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Budget Limiting

This page covers budget limiting configuration and features in the Radicalbit AI Gateway.

Overview​

Budget limiting in the Radicalbit AI Gateway controls costs by setting a limit on the combined budget (input + output) consumed across all models in a route, helping to manage AI usage expenses.

A single time window tracks total spending — when the combined cost of input and output tokens crosses the configured threshold within the window, further requests are rejected until the window resets.

Budget Limiting Configuration​

Basic Budget Limiting​

Set a combined cost limit for input and output tokens:

chat_models:
  - model_id: gpt-4o
    model: openai/gpt-4o
    input_cost_per_million_tokens: 5.0
    output_cost_per_million_tokens: 15.0

routes:
  production:
    chat_models:
      - gpt-4o
    budget_limiting:
      algorithm: fixed_window
      window_size: 1 hour
      max_budget: 50.0   # $50 per hour maximum combined cost

Advanced Budget Configuration​

chat_models:
  - model_id: gpt-4o
    model: openai/gpt-4o
  - model_id: gpt-3.5-turbo
    model: openai/gpt-3.5-turbo

routes:
  enterprise:
    chat_models:
      - gpt-4o
      - gpt-3.5-turbo
    budget_limiting:
      algorithm: fixed_window
      window_size: 1 minute
      max_budget: 100

Budget Limiting Types​

Cost-Based Budget Limiting​

Control costs by setting a maximum budget (in dollars) per time window:

budget_limiting:
  algorithm: fixed_window
  window_size: 1 hour
  max_budget: 50.0   # $50 per hour for combined input + output tokens

Note: Budget limiting uses cost calculations based on model pricing. You can set either max_token (token-based limits) or max_budget (cost-based limits), but not both in the same configuration.

Budget Limiting Behavior​

When Limits Are Exceeded​

  • HTTP 429: Too Many Requests status code
  • Budget Exceeded: Clear error message
  • Retry Information: When budget resets

Response Headers (if exposed by the gateway)​

HTTP/1.1 429 Too Many Requests
X-Budget-Limit: 100
X-Budget-Remaining: 0
X-Budget-Reset: 1640995200

Header names/availability may vary depending on the deployment and gateway version.

Best Practices​

Budget Planning​

  • Start with conservative limits
  • Monitor actual usage patterns
  • Adjust based on business needs
  • Consider peak usage times

Cost Management​

  • Use different limits for different routes (and therefore workloads)
  • Ensure model costs are configured (manual or automatic price assignment)
  • Regular budget reviews
  • Cost optimization strategies

Error Handling​

  • Implement proper retry logic
  • Handle budget exceeded gracefully
  • Provide user feedback
  • Consider fallback to cheaper models (with fallback configuration)

Troubleshooting​

Common Issues​

  1. Budget Too Low: Increase limits based on actual usage
  2. Unexpected Costs: Check model pricing and usage
  3. Reset Issues: Verify window_size configuration

Next Steps​