Instant background removal for design & creativity workflows
remove.bg is an AI-driven background removal service that extracts subjects from images automatically; it's ideal for designers, e-commerce teams, and marketers who need fast, pay-as-you-go cutouts. The tool offers a limited free preview, pay-per-image credits and subscription packs, and integrations like Photoshop and Figma, making it practical for both occasional users and teams who require high-volume automated masking at predictable prices.
remove.bg is an AI image background removal tool that automatically isolates people, products and other subjects from photos. Its primary capability is one-click background extraction using a trained segmentation model, producing transparent PNGs and JPGs for designers and e-commerce images. The key differentiator is a focused, API- and desktop-integrated workflow (Photoshop, Figma, API) that scales from single images to bulk jobs. remove.bg serves graphic designers, online sellers and marketing teams who need clean cutouts quickly. Pricing is accessible with a free preview and pay-as-you-go credit bundles plus monthly plans.
remove.bg is a specialized AI service for removing backgrounds from images, launched as a focused solution for non-technical users and teams that need reliable subject extraction. Founded to simplify a common creative workflow, the product positions itself between consumer editing apps and developer APIs, offering browser, desktop, plugin and API access. Its core value proposition is automated, high-quality subject segmentation without manual masking. That lets users skip layer-by-layer tracing and export ready-to-use transparent PNGs or images with new backgrounds, saving time across repetitive tasks.
The product delivers several concrete features: automated background removal that produces transparent PNGs and JPG output with preserved subject edges and hair detail; a bulk upload and batch processing capability that accepts ZIP uploads and folders so you can process dozens to hundreds of images in one job; an API with per-image or per-pixel pricing that supports base64 uploads and URL inputs for developers to integrate into e-commerce stores and automation; and desktop plugins for Adobe Photoshop and Figma which let designers remove backgrounds in-place without leaving their design environment. remove.bg also provides options to replace backgrounds with solid colors, transparent layers, or user-supplied images, and exposes parameters (size choices and format) so you control output resolution.
Pricing is split between a free preview and paid credit bundles or subscription plans. The free tier offers a single small preview download per image (low-resolution preview with watermark removed for viewing), while paid plans run as credit-based bundles or monthly subscriptions—credit packs (one-off) and monthly subscription options are available on the website. Subscription tiers start with moderate monthly credit packages for individual users, scaling to larger monthly packs and an enterprise option with custom quotas and SLA. Pay-as-you-go credit pricing and monthly plans are both displayed on remove.bg’s pricing page, and teams can buy larger bundles for bulk work or use the API with a separate enterprise contract for high-volume automated pipelines.
remove.bg is used by independent graphic designers removing product or model backgrounds for compositing, and by e-commerce managers automating catalog image cleanup. For example, a product photographer can process 500 catalog images per session to generate transparent PNGs for listings, and a marketing manager can prepare hero images for ads with background replacement, saving hours of manual masking. Agencies and small retailers commonly use the Photoshop plugin to remove backgrounds directly inside mockups. Compared to a general photo editor like Adobe Photoshop, remove.bg focuses strictly on segmentation and pipeline automation, while competitors such as PhotoRoom or Adobe have broader editing toolsets.
Three capabilities that set remove.bg apart from its nearest competitors.
Current tiers and what you get at each price point. Verified against the vendor's pricing page.
| Plan | Price | What you get | Best for |
|---|---|---|---|
| Free | Free | One low-resolution preview per image; not for high-res exports | Testing single images or evaluating quality |
| Credit Packs | Pay-as-you-go (e.g., credits from €0.14/image approximated) | Buy credits in bundles, use until depleted; exact per-image cost varies | Occasional users who process images irregularly |
| Subscription | Monthly plans (eg. small to large monthly credit bundles) | Monthly credits that renew; higher tiers lower per-image cost | Regular users with predictable monthly volume |
| Enterprise | Custom | Custom quotas, API SLA, invoice billing and priority support | High-volume businesses and platform integrations |
Copy these into remove.bg as-is. Each targets a different high-value workflow.
Role: You are remove.bg and your task is to produce marketplace-ready files from one product image. Constraints: remove the background accurately while preserving product edges, textures and labels; produce two outputs: a transparent PNG and a white-background JPG; longest side must be 2000px, sRGB color profile, no color correction beyond preserving original colors; center the subject on canvas with 5% padding; filename pattern must be {SKU}_transparent.png and {SKU}_white.jpg. Output format: return downloadable URLs for both files and a one-line JSON summary containing file names, dimensions and file sizes. Example: input product_ABC123.jpg => outputs ABC123_transparent.png and ABC123_white.jpg.
Role: You are remove.bg producing a social media-ready cutout for one portrait image. Constraints: remove background and retain fine hair detail; output a single transparent PNG sized to exactly 1080x1350 portrait, center the subject, add a subtle soft drop shadow (20% opacity, 10px offset) and 8% canvas padding; do not upscale beyond the original resolution; keep colors unchanged. Output format: provide one downloadable PNG and a one-line metadata JSON with final dimensions, padding percentage and background type. Example: input story_photo.jpg -> output story_photo_1080x1350.png with metadata.
Role: You are remove.bg configured for an e-commerce batch pipeline. Constraints: accept a CSV input listing up to 500 images (columns: sku,image_url,view); for each image remove background, align the product center-bottom on a 1500x1500 canvas with white background (#FFFFFF), apply a natural drop shadow, and apply a mild global fill (+5% brightness max) for consistency; do not remove packaging labels or text. Output format: produce a ZIP of PNGs named {sku}_{view}.png and a CSV manifest mapping original_url, output_url, width, height and file_size. Example CSV row: ABC123,https://...,front -> ABC123_front.png.
Role: You are remove.bg acting as a professional photographer's batch processor. Constraints: process a folder of 200+ catalog shots, use hair-refinement mode to preserve fine edges of hair and fabric, output two variants per file: (A) transparent PNG with preserved layer mask and (B) print-ready JPG at 300 dpi, sRGB, with longest side 3000px; anchor a soft natural shadow at the subject contact point and preserve original colors and EXIF orientation. Output format: deliver a single ZIP with subfolders /png/ and /jpg/ and a CSV mapping original filename to png/jpg output filenames plus original capture date. Example: model_001.CR2 -> model_001.png and model_001_300dpi.jpg.
Role: You are remove.bg production assistant for a creative director building hero composites. Step 1: remove backgrounds from supplied subject images, preserving hair and semi-transparent areas. Step 2: composite each subject onto the provided background asset with these constraints: subject height must equal 55% of canvas height, place on left third, apply a -3° perspective tilt and add a separate shadow layer (soft edge, adjustable opacity). Step 3: perform a subtle color-match: apply a global color temperature shift up to ±150K and up to 3% contrast blend to visually match provided examples. Output format: a layered PSD per composite with layers named Subject, Mask, Shadow, ColorAdjustment and a 2-line JSON with PSD URL and recommended manual tweak notes. Examples: subj1.png + bg_hero.jpg -> hero_subj1.psd; subj2.png + bg_prod.jpg -> hero_subj2.psd.
Role: You are remove.bg implementing an automated QA pipeline for product cutouts. Multi-step: A) remove backgrounds for the provided batch. B) compute per-image mask quality metrics: edge accuracy percentage, hair transparency score, and isolated-segments count. C) if edge accuracy <95% OR hair score <90% OR segments >3, re-run using 'fine' mask settings; if reprocessed image still fails thresholds mark for manual review. Constraints: cap reprocessing to two attempts per image. Output format: JSON report listing each image with original_url, final_output_url, metrics, status ('ok','reprocessed','manual_review'), 200x200 thumbnail URL, and a summary object with totals and failure reasons. Examples: product1.jpg -> ok; product2.jpg -> manual_review (hair score 72%).
Choose remove.bg over PhotoRoom if you prioritize per-image API credits and Photoshop/Figma plugin workflow for developer or design integrations.
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