Free credits are a common way to reduce friction in AI products. A new user gets image generations, video minutes or agent runs before paying. The problem is that those free actions still consume real model and infrastructure resources.

If promotional usage is mixed with paid usage, gross margin can become difficult to interpret.

Separate paid and promotional consumption

Every credit should have a source: purchased, included in subscription, referral bonus, trial or customer-support adjustment. Track the serving cost against that source.

This makes it possible to see whether a promotion is functioning as acquisition spend or as hidden product cost.

Free usage is often a marketing expense economically

If a company gives a new user $5 of compute to encourage conversion, that cost behaves more like customer acquisition than paid service delivery. Management reporting can analyze it separately even if accounting classification differs.

The goal is clearer decision-making.

High-cost modalities need different trial limits

One free text chat and one free generated video do not have the same cost. Promotional design should reflect variable serving economics.

A single generic credit balance can help users understand value, but internal systems should still know the real cost behind each redemption.

Conversion alone is not enough

A promotion that converts many users can still be unattractive if those users require extremely high free usage before paying. Measure free compute cost per converted customer.

Compare that figure with other acquisition channels.

Watch for abuse

Generous free credits can attract users who create multiple accounts or automate signups. Abuse increases compute cost without increasing future revenue.

Rate limits, identity checks and device-level controls may be justified for expensive features.

Measure payback after free serving cost

Traditional CAC may include ads but exclude free inference. Add promotional compute to the acquisition cost when evaluating payback.

This is particularly important for video or image products where trial usage can be material.

Promotions can hide product-market problems

If engagement collapses as soon as free credits disappear, the issue may be willingness to pay rather than trial size. Test smaller offers to see whether users value the product enough to purchase earlier.

Model provider price changes affect old promotions

A credit package designed when inference was cheap may become uneconomic after a provider raises prices. Review promotional economics whenever the serving stack changes.

Our article on gross-margin sensitivity covers this risk.

Use cohort analysis by promotion

Compare retention, paid conversion, expansion and compute cost for users acquired through different credit campaigns. A smaller promotion may produce fewer signups but higher-quality customers.

A useful dashboard

• Promotional credits issued.
• Promotional credits redeemed.
• Compute cost of free usage.
• Conversion after free usage.
• Free-cost-per-converted-customer.
• Retention by promotion cohort.

Set a budget for promotional compute

Growth teams should know how much serving cost they are allowed to spend on free activation each month. A campaign can have a cap in dollars of compute, not just a cap in credits issued, because different features consume different resources.

This budget can be compared with paid advertising and other acquisition channels. If free inference costs $40 to acquire a customer whose first-year contribution is only $30, the promotion is not sustainable even if conversion looks strong. Treating promotional compute as a deliberate growth investment creates much clearer accountability than hiding it inside aggregate gross margin.

Free should have an explicit economic purpose

Free credits are not harmless because the user pays nothing. They are a real investment in acquisition and activation. Separating their cost from paid usage gives founders a much cleaner view of product margin and marketing efficiency.