# Bulk Image Generation for Product Catalogues (2026) > How bulk image generation works for a product catalogue: the run itself, the credit arithmetic, where the programmatic route stops, and what breaks at scale. Published: 2026-08-24 Updated: 2026-10-14 Author: Olga Stogova Tags: bulk-image-generation, product-catalogue, ecommerce-imagery, workflow-automation, ai-product-photography Kive studio: Saffron Corner Canonical: https://kive.ai/learn/bulk-image-generation-product-catalogue Bulk image generation means applying one set of decisions – a studio, an aspect ratio, a prompt – across many products in a single run, instead of generating each product separately. The reason to do it is not speed so much as sameness: a catalogue photographed one product at a time slowly stops looking like one catalogue. ## Key takeaways - A bulk run's output equals line items multiplied by versions: ten products at two versions each is twenty images. - Credits follow that same multiplication, which is how batches get expensive without the product count changing. - Kive publishes no REST API for generation and no CSV import; its programmatic surface is MCP, reachable from ChatGPT, Cursor, Figma, Notion or Zapier. - A review step lets individual line items take a different studio or a character model before anything generates. - Multiple products can share one line item, which is how a batch produces styled multi-product shots. --- ## What a bulk run actually does The flow is short enough to describe completely. In Kive you open the + menu in the prompt bar (it sits on the home screen as well as Create), choose Bulk, then tick the products you want included – search helps once the library is large – and continue. You pick one [studio preset](/learn/what-are-ai-studios-how-do-they-work) that will apply to every product in the run, from Discover or from your own studios, and that single choice determines lighting, background and style for the whole batch. Then comes the part that makes it usable rather than blunt. A review screen lists every line item with its assigned product, studio and character, and you can override individually from there: give specific products a different studio, attach a character model to particular items, or add several products to one line item so they are composed into a single shot together. The last one is how a batch produces styled group imagery rather than a hundred isolated objects. ### The settings that apply to everything After the review you add an optional custom prompt, which applies to all generations – so it is for details you want everywhere, not for anything product-specific. You set the aspect ratio, and you set how many versions each line item should produce. The full step-by-step lives in Kive's [bulk generate documentation](https://kive.ai/docs/creating-with-ai/bulk-generate). _One studio, first product: red and black props, soft even light – Kive studio: Stark Plateau_ --- ## The arithmetic that decides your batch size Versions multiply by line items. Two versions across ten products is twenty images, and the credit cost is the same multiplication – line items times versions per item. That sounds obvious written down and it is the single most common way a batch costs more than expected, because raising versions from one to three feels like a quality decision and is actually a tripling. At roughly ten credits an image, Kive's $40 Basic plan (the cheapest tier with bulk generation; the feature is not on the free plan) and its 2,000 credits cover about two hundred generations a month, with unused credits rolling over up to twice the monthly limit (kive.ai/pricing). Against the wider market that lands mid-range: Claid's survey of lifestyle generators puts subscription pricing anywhere from a few cents to roughly half a dollar per image [[2]](https://claid.ai/blog/article/ai-lifestyle-photo-generators). Pebblely, at the pure-volume end, sells 30 images for $9, 200 for $19 and 500 for $39, though bulk generation itself only appears from the $19 tier upward (pebblely.com) – our [Kive vs Pebblely comparison](/learn/kive-vs-pebblely) prices that trade head to head. The practical rule from Kive's own documentation is to test settings on a few products before committing a large batch. A studio you dislike at product four costs almost nothing. The same realisation at product four hundred is the expensive version of the same lesson. --- ## Where the programmatic route stops This is the honest limitation, and it decides the tool for a whole category of team. Kive's documentation lists no REST API for generation and no CSV import. Bulk generation is driven by the product picker in the app, and the programmatic surface is MCP – the [connector any MCP client can reach](https://kive.ai/docs/mcp/connect-any-client), including ChatGPT, Claude, Cursor, Figma, Notion and Zapier. That is genuinely useful for agent-driven work: an assistant can pick products, choose a studio and poll jobs conversationally, which is a different shape of automation from a cron job hitting an endpoint – our [Claude MCP product-shot walkthrough](/learn/claude-mcp-product-shots) documents one such session end to end. It is not a substitute for one. If the requirement is a script that runs nightly against a store feed, or a spreadsheet of SKUs handed to a pipeline, Photoroom is the tool that publishes that surface – an API for wiring staging into a website or marketplace, plus batch editing that applies the same background or effect across hundreds of images in a few clicks [[1]](https://www.photoroom.com/tools/product-staging). Teams who need both usually run both, which is less absurd than it sounds when the two are solving different halves. _Same studio, second product: the props change, the look does not – Kive studio: Stark Plateau_ --- ## How the tools divide the work Facts below come from each vendor's public pages, checked August 2026. | Tool | How a batch runs | Programmatic access | | --- | --- | --- | | Kive | Product picker, one studio across the run, per-item overrides at review | MCP connector; no published REST API for generation or CSV import | | Photoroom | Batch editing applies one background or effect across hundreds of images | REST API for stores and marketplace feeds | | Pebblely | Metered purely on image count – 30, 200 or 500 a month by plan, with bulk generation starting on the $19 Basic tier | Not published on its plan pages | Pick by what the batch is for. Photoroom's unit of work is a listing, which is why it meters exports and why its automation story is an endpoint. Pebblely's is an image, which makes its $9 tier the cheapest way to find out whether AI scenes suit your category at all – though batching starts a tier above that. Kive's is a look applied to a catalogue, which is why the run starts by choosing one studio and why the interesting controls are the per-item overrides rather than the throughput. Our [three-way comparison of Kive, Flair.ai and Photoroom](/learn/kive-vs-flair-ai-vs-photoroom) goes deeper on the staging differences behind those choices. --- ## What actually breaks at catalogue scale Not the generation. The generation is unattended and finishes while you do something else. What breaks is review. A hundred images arrive at once and get approved in a scroll, which is exactly the condition under which a drifting backdrop or a wrong crop ships. Put the outputs in a grid at one size and read them as a row, the way a customer meets them – the same check that catches [campaign drift in a brand kit](/learn/ai-brand-kit-consistent-campaign-visuals). The second failure is a batch run before the direction was settled. Bulk multiplies whatever decision you fed it, including a bad one, and the cost of being wrong scales with the line-item count. Settle the look on a handful of products, then scale the settled thing. Where a bulk run sits among the other steps, from capture to export per channel, is mapped in the [product photography workflow guide](/learn/product-photography-workflow). Generating scenes one product at a time first is covered in the [AI scene generator guide](/learn/ai-scene-generator-product-lifestyle). --- Bulk generation is the point where AI imagery stops being a novelty and starts being an operation. The interesting question moves from whether the model can render your product to whether four hundred renders of it agree with each other. Which makes the boring parts decisive: how the tool lets you override one item without splitting the run, whether unused credits survive a quiet month, and whether the automation surface you need is the one the vendor happens to publish. ## Sources 1. [Photoroom – Product staging](https://www.photoroom.com/tools/product-staging) 2. [Claid – Best AI lifestyle photo generators](https://claid.ai/blog/article/ai-lifestyle-photo-generators) ## FAQ ### How do I generate product images in bulk? Select the products, apply one studio preset to all of them, review the line items, then set aspect ratio and how many versions each product should get. In Kive that starts from the prompt bar's + menu, and the same preset is what keeps a hundred outputs looking like one shoot rather than a hundred decisions. ### How many images does a bulk run actually produce? Line items multiplied by versions. Ten products at two versions each is twenty images, and the credit cost follows the same multiplication. This is where batches get expensive by accident, because raising versions from one to three triples the bill without changing the product count. ### Can I run bulk generation through an API? Kive publishes no REST API for generation and no CSV import; its programmatic surface is MCP, which any MCP client can reach – ChatGPT, Claude, Cursor, Figma, Notion or Zapier. Photoroom is the option to look at when you specifically need a REST endpoint wired into a store or marketplace feed. ### Should every product in a batch use the same studio? Usually yes, because a shared studio is the thing that makes the grid read as one catalogue. Kive's review step still lets you override individual line items, so the handful of products that genuinely need different treatment do not force you to split the run. ### How do I avoid wasting credits on a large batch? Test the settings on a few products first. Kive's own documentation recommends starting small before running a large batch, and the reason is that a studio choice you dislike at product four is cheap while the same discovery at product four hundred is not.