AI startups can sign infrastructure commitments long before revenue fully catches up. These deals may secure scarce capacity or better pricing, but they can also create obligations that make headline cash balances look safer than they are.

Separate cash from committed spend

A startup with substantial cash may still have limited flexibility if a large portion is effectively reserved for non-cancellable infrastructure commitments.

Understand minimum usage clauses

Cloud and GPU contracts can include minimum spend, take-or-pay structures or prepaid credits. Investors should know what happens if demand grows more slowly than expected.

Measure utilization

Committed capacity is valuable only if the company can use it productively. Low utilization turns an infrastructure advantage into a margin and runway problem.

Model downside scenarios

Stress-test lower revenue growth, slower customer onboarding and falling model prices. A commitment that looks attractive in a high-growth plan may be expensive in a downside case.

Consider vendor concentration

Large commitments can create switching costs and dependence on one cloud or hardware provider.

Compare with API flexibility

Using external model APIs may cost more per unit but preserve variable-cost flexibility. The right choice depends on scale and predictability.

Link commitments to infrastructure risk

Our AI infrastructure exposure guide covers provider and capacity risk. Contract commitments are the financial side of that same decision.

Runway should be adjusted for obligations

A practical analysis subtracts near-term committed cash outflows before estimating discretionary runway.

Compute deals are financing decisions too

Infrastructure strategy affects more than technical performance. Multi-year commitments can materially change capital needs and operating leverage.