Set API Key: Your Vertex API key
3. Set Project ID: Your Google Cloud project ID
4. Set Region: e.g., us-central1
5. Set Auth Credentials: Service account credentials JSON
6. Save configuration

Using API

curl --location 'http://localhost:8080/api/providers' \
--header 'Content-Type: application/json' \
--data '{
    "provider": "vertex",
    "keys": [
        {
            "name": "vertex-key-1",
            "value": "env.VERTEX_API_KEY",
            "models": ["gemini-pro", "gemini-pro-vision"],
            "weight": 1.0,
            "vertex_key_config": {
                "project_id": "env.VERTEX_PROJECT_ID",
                "region": "us-central1",
                "auth_credentials": "env.VERTEX_CREDENTIALS",
                "deployments": {
                    "fine-tuned-gemini-2.5-pro": "123456789"
                }
            }
        }
    ]
}'

Using config.json

{
    "providers": {
        "vertex": {
            "keys": [
                {
                    "name": "vertex-key-1",
                    "value": "env.VERTEX_API_KEY",
                    "models": ["gemini-pro", "gemini-pro-vision"],
                    "weight": 1.0,
                    "vertex_key_config": {
                        "project_id": "env.VERTEX_PROJECT_ID",
                        "region": "us-central1",
                        "auth_credentials": "env.VERTEX_CREDENTIALS",
                        "deployments": {
                            "fine-tuned-gemini-2.5-pro": "123456789"
                        }
                    }
                }
            ]
        }
    }
}

Notes:

• You can leave both API Key and Auth Credentials empty to use service account authentication from the environment.
• You must set Project Number in Key config if using fine-tuned models.
• API Key Authentication is only supported for Gemini and fine-tuned models.
• You can use custom fine-tuned models by passing vertex/<your-fine-tuned-model-id> or vertex/<model-deployment-alias> if you have set the deployments in the key config.

Vertex AI support for fine-tuned models is currently in beta. Requests to non-Gemini fine-tuned models may fail, so please test and report any issues.

Next Steps

Now that you understand provider configuration, explore these related topics:

Essential Topics

• Streaming Responses - Real-time response generation
• Tool Calling - Enable AI to use external functions
• Multimodal AI - Process images, audio, and text
• Integrations - Drop-in compatibility with existing SDKs

Advanced Topics

• Core Features - Advanced Bifrost capabilities
• Architecture - How Bifrost works internally
• Deployment - Production setup and scaling