ABrush vs Montage — The runtime for agentic user interfaces: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of ABrush and Montage — The runtime for agentic user interfaces — 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
M
Montage — The runtime for agentic user interfaces
Montage
A model-agnostic SDK that renders agentic, interactive UIs, hydrates artifacts faster, and reduces token usage across underlying models.
Key features
- Model-agnostic SDK: A single SDK and runtime that works with any underlying model provider, enabling developers to render agentic UI components without locking into a specific LLM.
- Agentic UI Rendering: Renders rich, interactive components (agentic widgets) that encapsulate agent behavior, state, and UI in a reusable format for web applications.
- Faster Hydration: Optimized client/server hydration flow to initialize interactive artifacts quickly, reducing perceived latency for end users.
- Token Usage Reduction: Built-in optimizations for prompt/state management that cut token usage across models, lowering operational costs and improving efficiency.
- Stateful Artifact Management: Manages and persists the state and lifecycle of interactive artifacts and their events, simplifying developer handling of agent-driven UI.
- Seamless Front-end Integration: Designed to integrate with common front-end workflows and frameworks, allowing easy embedding of agentic interfaces into existing apps.
- Model Swapping and Fallbacks: Enables switching or combining different underlying models without reauthoring UI logic, supporting fallbacks and provider-agnostic strategies.
- Developer Tooling: Provides developer-oriented APIs and runtime controls to debug, test, and tune agentic components and their interactions.
- Single SDK to render rich, interactive agentic UI components
- Runtime that hydrates interactive artifacts faster
- Reduces token usage across underlying models
- Model-agnostic operation that works with any underlying model
- Focus on rendering agentic interfaces and interactive components
Best for
- Embedding conversational, agent-driven widgets in web apps to handle complex, interactive user workflows without rebuilding front-end UI each time.
- Prototyping agentic interfaces quickly using the single SDK to iterate on UI/agent behavior while swapping underlying models for evaluation.
- Reducing LLM operational costs by leveraging Montage’s prompt and state optimizations to cut token usage across responses and background context.
- Hydrating server-rendered interactive artifacts on the client for faster startup and seamless handoff from server logic to agent-driven interactions.
- Integrating multiple model providers and failover strategies so applications can route requests to different models without changing UI code.
- Managing long-lived, stateful agent interactions (e.g., multi-step assistants or tool-using agents) with built-in lifecycle and state management.
- Build interactive agentic user interfaces with a single SDK
- Embed agent-driven UI components into applications while minimizing token costs
- Hydrate and rehydrate interactive artifacts quickly for responsive UX
- Integrate agentic UI rendering on top of different underlying language models