Anthropic Multi-Environment Setup

Overview

Configure isolated Claude API environments with per-env API keys, model selection, and spend controls using Anthropic Workspaces.

Environment Configuration

# config.py
import os
from dataclasses import dataclass

@dataclass
class ClaudeConfig:
    api_key: str
    model: str
    max_tokens: int
    max_retries: int
    timeout: float
    monthly_budget_usd: float

CONFIGS = {
    "development": ClaudeConfig(
        api_key=os.environ["ANTHROPIC_API_KEY_DEV"],
        model="claude-haiku-4-20250514",       # Cheap for dev
        max_tokens=256,
        max_retries=1,
        timeout=15.0,
        monthly_budget_usd=10.0,
    ),
    "staging": ClaudeConfig(
        api_key=os.environ["ANTHROPIC_API_KEY_STAGING"],
        model="claude-sonnet-4-20250514",
        max_tokens=1024,
        max_retries=2,
        timeout=30.0,
        monthly_budget_usd=50.0,
    ),
    "production": ClaudeConfig(
        api_key=os.environ["ANTHROPIC_API_KEY_PROD"],
        model="claude-sonnet-4-20250514",
        max_tokens=4096,
        max_retries=5,
        timeout=120.0,
        monthly_budget_usd=5000.0,
    ),
}

def get_config() -> ClaudeConfig:
    env = os.getenv("APP_ENV", "development")
    return CONFIGS[env]

Anthropic Workspaces (Key Isolation)

Create separate Workspaces in console.anthropic.com:

Each workspace has independent API keys, usage tracking, and rate limits.

Environment Files

# .env.development
ANTHROPIC_API_KEY_DEV=sk-ant-api03-dev-...
APP_ENV=development

# .env.staging
ANTHROPIC_API_KEY_STAGING=sk-ant-api03-stg-...
APP_ENV=staging

# .env.production (stored in secret manager, not files)
ANTHROPIC_API_KEY_PROD=sk-ant-api03-prd-...
APP_ENV=production

Client Factory

import anthropic

def create_client() -> anthropic.Anthropic:
    config = get_config()
    return anthropic.Anthropic(
        api_key=config.api_key,
        max_retries=config.max_retries,
        timeout=config.timeout,
    )

Per-Environment Model Override

# Development: always use Haiku (cheapest)
# Staging: use production model for accuracy testing
# Production: use configured model

def get_model(override: str | None = None) -> str:
    if override:
        return override
    return get_config().model

Error Handling

Resources

• Workspaces
• Console

Next Steps

For monitoring, see anth-observability.