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Documentation Index

Fetch the complete documentation index at: https://docs.stagewise.io/llms.txt

Use this file to discover all available pages before exploring further.

Custom endpoints let you route inference requests to any service that implements a supported API specification. This covers self-hosted models, enterprise proxy servers, and cloud platforms like Azure OpenAI or AWS Bedrock.

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Creating a custom endpoint

1

Open Models & Providers

Navigate to Settings → Agent → Models & Providers and scroll to the Models section.
2

Open Custom Providers Settings

Click Custom Providers in the top-right corner of the Models section.
3

Add Provider

Click Add Provider in the bottom right corner of the list.
4

Set name

Under** Name**, enter the displayed name of the provider.
5

Set provider type

Under Provider Type, select the API spec that your provider uses.
Unless the Provider specifically mentioned a certain API spec, you most likely want to use OpenAI (Chat Completions) as it’s the de facto standard for now.
6

Set Base URL

Under** Base URL**, set the URL of the provider endpoint.
7

Set custom model mapping

This may be required for some providers. See Model ID Mapping.

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Supported API specifications

SpecDescriptionExample services
anthropicAnthropic Messages APISelf-hosted Claude, any Anthropic-compatible proxy
openai-chat-completionsOpenAI Chat CompletionsvLLM, Ollama, LiteLLM, Together AI
openai-responsesOpenAI Responses APIDirect OpenAI-compatible responses endpoints
googleGoogle Generative AISelf-hosted Gemini-compatible services
azureAzure OpenAIAzure OpenAI Service
amazon-bedrockAWS BedrockAmazon Bedrock (requires region + secret key)
google-vertexGoogle Vertex AIVertex AI (requires project ID + location + credentials)

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Cloud provider configuration

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Azure OpenAI

Azure endpoints require additional fields:
  • Resource name — Your Azure OpenAI resource name
  • API version — The API version string (e.g. 2024-02-01)

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Amazon Bedrock

Bedrock endpoints require:
  • Region — AWS region (e.g. us-east-1)
  • API key — AWS Access Key ID
  • Secret key — AWS Secret Access Key

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Google Vertex AI

Vertex endpoints require:
  • Project ID — Your GCP project ID
  • Location — GCP region (e.g. us-central1)
  • Google credentials — Service account JSON credentials

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Model ID mapping

Some custom endpoints use different model identifiers than the official APIs. Use the Model ID Mapping field to remap built-in model IDs to the IDs your endpoint expects. For example, if your Ollama instance names Claude as claude-v2: 
{
  "claude-sonnet-4-6": "claude-v2"
}

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Using a custom endpoint

After creating the endpoint, go to the provider configuration for any provider and select Custom endpoint mode. Choose your endpoint from the dropdown. All models for that provider will route through the custom endpoint.