Migrate from Google AI Studio / Vertex AI to Railwail

Google's Gemini is one of the most capable multimodal models on the market — but accessing it via Vertex AI requires a GCP project, service account credentials, IAM bindings, and a billing account. AI Studio is friendlier but rate-limited and not production-ready. Railwail exposes the same Gemini models through the standard OpenAI Chat Completions API with no GCP setup whatsoever.

Common migration triggers: avoiding GCP project sprawl, escaping AI Studio's free-tier RPM caps, and unifying Gemini calls with GPT-4o or Claude under one SDK.

Create an account at railwail.com and generate a key in Dashboard → API Keys. No GCP project required.

TypeScript / JavaScript

import { GoogleGenerativeAI } from "@google/generative-ai";

const genAI = new GoogleGenerativeAI(process.env.GOOGLE_API_KEY);
const model = genAI.getGenerativeModel({ model: "gemini-2.5-pro" });

const result = await model.generateContent("Hello Gemini");
console.log(result.response.text());

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.RAILWAIL_API_KEY,
  baseURL: "https://railwail.com/api/v1",
});

const res = await client.chat.completions.create({
  model: "gemini-2.5-pro",
  messages: [{ role: "user", content: "Hello Gemini" }],
});
console.log(res.choices[0].message.content);

from openai import OpenAI

client = OpenAI(
    api_key=os.environ["RAILWAIL_API_KEY"],
    base_url="https://railwail.com/api/v1",
)

resp = client.chat.completions.create(
    model="gemini-2.5-pro",
    messages=[{"role": "user", "content": "Hello Gemini"}],
)
print(resp.choices[0].message.content)

curl https://railwail.com/api/v1/chat/completions \
  -H "Authorization: Bearer $RAILWAIL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-2.5-pro",
    "messages": [{"role": "user", "content": "Hello Gemini"}]
  }'

Gemini's killer feature is native multimodality. On Railwail, multimodal inputs use the standard OpenAI vision schema (content array with image_url or input_audio blocks). Railwail translates to Gemini's native parts format internally.

const res = await client.chat.completions.create({
  model: "gemini-2.5-pro",
  messages: [{
    role: "user",
    content: [
      { type: "text", text: "What is in this image?" },
      { type: "image_url", image_url: { url: "https://example.com/img.jpg" } }
    ]
  }],
});

Gemini's function calling translates to the OpenAI tools schema. Define your tools as JSON Schema, set tool_choice, and read tool_calls off the response — Railwail handles the conversion to Gemini's tools.functionDeclarations format and back.

• No GCP project — just an API key
• OpenAI-compatible SDK — unify Gemini calls with GPT-4o and Claude in one client
• Higher default rate limits than AI Studio's free-tier
• 1M context, multimodal, function calling — all preserved
• Built-in playground to A/B Gemini vs other models on the same prompt

• Create your Railwail account at railwail.com
• Generate an API key in Dashboard → API Keys
• Replace @google/generative-ai with the OpenAI SDK in your code
• Update baseURL to https://railwail.com/api/v1
• Read the full API reference at railwail.com/docs
• Browse Gemini models at railwail.com/models?provider=google
• Compare per-token pricing at railwail.com/pricing