Enables agents and developers to estimate, budget, and compare LLM spend per task before and during a run, including ranking models by expected cost per successful task and tracking spend in a local JSON ledger.
Provides real-time AI model pricing, cost estimation, and budget management tools to help agents understand and optimize their spending. It enables agents to compare costs across multiple providers and select the most cost-effective models for specific tasks.
Enables monitoring of running agent loops with token and monetary cost estimation, pruning of verbose thinking chains near limits, and routing sub-tasks between fast/cheap and deep reasoning models. It provides deterministic, MCP-compatible budget optimization for autonomous agents and copilot frameworks.
Enables AI agents to pre-flight estimate query costs, enforce hard budgets, reconcile actual billed spend, and suggest cheaper rewrites across BigQuery, Snowflake, and Databricks.