# FASHN vs Botika vs Kive: On-Model Photos Without a Shoot (2026) > FASHN, Botika and Kive make on-model photos of your clothing without a photoshoot, from different inputs. Pricing verified October 2026 and the job each fits. Published: 2026-10-19 Author: Olga Stogova Tags: fashn-vs-botika, botika-alternative, virtual-try-on, on-model-photography, ai-clothing-model-generator Canonical: https://kive.ai/learn/fashn-vs-botika-vs-kive-on-model-photos FASHN, Botika and Kive all promise on-model photos of your clothing without a photoshoot, and they get there by three different routes. FASHN is a try-on model with an app and an API: it puts a garment onto a model image you supply or pick, one output at a time. Botika is a managed service with a library of ready AI models and human retouchers on the yearly plans. Kive is a workspace where the person is a saved character trained from 1–3 photos, the garment is a saved product and the scene is a look picked from Ideas; remix the same look with the next garment and the person, framing and light stay the same across a campaign. Which one fits depends on how many garments share one model, and on who fixes the misses. ## Key takeaways - FASHN Basic is $19 a month for 200 credits; most tools start at 1 credit per output, so a single try-on costs cents. - Botika Pro is $46 a month billed annually for 600 credits a year, one credit per photo, with two human retouch rounds. - Kive Basic is $40 a month for 2,000 credits, about 200 images, with a character trained from 1–3 photos in about 10 seconds. - Only Botika includes human retouching; FASHN and Kive leave fixes to you, metered in credits rather than rounds. --- ## What you have to supply The input decides the tool before any feature does. FASHN wants two images, a person and a garment. Botika wants the garment alone. Kive wants the garment plus 1–3 photos of whoever will wear it. FASHN's try-on endpoints take a model image and a product image and return the garment on that person. Try-On Max handles clothing, shoes, bags, hats and jewelry at up to 4K, and Product to Model turns a flat-lay or ghost-mannequin photo into an on-model image with an optional face and background reference (fashn.ai/products/api, October 2026). The app wraps the same tools in a chat agent and an Elements library of ready models, poses and backgrounds. Botika takes a flat lay, hanger or ghost-mannequin shot and returns on-model photography in the model, pose and background your team picks from a library of 150+ AI models [[1]](https://botika.com). You never supply a person. That is the point for a catalog team, and the limit for a brand that wants its own face: custom models arrive through the Enterprise tier, built by Botika's team. ### The workspace route asks for more up front In Kive a [character model](https://kive.ai/docs/guides/ai-character-models) trains from 1–3 photos of one person in about 10 seconds and is referenced as @name in any prompt; the garment becomes a [product model](https://kive.ai/docs/creating-with-ai/train-a-custom-model) from 1–4 photos; lighting and framing come from a [look picked in Ideas](https://kive.ai/docs/discover-inspiration/start-from-an-idea), Kive's catalog of product and fashion photos. Open the look, swap in the garment, add @name for the person, send, and Kive keeps the rest of the scene. Both model types sit on paid plans. The reward for that setup is that the fiftieth garment, remixed into the same look, gets the same person, light and framing as the first without anyone choosing them again. The [on-model photography guide](/learn/on-model-photography-without-photoshoot) covers the capture step all three share. --- ## Identity across frames and garment fidelity All three hold a model's identity between images; they differ in where the identity lives. In FASHN it lives in a reference you attach each time: Face Reference adds 2 credits per image in the Studio on Product to Model, Model Swap and Create Model, and the API's product-to-model endpoint takes optional face and background references (help.fashn.ai, October 2026). In Botika it lives in the library model you chose, held by the service across the collection. In a trained character it lives in the model itself, called by name, which is the difference between re-attaching a face for every frame and never thinking about it after day one. Garment fidelity is a separate test, and no vendor passes it unattended. FASHN's own FAQ tells you to compare each image with the real product before publishing because small details can still go wrong. Botika's homepage promises that "prints stay aligned, logos stay placed, texture stays readable" (botika.com). Read both as the same instruction, and apply it to any generator: zoom the neckline, the print and the wash before the image goes live. The [virtual try-on vs product photography explainer](/learn/virtual-try-on-vs-ai-product-photography) covers where each technique breaks, try-on on occlusion and generation on drift. The [same-model guide](/learn/same-model-across-ai-product-photos) covers the identity drift that outlasts a locked face: hands, jewelry and extreme angles. _The same model leans forward in the olive trench coat with black boots against a blue seamless backdrop._ _The same model in the same pose wearing a black sleeveless pleated midi dress and black boots._ _One model, one look, two garments: the coat and the dress remixed into the same scene in Kive._ --- ## Price unit, turnaround and seats Each tool meters a different unit. FASHN sells credits: most tools start at 1 credit per output at 1K and rise to 3 at 4K in Quality mode. Basic is $19 a month for 200 credits and 2 team members, Pro $49 for 750 plus 50 daily credits and 5 members, Agency $99 for 1,500 plus 100 daily and 10 members. Annual billing is priced at 10 months, top-ups are $0.10 a credit, and the API prices per successful result from $0.075 a credit (fashn.ai/pricing and help.fashn.ai, October 2026). Botika sells photos. One credit is one photo and five are a video. Pro is $46 a month billed annually ($55 monthly) for 600 credits a year at 2K, with two retouch rounds and 48-hour fixes; Advanced is $100 billed annually ($120 monthly) for 1,200 credits at 4K, with three rounds and 1-day fixes. Retouch rounds are a yearly-plan feature, and the AI pass itself takes about 15 minutes [[2]](https://botika.com/pricing). Kive sells credits at about 10 per image. Basic is $40 a month, or $30 billed yearly, for 2,000 credits, roughly 200 images, with 2 users; Pro is $100, or $75 yearly, for 5,000 credits and 10 users. Unused credits roll over up to twice the monthly allowance (kive.ai/pricing, October 2026). | | FASHN | Botika | **Kive** | | --- | --- | --- | --- | | Input you supply | Person image plus garment image, or a flat lay for Product to Model | Flat lay, hanger or ghost-mannequin shot | 1–3 photos of the person, 1–4 of the garment | | Where the model identity lives | Face reference attached per generation | Library model chosen per collection, 150+ to pick from | Trained character called by @name | | Entry plan | Basic $19/month, 200 credits | Pro $46/month billed annually, 600 credits a year | Basic $40/month, 2,000 credits | | Metering unit | Credit, most tools from 1 per output | Credit, 1 per photo, 5 per video | Credit, about 10 per image | | Free tier | 10 credits | Free trial | 40 credits | | Human retouching | no | 2 or 3 rounds per photo, yearly plans | no | | Turnaround | Seconds per output on Try-On v1.6 | About 15 minutes, then 48-hour or 1-day fixes | not published | | Team seats | 2 on Basic, 10 on Agency | Team collaboration from Advanced | 2 on Basic, 10 on Pro | | Scene held across garments | Backgrounds from the Elements library | Backgrounds from the Botika library | One look from Ideas, remixed per garment | | Resolution | Up to 4K | 2K on Pro, 4K on Advanced | Upscale to 16 or 24 megapixels | | API | yes, from $0.075 per credit | not documented | MCP connector, no public REST API | _Verified from fashn.ai/pricing, help.fashn.ai, fashn.ai/products/api, botika.com/pricing and kive.ai/pricing, October 19, 2026. "Not documented" means the vendor publishes no such capability, not that it is proven absent._ Divide it out and the entry tiers land at roughly $0.10 a FASHN credit, $0.92 a Botika photo on the annual Pro plan and $0.20 a Kive image. The Botika dollar buys a managed pipeline with retouching behind it. The other two buy a raw generation you iterate yourself, which is cheaper per frame and more expensive per hour if nobody on the team wants to iterate. --- ## What each one cannot do FASHN will not fix an image for you, and nothing in it persists across a catalog on its own: the consistency tools are references you attach per generation, with the 2-credit face surcharge in the Studio. It is metered per output, so a brand that wants one garment in eight settings pays eight times and reviews eight times. That is fine at ten garments and a budgeting exercise at four hundred. Botika will not let you train your own model on the self-serve tiers. Custom models and custom retouching briefs sit in Enterprise, the full model library and team collaboration start at Advanced, and retouch rounds exist only on yearly plans. A service also has a queue rather than a slider: 48 hours on Pro, a day on Advanced, between spotting a warped seam and holding the fixed file. Kive will not retouch for you either; nobody at Kive touches your image. There is no shopper-facing try-on for a product page, and no public REST API for generation, only the MCP connector. Character and product models need a paid plan. If the brief is one garment on one existing model photo, the workspace setup is overhead you do not need, and the [Botika vs Lalaland comparison](/learn/kive-vs-botika-vs-lalaland) covers how that overhead compares with an enterprise 3D pipeline. ### The bottom line These three rarely lose to each other on image quality. They lose on fit: FASHN is the per-output tool, Botika is the service, Kive is the repeatability tool. Buy by the shape of your week, not by the demo reel. - **One garment onto an existing model photo, or try-on inside your own app:** FASHN – A person image plus a garment image returns a try-on from 1 credit, with an API priced per successful result. - **An outsourced catalog delivered retouched:** Botika – Ready models, one credit per photo and two or three human retouch rounds on yearly plans. - **A campaign where one model and one look repeat across many products:** Kive – A trained character, a product model and one look from Ideas hold the person, garment and scene across every frame. --- Count the garments that will share one model this season. If the answer is one or two, pay per output and move on. If it is a collection and nobody in-house will retouch, buy the service and count the rounds. If it is a campaign where the same face, the same light and the same framing have to survive forty products and a video, set the person and the scene up once and let them repeat. Whichever you pick, the garment photo you feed in still decides what comes out. Shoot that one properly. --- ## Sources 1. [Botika homepage]() 2. [Botika pricing]() 3. [FASHN pricing]() 4. [FASHN app subscription plans and credit costs]() 5. [FASHN virtual try-on API]() ## FAQ ### What is the difference between FASHN and Botika? FASHN is self-serve software: an app and an API that put a garment onto a model image, metered from 1 credit per output on a $19 Basic plan. Botika is a managed service: you upload flat lays, pick from its library of ready AI models, and human retouchers fix misses on yearly plans at one credit per photo. ### How much does FASHN cost in 2026? As of October 2026 FASHN starts free with 10 credits. Basic is $19 a month for 200 credits and 2 team members, Pro $49 for 750 plus 50 daily credits, Agency $99 for 1,500 plus 100 daily. Annual billing is priced at 10 months, top-ups cost $0.10 a credit, and the API prices from $0.075 a credit. ### How much does Botika cost in 2026? Botika's Pro plan is $46 a month billed annually, $55 monthly, for 600 credits a year at 2K with two retouch rounds. Advanced is $100 billed annually, $120 monthly, for 1,200 credits at 4K with the full model library and three rounds. A photo is one credit, a video five; Enterprise is priced on request. ### Can I get on-model photos of my clothing from a flat lay without a photoshoot? Yes. All three accept a garment-only capture: FASHN's Product to Model converts a flat-lay or ghost-mannequin photo, Botika takes flat lay, hanger or ghost mannequin shots, and Kive builds a product model from 1–4 photos. The capture sets the ceiling, so shoot it sharp, evenly lit and color-checked before anything else. ### Which tool keeps the same model across a whole clothing store? Each does it differently. FASHN attaches a face reference per generation, 2 extra credits in its Studio. Botika holds one library model across a collection as part of the service. A trained character model, where a tool offers one, repeats the person by name without re-attaching anything; the trade is setup time and a paid plan. ### Do I need a photo of a real person to use these tools? Only for FASHN's try-on endpoints, which need a model image alongside the garment, and for training a character from your own model's photos. Botika supplies the person from its library, and FASHN's Model Create generates one from a prompt. If you upload a real person, you need their permission first.