While codeburn currently offers an excellent user experience for monitoring token expenditure through online models and their API rates, its current approach is only half of the story for cost optimization.
The other half is to support local model cost saving analysis. I currently do this by having my local model map to the model I would usually use in place of it (e.g., Qwen 3 Coder mapping to GPT 5.3 codex). While this gets the job done by showing the hypothetical money saved, it is still being reported as "spend" rather than savings.
Key abilities:
- Map local models as "savings" rather than costs
- Provide toggles that allow users to map their local models to the pricing of different models easily, to estimate the savings based on which model they're hypothetically replacing. This is useful for reporting on how much a user saved by using a local model in place of other models.
- Forecasting of savings over time based on increased or decreased percentage of overall LLM usage going toward local models versus paid models.
While codeburn currently offers an excellent user experience for monitoring token expenditure through online models and their API rates, its current approach is only half of the story for cost optimization.
The other half is to support local model cost saving analysis. I currently do this by having my local model map to the model I would usually use in place of it (e.g., Qwen 3 Coder mapping to GPT 5.3 codex). While this gets the job done by showing the hypothetical money saved, it is still being reported as "spend" rather than savings.
Key abilities: