Tokenomics 101

AI token consumption is rising rapidly as models gain larger context windows, longer-running agent capabilities, and the ability to coordinate subagents, making cost management increasingly important. Major providers generally charge for input, output, and cached tokens, while the most useful cost metric for agentic systems is increasingly the cost per successfully completed task rather than the cost of individual conversations or tool calls. Recommended optimization approaches include routing simpler work to smaller or specialized models, using lightweight agent harnesses with limited prompts and essential tools, and maintaining organizational memory to avoid repeatedly rediscovering information. The text also highlights capacity planning, suggesting that lower-priority asynchronous tasks could use slower, cheaper inference during off-peak periods. For workloads where these measures are insufficient, it promotes dedicated GPU infrastructure with flat-rate pricing as a potentially cheaper alternative to fully pay-per-token usage, while retaining elastic capacity for traffic spikes.

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