ABrush vs Solarch: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of ABrush and Solarch — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
ABrush
ABrush
AI image generation and editing studio that runs as a panel inside Adobe Photoshop, with 23+ models, ControlNet, LoRA styles and layer-native output.
Key features
- 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
Best for
- 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
Solarch
Solarch
Visual backend architecture tool: draw node/edge graphs, validate with a rules engine, and generate matching backend code to prevent architectural drift.
Key features
- Live Graph Editor: A node-and-edge visual editor for composing backend architectures (services, data stores, connections) with live feedback and manipulation.
- Rules Engine Validation: Built-in validation rules that check the drawn architecture for policy, best-practice, and safety violations before code generation.
- Automated Code Generation: Generates scaffolded backend code that directly matches the validated architecture, reducing manual translation work.
- Architecture-Code Parity: Keeps diagrams and generated code in sync to prevent architectural drift and ensure the implementation follows the approved design.
- Scaffold Customization: Produces customizable starter code and project structure so teams can iterate from a working baseline rather than from scratch.
- Design-as-Source-of-Truth: Treats architecture diagrams as the authoritative specification, enabling governance, review, and reproducible builds from the visual model.
- Live node/edge graph editor for backend architecture
- Rules engine to validate architecture diagrams against constraints
- Automated code generation that produces backend code matching the diagram
- Prevents architectural drift by keeping design and code in sync
- Visual-to-code workflow enabling design-driven development
Best for
- Designing Microservice Backends: Visually model microservice boundaries, communication paths, and data stores, then generate matching service scaffolds to accelerate implementation.
- Onboarding and Handoff: Provide new engineers with a validated visual architecture plus generated starter code so they can quickly understand and contribute to the system.
- Enforcing Architecture Governance: Apply organizational rules in the validation engine to ensure new designs comply with standards before code is produced.
- Prototype-to-Production Acceleration: Rapidly iterate on architecture diagrams and produce working code prototypes that can be extended into production systems.
- Refactoring and Reconciliation: Use the visual model to plan refactors and produce updated scaffold code that brings implementation back into alignment with the intended architecture.
- Documentation-as-Code: Maintain diagrams as the source of truth and regenerate code or artifacts to keep documentation and implementation synchronized.
- Designing backend system architecture as diagrams and generating initial code scaffolding
- Enforcing architectural constraints across teams via automated validation
- Keeping infrastructure-as-code and implementation consistent with architecture diagrams
- Generating API or service stubs from a validated architecture graph
- Onboarding and documentation through visual architecture artifacts tied to code