> ## Documentation Index
> Fetch the complete documentation index at: https://docs.portkey.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Retool
> Connect Portkey as a Custom AI Provider in Retool to access 1600+ LLMs with observability and governance
Retool is a low-code platform for building internal apps and automations. Use Portkey as a Custom AI Provider in Retool to route requests through 1600+ LLMs, add observability, and enforce governance controls — all without changing your Retool queries.
## Prerequisites
* [Portkey API key](https://app.portkey.ai/api-keys)
* Provider configured in [Model Catalog](https://app.portkey.ai/model-catalog)
* Retool account with resource creation permissions
See [Retool's Custom AI Provider docs](https://docs.retool.com/data-sources/guides/connect/custom-ai-provider/resource) for more on resource setup.
## Setup
1. Go to [Model Catalog](https://app.portkey.ai/model-catalog) → **Add Provider**.
2. Select your provider (OpenAI, Anthropic, etc.), enter your API key, and create a slug like `openai-prod`.
3. Go to [API Keys](https://app.portkey.ai/api-keys) → **Create new key** → Save.
1. In Retool, go to **Resources** → **Create new** → **Resource**.
2. Search for **Custom AI Provider** and select it.
3. Give the resource a name (e.g., `Portkey`).
Fill in the following fields:
| Field | Value |
| --------------------- | ----------------------------------------------------------------------------------------------- |
| **Base URL** | `https://api.portkey.ai/v1` |
| **API Key** | Your Portkey API key |
| **Compatible Schema** | OpenAI |
| **Model Names** | Use Portkey Model Catalog slugs (e.g., `@openai-prod/gpt-5`, `@anthropic-prod/claude-opus-4-7`) |
Portkey model slugs follow the format `@/`, where the provider slug is the one you created in Model Catalog. This routes each request to the right provider and model automatically — no extra headers needed.
Click **Create resource**. To verify, create a query in your app or workflow, select the Portkey resource, choose **Generate chat response**, and run it.
Custom AI Provider resources don't have a **Test connection** button. Verify by running a query.
## Use Portkey in queries
In any Retool app or workflow, create a new query and select your Portkey resource. Choose an action type:
| Action | Description |
| ---------------------------- | -------------------------------------------------- |
| **Generate text** | Content generation, code generation, summarization |
| **Generate chat response** | Multi-turn conversations with message history |
| **Summarize text** | Condense documents or feedback |
| **Classify text** | Categorize inputs into predefined labels |
| **Extract entity from text** | Pull structured data from unstructured text |
### Example: Chat query
Set up a query with **Generate chat response** and use dynamic Retool expressions in your prompt:
```
System message:
You are a helpful assistant for our internal support team.
Prompt:
Analyze this customer feedback: {{ feedbackTextarea.value }}
```
## Advanced headers
Add these as custom headers on the resource for more control:
| Header | Value | Purpose |
| -------------------- | -------------------------------------- | ---------------------------------- |
| `x-portkey-config` | Your Config ID | Enable fallbacks, retries, caching |
| `x-portkey-metadata` | `{"_user": "{{ currentUser.email }}"}` | Tag requests with user info |
## What you get with Portkey
* **1600+ LLMs** — Switch between OpenAI, Anthropic, Google, Mistral, and more from the same Retool resource
* **Observability** — Every query logged with latency, cost, and token usage in the [Portkey Dashboard](https://app.portkey.ai/dashboard)
* **Reliability** — Automatic retries and fallbacks keep your Retool apps running
* **Governance** — Budget limits, rate limits, and RBAC per team or workspace
* **Guardrails** — PII detection and content filtering on requests and responses
***
# 3. Set Up Enterprise Governance
**Why Enterprise Governance?**
* **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 needs.
**Enterprise Implementation Guide**
### 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](/product/model-catalog/budget-limits)
* Prevent unexpected usage spikes using Rate limits
* Track departmental spending
#### Setting Up Department-Specific Controls:
1. Navigate to [Model Catalog](https://app.portkey.ai/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
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.
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:
```json theme={"system"}
{
"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](https://app.portkey.ai/configs) in your Portkey dashboard. You'll need the config ID for connecting.
Configs can be updated anytime to adjust controls without affecting running applications.
### 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](https://app.portkey.ai/)
* [API Key Management API](/api-reference/admin-api/control-plane/api-keys/create-api-key)
Example using Python SDK:
```python theme={"system"}
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](/api-reference/admin-api/control-plane/api-keys/create-api-key).
### Step 4: Deploy & Monitor
After distributing API keys to your engineering teams, your enterprise-ready 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
**You now have:**
* Departmental budget controls
* Model access governance
* Usage tracking & attribution
* Security guardrails
* Reliability features
# Portkey Features
Now that you have an enterprise-grade 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 `provider` 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.
### 5. Enterprise Access Management
Set and manage spending limits across teams and departments. Control costs with granular budget limits and usage tracking.
Enterprise-grade SSO integration with support for SAML 2.0, Okta, Azure AD, and custom providers for secure authentication.
Hierarchical organization structure with workspaces, teams, and role-based access control for enterprise-scale deployments.
Comprehensive access control rules and detailed audit logging for security compliance and usage tracking.
### 6. Reliability Features
Automatically switch to backup targets if the primary target fails.
Route requests to different targets based on specified conditions.
Distribute requests across multiple targets based on defined weights.
Enable caching of responses to improve performance and reduce costs.
Automatic retry handling with exponential backoff for failed requests
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
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
Update AI Provider limits at any time from [Model Catalog](https://app.portkey.ai/model-catalog): 1. Open the provider you want to modify. 2. Update the budget or rate limits. 3. Save your changes.
Yes! Add multiple AI Providers to Model Catalog (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.
Portkey provides several ways to track team costs:
* Create separate AI Providers for each team
* Use metadata tags in your configs
* Set up team-specific API keys
* Monitor usage in the analytics dashboard
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](https://portkey.sh/discord-report)
* [GitHub Repository](https://github.com/Portkey-AI)
For enterprise support and custom features, contact our [enterprise team](https://calendly.com/portkey-ai).