Product Photography vs AI Product Images

A photograph records light from a physical product under a chosen camera, lens, exposure, and set. It can still mislead through styling, retouching, perspective, or a wrong sample, but the visible geometry begins with the photographed object. An AI product image reconstructs a scene from visual and text input. That makes rapid art direction possible and also creates a new failure class: invented letters, altered caps, missing seams, changed material, false scale, extra accessories, and inconsistent variants. Treat the photographic source as evidence and the generated output as a candidate until approved.

The retained source is a 1024 × 1024 photograph-like image of one tall blue ceramic cup on a plain gray set. Nano Banana 2 Lite received a warm limestone pedestal, soft upper-left daylight, subtle contact shadow, no added text, no props, 1:1, 1K, PNG, and one output. The run cost 8 Credits. Human review accepted the blue glaze, brown rim, opening, straight silhouette, foot, unbranded surface, and product count. Set, crop, lighting, and shadow changed. The evidence shows a successful directed variation, not a guarantee for another product or retry.

Prefer conventional capture for primary pack shots, regulated labels, serial numbers, color-critical variants, jewelry settings, transparent assemblies, reflective controls, garment fit, texture close-ups, included accessories, dimensions, assembly steps, and any view that a buyer uses as factual evidence. Photograph multiple approved angles because one front image cannot define the rear or hidden structure. Keep camera and product position consistent across a catalog when comparison matters. Retouch dust, exposure, and background carefully, but preserve the actual offer. Save the unretouched master and color reference for later review.

AI is useful when the approved source already defines the product and the goal is a campaign setting, seasonal background, lighting direction, negative space, concept test, or secondary lifestyle candidate. Generate one role at a time and freeze the source while comparing prompts or models. It is a poor shortcut for a missing sample, hidden product view, illegible package, or wrong variant. If the source does not prove a fact, the generated image cannot make that fact trustworthy. Keep the factual pack shot beside every derivative and expose both during approval.

Photography cost includes sample logistics, location, equipment, lighting, stylist, photographer, talent, set, retouching, reshoots, and scheduling. AI cost includes model Credits, retries, source preparation, prompt work, identity review, channel checks, retouching, and the possibility that a polished output is unusable. The cup’s 8-Credit charge is one historical generation cost, not the complete cost of an approved catalog asset. Pilot difficult SKUs, measure accepted results per attempt, and include reviewer time. A conventional shoot may be cheaper when dozens of exact angles are required; AI may be cheaper for a few approved scene variations.

Define which slots must be direct product evidence and which may be generated derivatives. Keep primary and detail photography factual; allow reviewed AI scenes for campaign, collection, editorial, and social support. Mark provenance internally, preserve source-to-output lineage, and keep marketplace metadata required for generative content. Before publication, compare logo, text, geometry, color, quantity, accessories, background, crop, alt text, file properties, and channel rules. If a generated theme repeatedly fails one product category, pause it rather than forcing more retries. The policy should make rejection normal and visible, not an exception hidden after launch.