Cost Control in AI: Implementing Budgets and Tracking in devlog-ist/landing

This post explores the recent updates to cost tracking and budgeting for AI operations within the devlog-ist/landing project.

The Challenge

As AI models become more integrated into our applications, managing and controlling the costs associated with their usage is crucial. Without proper tracking and budgeting, expenses can quickly spiral out of control, especially when dealing with various models and usage patterns.

The Solution

The solution involved several key components:

1. EUR-Based Cost Tracking: Implementing the ability to track per-request costs in EUR across all AI operations.
2. Model Pricing Storage: Storing pricing information for each AI model to accurately calculate costs.
3. Monthly Budget Limits: Enforcing monthly cost limits per plan to prevent overspending. Different plans have different limits (e.g., EUR 0.10 for free, EUR 1.00 for paid, and EUR 5.00 for corporate).
4. Specific Model Registration: Registering the gemini-2.5-flash-image model and tracking its token usage for specific tasks like LinkedIn banner generation.

Implementation Details

The implementation likely involved modifications to the existing request handling logic. For example, every time an AI model is called, the cost is calculated based on the model's pricing and token usage. This cost is then tracked against the monthly budget limit for the user's plan. Here's an illustrative example of how the cost tracking might work in a simplified system:

def calculate_cost(model_name, token_usage):
    model_pricing = {
        'gemini-2.5-flash-image': 0.00002  # EUR per token
    }
    cost_per_token = model_pricing.get(model_name)
    if cost_per_token is None:
        raise ValueError(f'Pricing not found for model: {model_name}')
    cost = token_usage * cost_per_token
    return cost

def track_usage(user_plan, cost):
    budget_limits = {
        'free': 0.10,
        'paid': 1.00,
        'corporate': 5.00
    }
    budget_limit = budget_limits.get(user_plan)
    if budget_limit is None:
        raise ValueError(f'Invalid plan: {user_plan}')

    # Simplified example, would require database interaction
    current_usage = 0  # Fetch from database
    new_usage = current_usage + cost
    if new_usage > budget_limit:
        raise ValueError('Monthly budget exceeded')

Benefits

• Cost Control: Clear limits prevent unexpected expenses.
• Transparency: Tracking provides insights into AI usage costs.
• Scalability: The system can adapt as new models are added.

Conclusion

Implementing cost tracking and budget limits for AI operations is essential for sustainable development. By monitoring costs and enforcing limits, devlog-ist/landing can ensure that AI usage remains within acceptable financial boundaries. Consider implementing similar cost-control measures in your projects to maintain financial stability and predictability.