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The full cost of a technology over its life, including licenses, usage, infrastructure, integration, training and support, not only its purchase price.

TCO prevents buying decisions based on sticker price alone, which often hides larger costs for usage, integration, change and support; for AI tools it puts token spend, training time and unused seats next to license fees.

A company comparing two AI assistants for 50 staff adds three years of seat licenses, expected API usage, single sign-on setup, security review, training hours at a loaded hourly cost and admin time, then compares the totals instead of the per-seat prices.

TCO estimates rest on assumptions about usage, prices and lifetime that change quickly, especially for usage-priced AI. It measures cost only, so a decision also needs the expected value, for example through ROI or payback.

TCO sums direct and indirect costs over a defined period, often three to five years: acquisition or subscription, implementation and integration, infrastructure and usage, training, operation and support, and exit or migration. For AI, usage-based costs such as API calls and tokens (see LLM Tokenization) grow with adoption, and staff time spent learning and reviewing AI output is a real cost. Options must be compared over the same period, user count and assumptions. TCO is the cost side of Return on Investment (ROI) and Payback Period calculations, and modeling exit costs early exposes Vendor Lock-In before renewal.