ABrush vs Dazl: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of ABrush and Dazl — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

ABrush

Freemium

AI image generation and editing studio that runs as a panel inside Adobe Photoshop, with 23+ models, ControlNet, LoRA styles and layer-native output.

Photoshop-native panel: Generation, editing and upscaling happen on the open document and land on real layers, with no export-import round trip
23+ models in one panel: Switch between Stable Diffusion, Flux, Qwen Image and others per stage of a piece rather than committing to one provider
Targeted editing: Inpaint or regenerate only the region that needs changing, keeping the rest of the composition untouched
Pro conditioning controls: ControlNet support plus IP-Adapter and reference images for pose, composition and style control
Custom LoRA styles: Load your own LoRA or style models to keep generations consistent with an established look
Generation history: Every generation is saved and recoverable, so artists can return to an earlier variation without regenerating
Shareable presets: Save prompts and settings as presets and share them across a team to reproduce a house style
Commercial-safe data policy: Generated images belong to the user and customer images are not used for model training

A concept artist generating multiple variations of a character directly in the working file and painting over the strongest one
A retoucher fixing a single element of a composite with inpainting rather than regenerating the whole image
A studio distributing a shared preset pack so several artists produce work in a consistent house style
A freelance illustrator using a custom LoRA to keep generated assets on-style with a client's brand
A designer upscaling and cleaning up a low-resolution asset without leaving Photoshop
An agency handling commercial client work that needs assurance the images aren't used for model training

Paid

Early-access platform aimed at product makers (sign-ups open on the official site).

Unified logging interface and configuration format across multiple Go logging backends
Pluggable backend support with adapters for zap and zerolog
Path-like logger naming to establish hierarchical logger relationships
Runtime configuration of individual loggers (enable/disable, set levels)
Inheritance of log levels by descendant loggers for package/module-scoped control
Enables per-package, subpackage, or module-level logging changes via configuration

Standardize logging across a Go codebase that uses different logging libraries
Allow operators to enable debug logging for specific packages or modules at runtime
Swap or migrate logging backends without changing application code
Provide consistent logging configuration for libraries and applications in a large monorepo
Enable end-users or administrators to customize log levels for troubleshooting in production