Migration Guide

Why Migrate to SaveGate?

30-50% Cost Savings

Save significantly on API costs with enterprise pricing passed to you

Unlimited Rate Limits

No more TPM/RPM restrictions - scale without limits

Multi-Provider Access

Access all providers through a single API key

Drop-In Replacement

Change 2 lines of code - that’s it!

From OpenAI

Python

from openai import OpenAI

client = OpenAI(
    api_key="sk-...your-openai-key..."
)

response = client.chat.completions.create(
    model="gpt-5.1",
    messages=[{"role": "user", "content": "Hello!"}]
)

from openai import OpenAI

client = OpenAI(
    api_key="sk-savegate-xxxxxxxxxxxxx",  # SaveGate API key
    base_url="https://api.savegate.ai/v1"  # SaveGate base URL
)

response = client.chat.completions.create(
    model="gpt-5.1",
    messages=[{"role": "user", "content": "Hello!"}]
)

Node.js

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY
});

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.SAVEGATE_API_KEY,
  baseURL: 'https://api.savegate.ai/v1'
});

Environment Variables

Update your .env file:

OPENAI_API_KEY=sk-...your-openai-key...

SAVEGATE_API_KEY=sk-savegate-xxxxxxxxxxxxx
OPENAI_API_BASE=https://api.savegate.ai/v1

From Anthropic

Python

from anthropic import Anthropic

client = Anthropic(
    api_key="sk-ant-..."
)

message = client.messages.create(
    model="claude-sonnet-4.5",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello"}]
)

from anthropic import Anthropic

client = Anthropic(
    api_key="sk-savegate-xxxxxxxxxxxxx",
    base_url="https://api.savegate.ai/v1"
)

message = client.messages.create(
    model="claude-sonnet-4.5",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello"}]
)

from openai import OpenAI

client = OpenAI(
    api_key="sk-savegate-xxxxxxxxxxxxx",
    base_url="https://api.savegate.ai/v1"
)

response = client.chat.completions.create(
    model="claude-sonnet-4.5",
    messages=[{"role": "user", "content": "Hello"}]
)

From Google AI

import google.generativeai as genai

genai.configure(api_key="...")
model = genai.GenerativeModel('gemini-pro')
response = model.generate_content("Hello")

from openai import OpenAI

client = OpenAI(
    api_key="sk-savegate-xxxxxxxxxxxxx",
    base_url="https://api.savegate.ai/v1"
)

response = client.chat.completions.create(
    model="gemini-pro",
    messages=[{"role": "user", "content": "Hello"}]
)

From LangChain

Before

from langchain.chat_models import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-5.1",
    openai_api_key="sk-..."
)

After

from langchain.chat_models import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-5.1",
    openai_api_key="sk-savegate-xxxxxxxxxxxxx",
    openai_api_base="https://api.savegate.ai/v1"
)

Or use environment variables:

import os

os.environ["OPENAI_API_KEY"] = "sk-savegate-xxxxxxxxxxxxx"
os.environ["OPENAI_API_BASE"] = "https://api.savegate.ai/v1"

from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(model="gpt-4")

From LlamaIndex

Before

from llama_index.llms import OpenAI

llm = OpenAI(model="gpt-4", api_key="sk-...")

After

from llama_index.llms import OpenAI

llm = OpenAI(
    model="gpt-5.1",
    api_key="sk-savegate-xxxxxxxxxxxxx",
    api_base="https://api.savegate.ai/v1"
)

Migration Checklist

Get SaveGate API Key

Update Configuration

Change your API key and base URL in your code or environment variables

Test in Development

Run your application in development to ensure everything works

Monitor Usage

Check the SaveGate dashboard to verify requests are flowing through

Deploy to Production

Update your production environment variables and deploy

Optimize

Use multi-model features and monitor costs to optimize further

Common Migration Patterns

Pattern 1: Feature Flag

Test SaveGate alongside your current provider:

import os
from openai import OpenAI

USE_SAVEGATE = os.getenv("USE_SAVEGATE", "false").lower() == "true"

if USE_SAVEGATE:
    client = OpenAI(
        api_key=os.getenv("SAVEGATE_API_KEY"),
        base_url="https://api.savegate.ai/v1"
    )
else:
    client = OpenAI(
        api_key=os.getenv("OPENAI_API_KEY")
    )

# Rest of your code remains the same
response = client.chat.completions.create(...)

Pattern 2: Gradual Rollout

Migrate a percentage of requests:

import random
from openai import OpenAI

def get_client():
    # 20% of requests go to SaveGate
    if random.random() < 0.2:
        return OpenAI(
            api_key="sk-savegate-xxxxxxxxxxxxx",
            base_url="https://api.savegate.ai/v1"
        )
    else:
        return OpenAI(api_key="sk-openai-...")

client = get_client()

Pattern 3: Fallback Strategy

Use SaveGate with fallback to original provider:

from openai import OpenAI

def chat_with_fallback(message, model="gpt-4"):
    # Try SaveGate first
    try:
        client = OpenAI(
            api_key="sk-savegate-xxxxxxxxxxxxx",
            base_url="https://api.savegate.ai/v1"
        )
        response = client.chat.completions.create(
            model=model,
            messages=[{"role": "user", "content": message}],
            timeout=10
        )
        return response.choices[0].message.content

    except Exception as e:
        print(f"SaveGate failed, using fallback: {e}")

        # Fallback to OpenAI
        client = OpenAI(api_key="sk-openai-...")
        response = client.chat.completions.create(
            model=model,
            messages=[{"role": "user", "content": message}]
        )
        return response.choices[0].message.content

Troubleshooting

401 Unauthorized

Issue: Invalid API keySolution:

Verify your SaveGate API key is correct
Check that you’re using sk-savegate- prefix
Ensure the key hasn’t been revoked

Model Not Found

Issue: Model ID not recognizedSolution:

Check the models list
Verify the model ID is correct
Some model IDs may differ slightly from original providers

Different Response Format

Issue: Response structure seems differentSolution:

SaveGate uses OpenAI-compatible response format
Update your response parsing if using Anthropic SDK
See API reference for details

Rate Limits (Rare)

Issue: Hit rate limitsSolution:

This should be rare with SaveGate
Implement exponential backoff
Contact support if persistent

Cost Comparison Tool

Estimate your savings after migration:

def calculate_savings(monthly_tokens_in, monthly_tokens_out, model="gpt-4"):
    # Pricing per 1K tokens
    pricing = {
        "gpt-5.1": {
            "openai": {"input": 0.04, "output": 0.08},
            "savegate": {"input": 0.028, "output": 0.056}
        },
        "gpt-4.2": {
            "openai": {"input": 0.02, "output": 0.04},
            "savegate": {"input": 0.014, "output": 0.028}
        },
        "claude-sonnet-4.5": {
            "anthropic": {"input": 0.004, "output": 0.020},
            "savegate": {"input": 0.003, "output": 0.014}
        }
    }

    model_pricing = pricing.get(model)
    if not model_pricing:
        return "Model not found"

    # Calculate costs (convert to thousands)
    tokens_in_k = monthly_tokens_in / 1000
    tokens_out_k = monthly_tokens_out / 1000

    original_cost = (
        tokens_in_k * list(model_pricing.values())[0]["input"] +
        tokens_out_k * list(model_pricing.values())[0]["output"]
    )

    savegate_cost = (
        tokens_in_k * model_pricing["savegate"]["input"] +
        tokens_out_k * model_pricing["savegate"]["output"]
    )

    savings = original_cost - savegate_cost
    savings_percent = (savings / original_cost) * 100

    return {
        "original_cost": f"${original_cost:.2f}",
        "savegate_cost": f"${savegate_cost:.2f}",
        "monthly_savings": f"${savings:.2f}",
        "yearly_savings": f"${savings * 12:.2f}",
        "savings_percent": f"{savings_percent:.1f}%"
    }

# Example: 10M input tokens, 5M output tokens per month
result = calculate_savings(10_000_000, 5_000_000, "gpt-4.2")
print(result)
# {'original_cost': '$400.00', 'savegate_cost': '$280.00',
#  'monthly_savings': '$120.00', 'yearly_savings': '$1,440.00',
#  'savings_percent': '30.0%'}

Need Help?

Migration Support

Get help from our team with your migration

Documentation

Review our quickstart guide

API Reference

Explore the complete API docs

Getting Started

Core Concepts

SDK Integration

Guides

Why Migrate to SaveGate?

30-50% Cost Savings

Unlimited Rate Limits

Multi-Provider Access

Drop-In Replacement

From OpenAI

Python

Node.js

Environment Variables

From Anthropic

Python

From Google AI

From LangChain

Before

After

From LlamaIndex

Before

After

Migration Checklist

Common Migration Patterns

Pattern 1: Feature Flag

Pattern 2: Gradual Rollout

Pattern 3: Fallback Strategy

Troubleshooting

Cost Comparison Tool

Need Help?

Migration Support

Documentation

API Reference

​Why Migrate to SaveGate?

30-50% Cost Savings

Unlimited Rate Limits

Multi-Provider Access

Drop-In Replacement

​From OpenAI

​Python

​Node.js

​Environment Variables

​From Anthropic

​Python

​From Google AI

​From LangChain

​Before

​After

​From LlamaIndex

​Before

​After

​Migration Checklist

​Common Migration Patterns

​Pattern 1: Feature Flag

​Pattern 2: Gradual Rollout

​Pattern 3: Fallback Strategy

​Troubleshooting

​Cost Comparison Tool

​Need Help?

Migration Support

Documentation

API Reference

Why Migrate to SaveGate?

From OpenAI

Python

Node.js

Environment Variables

From Anthropic

Python

From Google AI

From LangChain

Before

After

From LlamaIndex

Before

After

Migration Checklist

Common Migration Patterns

Pattern 1: Feature Flag

Pattern 2: Gradual Rollout

Pattern 3: Fallback Strategy

Troubleshooting

Cost Comparison Tool

Need Help?