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Kevin Stumpf, Senior Staff Engineer at Databricks, proposed that enterprise AI cost management is shifting from tokenmaxxing, the pursuit of token consumption volume, to valuemaxxing, measuring the impact produced per dollar invested. This judgment was published by the Databricks Blog on October 23, 2026. Stumpf also introduced cost attribution methods: Databricks can attribute costs out of the box by user, group, agent, AI gateway endpoint, model, provider, and time period, and also supports custom attribution via request-level tags to specific pull requests or external customers. On controlling context bloat, he proposed that the key is to have agents pull context on demand through tools, MCP servers, or skills, rather than pushing it in advance. Currently this view comes from a single first-party source, with no subsequent development facts yet.
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Oct 23
Databricks BlogFirst-party
Databricks on AI cost management: from tokenmaxxing to valuemaxxing
Kevin Stumpf, Senior Staff Engineer at Databricks, argues that enterprise AI cost management is shifting from tokenmaxxing—the pursuit of token consumption volume—to valuemaxxing, which measures the impact generated per dollar invested. Databricks can attribute costs out of the box by user, group, agent, AI gateway endpoint, model, provider, and time period, and also supports custom attribution via request-level tags to specific pull requests or external customers. The key to controlling context bloat is to have agents pull context on demand through tools, MCP servers, or skills, rather than pushing it in advance.