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Discover Available Gateways

Redpanda Agentic Data Plane is supported on BYOC clusters running with AWS and Redpanda version 25.3 and later. It is currently in a limited availability release.

As a builder, you need to know which gateways are available to you before integrating your agent or application. This page shows you how to discover accessible gateways, understand their configurations, and verify connectivity.

After reading this page, you will be able to:

  • List all AI Gateways you have access to and retrieve their endpoints and IDs

  • View which models and MCP tools are available through each gateway

  • Test gateway connectivity before integration

Before you begin

  • You have a Redpanda Cloud account with access to at least one AI Gateway

  • You have access to the Redpanda Cloud Console or API credentials

List your accessible gateways

  • Using the Console

  • Using the API

  1. Navigate to Gateways in the Redpanda Cloud Console.

  2. Review the list of gateways you can access. For each gateway, you’ll see the gateway name, ID, endpoint URL, status, available models, and provider performance.

    Click the Configuration, API, MCP Tools, and Changelog tabs for additional information.

To list gateways programmatically:

curl https://api.redpanda.com/v1/gateways \
  -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}"

Response:

{
  "gateways": [
    {
      "id": "gw_abc123",
      "name": "production-gateway",
      "mode": "ai_hub",
      "endpoint": "https://gw.ai.panda.com",
      "status": "active",
      "workspace_id": "ws_xyz789",
      "created_at": "2025-01-15T10:30:00Z"
    },
    {
      "id": "gw_def456",
      "name": "staging-gateway",
      "mode": "custom",
      "endpoint": "https://gw-staging.ai.panda.com",
      "status": "active",
      "workspace_id": "ws_xyz789",
      "created_at": "2025-01-10T08:15:00Z"
    }
  ]
}

Understand gateway information

Each gateway provides specific information you’ll need for integration:

Gateway endpoint

The gateway endpoint is the URL where you send all API requests. It replaces direct provider URLs (like api.openai.com or api.anthropic.com). The gateway ID is embedded directly in the endpoint URL.

Example:

https://example/gateways/gw_abc123/v1

Your application configures this as the base_url in your SDK client.

Available models

Each gateway exposes specific models based on administrator configuration. Models use the vendor/model_id format:

  • openai/gpt-5.2

  • anthropic/claude-sonnet-4.5

  • openai/gpt-5.2-mini

To see which models are available through a specific gateway:

curl ${GATEWAY_ENDPOINT}/models \
  -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}"

Response:

{
  "object": "list",
  "data": [
    {
      "id": "openai/gpt-5.2",
      "object": "model",
      "owned_by": "openai"
    },
    {
      "id": "anthropic/claude-sonnet-4.5",
      "object": "model",
      "owned_by": "anthropic"
    },
    {
      "id": "openai/gpt-5.2-mini",
      "object": "model",
      "owned_by": "openai"
    }
  ]
}

Rate limits and quotas

Each gateway may have configured rate limits and monthly budgets. Check the console or contact your administrator to understand:

  • Requests per minute/hour/day

  • Monthly spend limits

  • Token usage quotas

These limits help control costs and ensure fair resource allocation across teams.

MCP Tools

If Model Context Protocol (MCP) aggregation is enabled for your gateway, you can access tools from multiple MCP servers through a single endpoint.

To discover available MCP tools:

curl ${GATEWAY_ENDPOINT}/mcp/tools \
  -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \
  -H "rp-aigw-mcp-deferred: true"

With deferred loading enabled, you’ll receive search and orchestrator tools initially. You can then query for specific tools as needed.

Check gateway availability

Before integrating your application, verify that you can successfully connect to the gateway:

Test connectivity

curl ${GATEWAY_ENDPOINT}/models \
  -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \
  -v

Expected result: HTTP 200 response with a list of available models.

Test a simple request

Send a minimal chat completion request to verify end-to-end functionality:

curl ${GATEWAY_ENDPOINT}/chat/completions \
  -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-5.2-mini",
    "messages": [{"role": "user", "content": "Hello"}],
    "max_tokens": 10
  }'

Expected result: HTTP 200 response with a completion.

Troubleshoot connectivity issues

If you cannot connect to a gateway:

  1. Verify authentication: Ensure your API token is valid and has not expired

  2. Check gateway endpoint: Confirm the endpoint URL includes the correct gateway ID

  3. Verify endpoint URL: Check for typos in the gateway endpoint

  4. Check permissions: Confirm with your administrator that you have access to this gateway

  5. Review network connectivity: Ensure your network allows outbound HTTPS connections

Choose the right gateway

If you have access to multiple gateways, consider which one to use based on your needs:

By environment

Organizations often create separate gateways for different environments:

  • Production gateway: Higher rate limits, access to all models, monitoring enabled

  • Staging gateway: Lower rate limits, restricted models, aggressive cost controls

  • Development gateway: Minimal limits, all models for experimentation

Choose the gateway that matches your deployment environment.

By team or project

Gateways may be organized by team or project for cost tracking and isolation:

  • team-ml-gateway: For machine learning team

  • team-product-gateway: For product team

  • customer-facing-gateway: For production customer workloads

Use the gateway designated for your team to ensure proper cost attribution.

By capability

Different gateways may have different features enabled:

  • Gateway with MCP tools: Use if your agent needs to call tools

  • Gateway without MCP: Use for simple LLM completions

  • Gateway with specific models: Use if you need access to particular models

Example: Complete discovery workflow

Here’s a complete workflow to discover and validate gateway access:

#!/bin/bash

# Set your API token
export REDPANDA_CLOUD_TOKEN="your-token-here"

# Step 1: List all accessible gateways
echo "=== Discovering gateways ==="
curl -s https://api.redpanda.com/v1/gateways \
  -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \
  | jq '.gateways[] | {name: .name, id: .id, endpoint: .endpoint}'

# Step 2: Select a gateway (example)
export GATEWAY_ENDPOINT="https://example/gateways/gw_abc123/v1"

# Step 3: List available models
echo -e "\n=== Available models ==="
curl -s ${GATEWAY_ENDPOINT}/models \
  -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \
  | jq '.data[] | .id'

# Step 4: Test with a simple request
echo -e "\n=== Testing request ==="
curl -s ${GATEWAY_ENDPOINT}/chat/completions \
  -H "Authorization: Bearer ${REDPANDA_CLOUD_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-5.2-mini",
    "messages": [{"role": "user", "content": "Say hello"}],
    "max_tokens": 10
  }' \
  | jq '.choices[0].message.content'

echo -e "\n=== Gateway validated successfully ==="

Next steps