Cutout.pro vs Laguna by Poolside: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Cutout.pro and Laguna by Poolside — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Cutout.pro
Cutout.pro
All-in-one visual design platform for AI-powered photo and video editing, background removal, restoration, and content generation.
Key features
- Background Removal: Automatic one‑click background removal with high-accuracy subject masks and support for batch uploads to speed product photography and compositing workflows.
- Image Restoration & Inpainting: Tools to repair old or damaged photos, remove scratches and blemishes, and intelligently inpaint missing areas to recover image quality.
- Image Upscaling & Enhancement: AI-driven upscaling to increase resolution while reducing artifacts and preserving detail for print and high-resolution displays.
- Content Generation & Graphic Templates: AI-assisted image generation, stylization, and ready-made design templates for marketing assets, social media posts, and thumbnails.
- Video Editing Tools: Automated video processing features (e.g., background handling and frame restoration) to streamline video content preparation and enhancement.
- APIs and Developer Tools: REST APIs and SDKs for background removal, upscaling, and enhancement that enable integration into apps, pipelines, and automation scripts.
- Desktop & Web Workflow Support: Web-based editor plus a downloadable desktop application (Windows) for local processing and integration with online services.
- Batch Processing & Automation: Bulk processing capabilities and programmatic access to automate repetitive editing tasks and integrate into production pipelines.
- Automatic background removal / cutout
- Image restoration and enhancement (old photo repair, noise reduction)
- Image upscaling / super-resolution
- Graphic asset / content generation tools
- Basic video editing tools (AI-assisted)
- Public API for programmatic access to image processing endpoints
- Desktop application (Windows) and references to mobile integration
- Sample client implementations: Python scripts (requests), Android sample using Retrofit2, MVVM and Hilt
- Web-based UI for one-click processing and bulk operations
Best for
- E-commerce Photo Preparation: Remove backgrounds and batch-process product photos to create consistent, marketplace-ready images quickly.
- Photo Restoration Projects: Restore and repair old family photos or archival images by removing scratches, repairing damaged regions, and recovering detail.
- Marketing Asset Production: Generate stylized images, thumbnails, and social media visuals using templates and AI generation to accelerate campaign creation.
- Image Upscaling for Print and Web: Enlarge low-resolution images for print materials, large-format displays, or high-resolution web use while preserving detail.
- Developer Integration: Integrate background removal and enhancement APIs into SaaS platforms, mobile apps, or automated content pipelines to provide on-demand editing services.
- Video Frame Enhancement: Improve video quality by applying frame-level restoration and background processing to produce cleaner footage for creators and editors.
- E-commerce product photo background removal and batch processing
- Restoration and enhancement of old or low-quality images
- Upscaling images for print or high-resolution displays
- Automated creation of marketing graphics and visual assets
- Integrating automated image enhancement into mobile or server workflows via API
Laguna by Poolside
Poolside
Poolside's family of open Mixture-of-Experts foundation models for agentic coding — XS.2 runs locally, M.1 reaches 72.5% on SWE-bench Verified.
Key features
- Two Model Sizes: Laguna XS.2 (33B total / 3B active) and Laguna M.1 (225B total / 23B active) target different latency and capability needs.
- Mixture-of-Experts Architecture: Routes each token through a subset of experts for efficiency at large scale.
- Local Deployment: XS.2 is small enough to run on a Mac with 36 GB of RAM via Ollama under an Apache 2.0 license.
- Strong SWE-bench Results: XS.2 hits 68.2% and M.1 reaches 72.5% on SWE-bench Verified.
- Bundled Coding Agent: Ships 'pool,' a lightweight terminal-based coding agent.
- Agent Client Protocol: Includes a dual ACP client-server used internally for agent RL training and evaluation.
Best for
- Local Agentic Coding: Running XS.2 on a laptop for private, offline code generation and editing.
- High-Capability Code Tasks: Using M.1 for harder, long-horizon software engineering work.
- Self-Hosted Deployments: Building on open weights to avoid third-party API dependencies.
- Research & Fine-Tuning: Adapting permissively licensed weights for custom coding workflows.
- Benchmarking: Evaluating agentic coding performance against SWE-bench Verified and Pro.