Isolated testing

Run Radium alongside your existing provider without touching existing config

Isolated testing

You don’t have to migrate everything at once. Run Radium in parallel with your current provider to compare output quality, latency and cost before changing production code.

Goal

Send 5-10% of your traffic to Radium while the rest stays on your existing provider. No code changes to existing services required.

Prerequisites

• A Radium API key from the dashboard
• Your current provider still working as-is

Strategy 1: Environment-variable switching

The quickest way to test is route by changing which key your code reads.

# Existing setup
export OPENAI_API_KEY="sk-..."

# Test run — swap the base URL and key, keep everything else identical
export RADIUM_API_KEY="YOUR_RADIUM_API_KEY"
export BASE_URL="https://api.radium.cloud/v1"

In your code, read the base URL from env:

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["RADIUM_API_KEY"],
    base_url=os.environ.get("BASE_URL", "https://api.openai.com/v1"),
)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.RADIUM_API_KEY,
  baseURL: process.env.BASE_URL || "https://api.openai.com/v1",
});

Strategy 2: IDE profiles (Cursor, Cline, Continue)

Most coding agents support multiple provider profiles. Create a “Radium test” profile so you can switch without overwriting your main setup.

Cursor

1. Open Settings → Models
2. Add a custom OpenAI-compatible provider:
   • Base URL: https://api.radium.cloud/v1
   • API Key: your Radium key
   • Model: hal-1.0
3. Save as profile “Radium” and switch between OpenAI and Radium via the model dropdown

Cline

Edit .clinerules or Cline settings to add Radium as a secondary provider without removing your primary:

{
  "providers": [
    { "name": "OpenAI", "baseUrl": "https://api.openai.com/v1", "model": "gpt-4o" },
    { "name": "Radium", "baseUrl": "https://api.radium.cloud/v1", "model": "hal-1.0" }
  ]
}

Continue

Add Radium to your Continue config.json alongside existing providers:

{
  "models": [
    {
      "title": "GPT-4o",
      "provider": "openai",
      "model": "gpt-4o",
      "apiKey": "${OPENAI_API_KEY}"
    },
    {
      "title": "Radium Hal",
      "provider": "openai",
      "model": "hal-1.0",
      "apiBase": "https://api.radium.cloud/v1",
      "apiKey": "${RADIUM_API_KEY}"
    }
  ]
}

Strategy 3: Wrapper with fallback

For production services, wrap the client call so it falls back to your existing provider if Radium fails.

import os
from openai import OpenAI, APIError

radium = OpenAI(api_key=os.environ["RADIUM_API_KEY"], base_url="https://api.radium.cloud/v1")
openai = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

def chat_with_fallback(model: str, messages: list):
    try:
        radium_model = model.replace("gpt-4o", "hal-1.0")
        return radium.chat.completions.create(model=radium_model, messages=messages)
    except APIError as e:
        print(f"Radium error ({e.status_code}), falling back to OpenAI")
        return openai.chat.completions.create(model=model, messages=messages)

Strategy 4: Feature-flag routing

Use a feature flag (e.g., LaunchDarkly, Unleash, or a simple env var) to control the split:

import os

PROVIDER = os.environ.get("AI_PROVIDER", "openai")

if PROVIDER == "radium":
    client = OpenAI(api_key=os.environ["RADIUM_API_KEY"], base_url="https://api.radium.cloud/v1")
    model = "clarke-1.0"
else:
    client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
    model = "gpt-4o"

Validation checklist

Run through this before expanding the test:

• Responses complete successfully for your top 5 prompt types
• Tool calls return the same schema as your existing provider
• Streaming tokens arrive at acceptable speed
• Cost per request is visible in the Radium dashboard
• Error rate is below 1% over 100 requests

Rollback

If anything goes wrong, unset the env var or switch the IDE profile back. No code reverts needed.