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Deploying a prompt makes it available to your AI applications via the Adaline API or SDK. You can deploy directly from the Editor or from any deployment environment — and promote prompts across environments as they progress through your pipeline.

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Deploy from the Editor

When you are ready to ship a prompt you’ve been working on in the Editor, click the Deploy button: Deploying a prompt from the Editor This redirects you to the deployment environment view, where you can review the changes and confirm the deployment.

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Deploy from an environment

The deployment environment view is the central space for managing deployments. It is divided into three zones: Deployment environment view in Adaline
The banner at the bottom shows the deployment direction (e.g., Editor -> Production). This is a visual indicator of what you are about to deploy — it is not clickable.
ZoneWhat it shows
LeftDeployment history — all previously deployed prompt versions across environments. From here you can rollback to any previous version.
CenterThe diff view — blue-highlighted content shows what will change when you deploy. This includes changes to config (model and settings), messages, role, modality (text, image, PDF), and tools.
RightEnvironment selector, webhooks, and API/SDK integration code for the deployed prompt.

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Confirm the deployment

When you are ready, click Deploy to push the prompt to the selected environment: Confirming a deployment in Adaline After deployment completes, Adaline shows the deployed state: Completed deployment in Adaline The right panel now displays:
  • Integration IDs — Deployment snapshot ID, environment ID, and prompt bench ID for use in your API/SDK calls. Adaline auto-fills the code snippets in the API Integration section.
  • Webhook executions — Status of any webhook deliveries triggered by the deployment.

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Deploy to another environment

To promote a prompt from one environment to another (e.g., staging to production), select the prompt and click Deploy to…: Deploying to another environment This lets you move prompts forward or backward through your deployment pipeline. For example:
  • Staging -> Production — Promote a tested prompt to your live application.
  • Production -> Staging — Pull a production prompt back to staging for further testing or iteration.
Before deploying to production, compare the deployments between environments to review exactly what will change.

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Access deployed prompts

After deployment, your AI application can access the prompt via:
  • API — Use the Adaline REST API with your environment key and deployment IDs.
  • SDK — Use the TypeScript or Python SDK to fetch and execute deployed prompts.
  • Webhooks — Configure webhooks to receive real-time notifications whenever a deployment is created or rolled back.

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What gets deployed

A deployment captures a complete snapshot of your prompt, including:
ComponentDescription
ConfigThe selected model and all its parameter settings (temperature, max tokens, response format, etc.).
MessagesAll message templates — system, user, assistant, and tool messages.
VariablesAll variable definitions and their configured sources.
ToolsTool definitions, schemas, and request configurations.
MCP serversAny connected MCP server configurations.
Here is a sample deployment object returned by the API and SDK: 
{
  "id": "deploy_abc123",
  "createdAt": 1704067200000,
  "updatedAt": 1704153600000,
  "projectId": "proj_789",
  "promptId": "prompt_abc123",
  "deploymentEnvironmentId": "env_production",
  "prompt": {
    "config": {
      "providerName": "openai",
      "providerId": "provider_abc123",
      "model": "gpt-4o",
      "settings": {
        "temperature": 0.7,
        "maxTokens": 1000,
        "topP": 0.9
      }
    },
    "messages": [
      {
        "role": "system",
        "content": [
          {
            "modality": "text",
            "value": "You are a helpful customer support agent for {{company_name}}. Answer questions about our products and services."
          }
        ]
      },
      {
        "role": "user",
        "content": [
          {
            "modality": "text",
            "value": "{{user_message}}"
          }
        ]
      }
    ],
    "tools": [
      {
        "type": "function",
        "definition": {
          "schema": {
            "name": "search_knowledge_base",
            "description": "Search the company knowledge base for relevant articles",
            "parameters": {
              "type": "object",
              "properties": {
                "query": {
                  "type": "string",
                  "description": "The search query"
                }
              },
              "required": ["query"]
            }
          }
        }
      }
    ],
    "variables": [
      { "name": "company_name", "modality": "text" },
      { "name": "user_message", "modality": "text" }
    ]
  }
}

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Next steps

Rollback Your Prompt

Revert to a previous deployment instantly.

Compare Your Deployments

Review diffs between deployment versions.