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CleanSlop vs Montage — The runtime for agentic user interfaces: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of CleanSlop and Montage — The runtime for agentic user interfaces — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

CleanSlop logo

CleanSlop

CleanSlop

Freemium

Scans vibe-coded apps for launch-blocking issues like leaked keys and data exposure, and reports what to fix first, right from your coding agent.

Key features

  • Agent-Native Scanning: Tell Claude Code, Codex, Cursor or other agents to scan with CleanSlop and fix findings where you build.
  • System-Level Code Mapping: Maps routes, background jobs, data and external services so each finding follows a real call path.
  • Prioritized Reports: Findings grouped as needs attention, worth a look and minor, worst first.
  • Evidence for Every Finding: Shows where each issue lives in your code and why it was flagged.
  • Plain-English Fixes: Explains what is wrong, why it matters and what to do about it.
  • Copy for LLM: Hands any report to your AI tool so it can apply the fixes.
  • GitHub App Integration: Scan only the repositories you select, read-only access.
  • Expert Triage Calls: Book a 15-minute call with a CleanSlop engineer to walk through findings.

Best for

  • A solo founder checks a Next.js and Supabase app built with an AI coding assistant before launching.
  • A developer discovers an exposed AI API key bundled into browser code and moves it to a server route.
  • A side-project builder verifies that one user's data cannot be read by another through database policies.
  • A Claude Code user runs a scan from the terminal and has the agent fix the top findings.
  • A team lead gets a short prioritized list instead of hundreds of linter warnings.
  • An indie hacker books an expert triage call to confirm an ambiguous finding.
View CleanSlop details
M

Montage — The runtime for agentic user interfaces

Montage

Freemium

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
View Montage — The runtime for agentic user interfaces details