Open WebUI

Cost tracking, observability, and more for Open WebUI

This guide will help you implement enterprise-grade security, observability, and governance for OpenWebUI using Portkey. While OpenWebUI supports various provider plugins, Portkey provides a unified interface for all your LLM providers, offering comprehensive features for model management, cost tracking, observability, and metadata logging.

For IT administrators deploying centralized instances of OpenWebUI, Portkey enables essential enterprise features including usage tracking, access controls, and budget management. Let’s walk through implementing these features step by step.

Understanding the Implementation

When implementing Portkey with OpenWebUI in your organization, we’ll follow these key steps:

1. Basic OpenWebUI integration with Portkey
2. Setting up organizational governance using Model Catlog
3. Managing user access and permissions, setting- budget & rate limits, and more…

If you’re an individual user just looking to use Portkey with Open WebUI, you only need to complete Steps 1 and 2 to get started.

1. Setting Up Portkey

1. Create Portkey API Key

   1. Go to the API Keys section in the Portkey sidebar.
   2. Click Create New API Key with all the permissions.
   3. Save and copy the key — you’ll need it for OpenWebUI.
2. Add Your Provider

   1. Navigate to Model Catalog → AI Providers.

   

   2. Click Create Provider (if this is your first time using Portkey).
   3. Select Create New Integration → choose your AI service (OpenAI, Anthropic, etc.).
   4. Enter your provider’s API key and required details.
   5. (Optional) Configure workspace and model provisioning.
   6. Click Create Integration.
3. Get Your Model Slugs

   1. Go to Model Catalog → Models.
   2. Copy the slug for each model you want to use (@provider-slug/model-name).

   

   Example: @openai-test/gpt-4o — use this in the model field of API requests.

That’s it — your Portkey setup is ready. Now let’s integrate it with OpenWebUI.

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2. Integrating Portkey with OpenWebUI

Since Portkey is OpenAI API–compatible, connecting it to OpenWebUI is quick and straightforward.

You’ll need your Portkey API key from Step 1 before continuing.

1. Access Admin Panel

   1. Start your OpenWebUI server.
   2. Click on your username at the bottom left.
   3. Go to the Admin Panel -> Settings tab -> Select Connections from the sidebar.

   
2. Enable Direct Connections

   1. Turn ON Direct Connections and OpenAI API toggle switch.
   2. Click the + icon next to Manage OpenAI API Connections.
3. Configure Portkey Connection

   Fill in the following in the Edit Connection dialog:

   • URL: https://api.portkey.ai/v1
   • Key: Your Portkey API key from Step 1
   • Prefix ID: portkey (or any name you prefer)
   • Model IDs: Your model slugs (e.g., @openai/gpt-4o, @anthropic/claude-3-sonnet) from Step 1

   

   Click Save to finish.

4. Select and Use Your Model

   1. Go back to the main chat interface and select Portkey model (format: @model-name) from the dropdown at the top.
   2. Start chatting!

   For Anthropic models: You must set a Max Tokens value. Click the settings icon (top right) and set Max Tokens to a valid number (e.g., 1024).

You can track requests, usage, and costs in the Portkey Dashboard.

3. Set Up Enterprise Governance for Open WebUI

Why Enterprise Governance? If you are using Open WebUI inside your orgnaization, you need to consider several governance aspects:

• Cost Management: Controlling and tracking AI spending across teams
• Access Control: Managing team access and workspaces
• Usage Analytics: Understanding how AI is being used across the organization
• Security & Compliance: Maintaining enterprise security standards
• Reliability: Ensuring consistent service across all users
• Model Management: Managing what models are being used in your setup

Portkey adds a comprehensive governance layer to address these enterprise

Enterprise Implementation Guide

Step 1: Implement Budget Controls & Rate Limits

Step 1: Implement Budget Controls & Rate Limits

Model Catalog enables you to have granular control over LLM access at the team/department level. This helps you:

• Set up budget limits
• Prevent unexpected usage spikes using Rate limits
• Track departmental spending

Setting Up Department-Specific Controls:

1. Navigate to Model Catalog in Portkey dashboard
2. Create new Provider for each engineering team with budget limits and rate limits
3. Configure department-specific limits

Step 2: Define Model Access Rules

Step 2: Define Model Access Rules

As your AI usage scales, controlling which teams can access specific models becomes crucial. You can simply manage AI models in your org by provisioning model at the top integration level.

Step 4: Set Routing Configuration

Portkey allows you to control your routing logic very simply with it’s Configs feature. Portkey Configs provide this control layer with things like:

• Data Protection: Implement guardrails for sensitive code and data
• Reliability Controls: Add fallbacks, load-balance, retry and smart conditional routing logic
• Caching: Implement Simple and Semantic Caching. and more…

Example Configuration:

Here’s a basic configuration to load-balance requests to OpenAI and Anthropic:

{
  "strategy": {
    "mode": "load-balance"
  },
  "targets": [
    {
      "override_params": {
        "model": "@YOUR_OPENAI_PROVIDER_SLUG/gpt-model"
      }
    },
    {
      "override_params": {
        "model": "@YOUR_ANTHROPIC_PROVIDER/claude-sonnet-model"
      }
    }
  ]
}

Create your config on the Configs page in your Portkey dashboard. You’ll need the config ID for connecting to Open WebUI’s setup.

Configs can be updated anytime to adjust controls without affecting running applications.

Step 4: Implement Access Controls

Step 3: Implement Access Controls

Create User-specific API keys that automatically:

• Track usage per developer/team with the help of metadata
• Apply appropriate configs to route requests
• Collect relevant metadata to filter logs
• Enforce access permissions

Create API keys through:

• Portkey App
• API Key Management API

Example using Python SDK:

from portkey_ai import Portkey

portkey = Portkey(api_key="YOUR_ADMIN_API_KEY")

api_key = portkey.api_keys.create(
    name="frontend-engineering",
    type="organisation",
    workspace_id="YOUR_WORKSPACE_ID",
    defaults={
        "config_id": "your-config-id",
        "metadata": {
            "environment": "development",
            "department": "engineering",
            "team": "frontend"
        }
    },
    scopes=["logs.view", "configs.read"]
)

For detailed key management instructions, see our API Keys documentation.

Step 5: Deploy & Monitor

Step 4: Deploy & Monitor After distributing API keys to your engineering

teams, your enterprise-ready Open WebUI setup is ready to go. Each developer can now use their designated API keys with appropriate access levels and budget controls. Apply your governance setup using the integration steps from earlier sections Monitor usage in Portkey dashboard: - Cost tracking by engineering team - Model usage patterns for AI agent tasks - Request volumes - Error rates and debugging logs

Enterprise Features Now Available

Open WebUI now has:

• Per-developer budget controls
• Model access governance
• Usage tracking & attribution
• Code security guardrails
• Reliability features for development workflows

Here’s the new section for image generation using Portkey with Open WebUI:

4. Image Generation with Portkey

Portkey enables seamless image generation through Open WebUI by providing a unified interface for various image generation models like DALL-E 2, DALL-E 3, and other compatible models. This integration allows you to leverage Portkey’s enterprise features including cost tracking, access controls, and observability for all your image generation needs.

Setting Up Image Generation

Before proceeding, ensure you have completed the basic Portkey setup from Step 1 and have your Portkey API key ready.

1. Access Image Settings

   1. Navigate to your Open WebUI Admin Panel
   2. Go to Settings → Images from the sidebar

   
2. Configure Image Generation Engine

   In the Image Settings page, configure the following:

   1. Enable Image Generation: Toggle ON the Image Generation (Experimental) option
   2. Image Generation Engine: Select Default (Open AI) from the dropdown
   3. OpenAI API Config: Enter Portkey’s base URL:
      https://api.portkey.ai/v1
      

   4. API Key: Enter your Portkey API key (from Step 1)
   5. Set Default Model: Enter your model slug in the format:
      @provider-key/model-name
      

      For example: @openai-test/dall-e-3
3. Configure Model-Specific Settings

   Choose the model you wish to use. Note that image size options will depend on the selected model:

   • DALL·E 2: Supports 256x256, 512x512, or 1024x1024 images.
   • DALL·E 3: Supports 1024x1024, 1792x1024, or 1024x1792 images.
   • GPT-Image-1: Supports auto, 1024x1024, 1536x1024, or 1024x1536 images.

   Steps: Set the number of generation steps (typically 50 for good quality)

   • Other Models: Check your provider’s documentation (gemini, vertex, and more..) for supported sizes
4. Test Your Configuration

   1. Return to the main chat interface
   2. Type a prompt and click the image generation icon
   3. Your image will be generated using Portkey’s infrastructure
   4. Track usage and costs in the Portkey Dashboard

Monitoring Image Generation

All image generation requests through Portkey are automatically tracked with:

• Cost Attribution: See exact costs per image generation
• Request Logs: Full prompt and response tracking
• Performance Metrics: Generation time and success rates
• Metadata Tags: Track image generation by team/department

Pro Tip: If you are using a different AI provider (Gemini, Vertex AI, etc..) and you need to pass some additional params for image gen, you can do that by adding them in portkey’s config as override_params and attaching it to your Portkey API key. Here’s a guide

Portkey Features

Now that you have enterprise-grade Zed setup, let’s explore the comprehensive features Portkey provides to ensure secure, efficient, and cost-effective AI operations.

1. Comprehensive Metrics

Using Portkey you can track 40+ key metrics including cost, token usage, response time, and performance across all your LLM providers in real time. You can also filter these metrics based on custom metadata that you can set in your configs. Learn more about metadata here.

2. Advanced Logs

Portkey’s logging dashboard provides detailed logs for every request made to your LLMs. These logs include:

• Complete request and response tracking
• Metadata tags for filtering
• Cost attribution and much more…

3. Unified Access to 1600+ LLMs

You can easily switch between 1600+ LLMs. Call various LLMs such as Anthropic, Gemini, Mistral, Azure OpenAI, Google Vertex AI, AWS Bedrock, and many more by simply changing the model slug in your default config object.

4. Advanced Metadata Tracking

Using Portkey, you can add custom metadata to your LLM requests for detailed tracking and analytics. Use metadata tags to filter logs, track usage, and attribute costs across departments and teams.

Custom Metata

5. Enterprise Access Management

Budget Controls

Set and manage spending limits across teams and departments. Control costs with granular budget limits and usage tracking.

Single Sign-On (SSO)

Enterprise-grade SSO integration with support for SAML 2.0, Okta, Azure AD, and custom providers for secure authentication.

Organization Management

Hierarchical organization structure with workspaces, teams, and role-based access control for enterprise-scale deployments.

Access Rules & Audit Logs

Comprehensive access control rules and detailed audit logging for security compliance and usage tracking.

6. Reliability Features

Fallbacks

Automatically switch to backup targets if the primary target fails.

Conditional Routing

Route requests to different targets based on specified conditions.

Load Balancing

Distribute requests across multiple targets based on defined weights.

Caching

Enable caching of responses to improve performance and reduce costs.

Smart Retries

Automatic retry handling with exponential backoff for failed requests

Budget Limits

Set and manage budget limits across teams and departments. Control costs with granular budget limits and usage tracking.

7. Advanced Guardrails

Protect your Project’s data and enhance reliability with real-time checks on LLM inputs and outputs. Leverage guardrails to:

• Prevent sensitive data leaks
• Enforce compliance with organizational policies
• PII detection and masking
• Content filtering
• Custom security rules
• Data compliance checks

Guardrails

Implement real-time protection for your LLM interactions with automatic detection and filtering of sensitive content, PII, and custom security rules. Enable comprehensive data protection while maintaining compliance with organizational policies.

FAQs

Can I use multiple LLM providers with the same API key?

Yes! You can create multiple Integrations (one for each provider) and attach them to a single config. This config can then be connected to your API key, allowing you to use multiple providers through a single API key.

How do I track costs for different teams?

Portkey provides several ways to track team costs: - Create separate Integrations for each team - Use metadata tags in your configs - Set up team-specific API keys - Monitor usage in the analytics dashboard

What happens if a team exceeds their budget limit?

When a team reaches their budget limit: 1. Further requests will be blocked 2. Team admins receive notifications 3. Usage statistics remain available in dashboard 4. Limits can be adjusted if needed

Next Steps

Join our Community

• Discord Community
• GitHub Repository

For enterprise support and custom features, contact our enterprise team.