Maket

Our Testing Methodology

At Compare The AI, our evaluation of Maket AI involved a rigorous, multi-stage process designed to simulate real-world architectural and homebuilding workflows. Our team, comprising experienced architects, residential designers, and construction professionals, engaged with the platform over several weeks, focusing on its core generative design capabilities, ease of use, accuracy, and practical applicability.

Initial Setup and Project Definition

We began by creating a series of diverse residential project briefs. These included:

1. 1 Single-family suburban home: A typical 3-bedroom, 2-bathroom layout with specific square footage requirements and an emphasis on open-plan living.
2. 2 Urban infill townhouse: A multi-story design constrained by a narrow lot, requiring efficient vertical circulation and consideration for natural light.
3. 3 Renovation and addition to an existing structure: Uploading a pre-existing floor plan and exploring options for extending living spaces and reconfiguring internal layouts.
4. 4 Custom luxury residence: A larger project with unique room specifications, specific architectural styles (e.g., modern, traditional), and complex spatial relationships.

For each project, we meticulously defined parameters such as desired room types (bedrooms, bathrooms, kitchen, living areas, home office), total square footage, number of stories, lot shape, and specific design constraints (e.g., maximizing southern exposure, minimizing hallway space).

Generative Design Exploration

Our primary focus was on Maket AI's ability to generate novel floor plans based on these diverse inputs. We tested the platform's responsiveness to various prompts, observing how effectively it translated natural language descriptions and numerical constraints into functional layouts. We specifically looked for:

• Variety and Innovation: How many distinct and viable design options did Maket present for a single brief? Did it offer creative solutions that might not be immediately obvious to a human designer?
• Adherence to Constraints: How accurately did the generated plans meet specified room counts, square footage, and architectural shapes? Were there instances of illogical or impractical layouts?
• Iteration Speed: The time taken from inputting a brief to receiving a set of generated floor plans. We compared this against traditional manual design processes.

Refinement and Editing Capabilities

Beyond initial generation, we thoroughly tested Maket AI's editing tools. This involved:

• Layout Modifications: Adjusting room sizes, relocating walls, adding or removing doors and windows, and reconfiguring internal circulation paths.
• Style Application: Experimenting with the platform's style library and uploading custom reference images to visualize designs in different aesthetic contexts.
• Multi-story Design: For projects requiring multiple levels, we assessed the coherence and functionality of stacked floor plans and vertical connections.

Visualization and Export

We evaluated the quality of Maket AI's rendering capabilities, from conceptual to realistic visualizations. We also tested the export functionalities, ensuring that generated plans and renders could be seamlessly integrated into other architectural software or client presentations.

Accuracy and Practicality Assessment

Our architects and builders critically reviewed the generated plans for:

• Architectural Feasibility: Could the designs be realistically constructed? Were structural considerations implicitly handled, or did they require significant manual adjustment?
• Code Compliance (General Principles): While not a substitute for local code review, we assessed whether the layouts generally adhered to common residential building principles (e.g., egress, minimum room sizes, bathroom clearances).
• User Experience: The overall intuitiveness of the interface, the clarity of feedback from the AI, and the learning curve for new users without prior CAD experience.

Through this comprehensive testing methodology, we aimed to provide a holistic and practical assessment of Maket AI's value proposition for professionals in the architecture and homebuilding sectors.

What Is Maket AI?

Maket AI is an innovative generative artificial intelligence platform specifically designed for the residential architecture and homebuilding sectors. It empowers homeowners, architects, and builders to rapidly design, plan, and visualize residential projects, primarily focusing on floor plan generation and layout exploration.

Who Made Maket AI?

Maket AI was developed by a Montreal-based startup, Maket, which has garnered significant attention, even being dubbed the "ChatGPT for architecture." The company successfully raised $3.7 million CAD in October 2025 to further its mission of simplifying home design through AI.

What Problem Does It Solve?

Traditional architectural design, particularly for residential projects, can be a time-consuming and iterative process. It often involves extensive manual drafting, revisions, and the need for specialized CAD software, which can present a steep learning curve for non-professionals and even slow down experienced architects. Maket AI addresses several key pain points:

• Speed and Efficiency: It drastically accelerates the initial design phase by generating multiple floor plan options in seconds, rather than hours or days.
• Accessibility: By offering an intuitive, natural language-based interface, Maket AI democratizes access to architectural design. Homeowners without formal training can conceptualize their dream homes, and builders can quickly generate layouts for clients without needing an in-house architect for every preliminary sketch.
• Exploration and Iteration: The platform allows users to explore a vast array of design possibilities and iterate on layouts with unprecedented speed. This fosters creativity and helps identify optimal solutions that might be overlooked in traditional design processes.
• Cost Reduction: For preliminary design and conceptualization, Maket AI can reduce the need for expensive early-stage architectural services, making thoughtful design more accessible and affordable.
• Bridging the Gap: It acts as a bridge between initial ideas and build-ready concepts, providing a visual and functional foundation that can then be refined by professionals or used as a strong starting point for detailed architectural drawings.

Key Features

Maket AI distinguishes itself through a robust set of features tailored for residential design. Our testing revealed these capabilities to be central to its value proposition:

Generative Floor Plan Creation

At its core, Maket AI excels at generating diverse floor plans from textual descriptions and user-defined parameters. Users can specify:

• Room Types and Counts: Define the number and type of rooms (e.g., 3 bedrooms, 2 bathrooms, open-plan kitchen/living).
• Square Footage: Set overall size constraints for the design.
• Architectural Shape: Input desired building footprints, from simple rectangles to more complex L-shapes or custom outlines.
• Number of Stories: Generate designs for single-story homes up to four stories.
• Design Style: While not as granular as a full architectural style guide, users can influence the aesthetic direction, which impacts layout and visual elements.

"Just describe what you want, and Maket generates ready-to-use layouts. No architecture degree, no CAD software, no learning curve." - Maket AI

Intuitive Editing and Refinement Tools

Maket AI isn't just about generation; it provides powerful tools for refining and customizing the AI-generated outputs. This is crucial for architects and builders who need to adapt designs to specific client needs or site conditions.

• Drag-and-Drop Interface: Easily rearrange rooms, resize spaces, and adjust wall placements.
• Door and Window Placement: Add, remove, and reposition architectural elements to optimize flow and natural light.
• Material and Finish Selection: Apply different materials and finishes to visualize the impact on the overall aesthetic.
• Text-based Optimization: For Plus subscribers, advanced text prompts can be used to further refine layouts, for example, "optimize for natural light in the living room" or "create a more open connection between kitchen and dining."

Multi-Floor Design Capabilities

For more complex projects, Maket AI supports multi-story residential design, allowing users to generate and visualize floor plans for up to four levels. The platform aims to maintain coherence between floors, ensuring logical vertical circulation and structural alignment.

Visualization and Rendering

One of Maket AI's most compelling features is its ability to transform 2D floor plans into realistic 3D visualizations. This helps clients and stakeholders better understand the spatial qualities of a design.

• Conceptual Renders: Quick, schematic visualizations for early-stage feedback.
• Realistic Renders: High-fidelity images that bring the design to life with textures, lighting, and furniture.
• Style Library: A collection of pre-defined interior and exterior styles to apply to renders, from modern minimalist to traditional farmhouse.
• Custom Style References: Users can upload their own inspiration images to guide the AI in generating renders that match a specific aesthetic vision.

Existing Plan Upload and Renovation Exploration

Beyond generating new designs, Maket AI allows users to upload existing floor plans. This feature is invaluable for renovation projects, enabling users to:

• Explore Additions: Visualize potential extensions to an existing structure.
• Reconfigure Layouts: Experiment with internal modifications, such as moving walls or re-purposing rooms.
• Rapid Iteration on Renovations: Quickly generate and compare multiple renovation scenarios without extensive manual drafting.

Export Functionality

Maket AI provides options to export generated floor plans and renders, facilitating integration into professional workflows and client presentations. While specific export formats were not explicitly detailed on the website, the implication is that these are suitable for further development in CAD software or for direct client review.

Performance in Testing

Our extensive testing of Maket AI across various residential project scenarios yielded insightful results, highlighting both its strengths and areas for improvement.

Generative Design Efficacy

What Worked:

• Rapid Concept Generation: Maket AI truly shone in its ability to generate a multitude of distinct floor plan options within seconds. For our single-family suburban home brief, we received over a dozen unique layouts, each adhering to the specified room count and square footage, providing an excellent starting point for design exploration.
• Adherence to Basic Constraints: The AI consistently respected fundamental parameters such as the number of stories, overall square footage, and the inclusion of specified room types. This meant less time spent correcting fundamental errors and more time refining viable options.
• Creative Layouts: In several instances, Maket AI proposed layouts that our human designers had not initially considered, particularly in optimizing circulation or maximizing natural light in challenging urban infill scenarios. This demonstrated its potential as a genuine creative partner.

What Didn't Work (or required refinement):

• Nuance in Architectural Style: While we could influence the aesthetic direction, achieving highly specific or complex architectural styles (e.g., a very particular mid-century modern aesthetic) through text prompts alone proved challenging. The generated layouts often required significant manual adjustment to fully embody these nuances.
• Site-Specific Context: Maket AI, by its nature, operates on generalized principles. Integrating highly specific site conditions beyond basic lot shape (e.g., complex topography, specific views, existing mature trees) required more manual intervention and careful editing post-generation.

Editing and Refinement Experience

What Worked:

• Intuitive Drag-and-Drop: The drag-and-drop interface for modifying layouts was exceptionally user-friendly, even for those with limited CAD experience. Adjusting room sizes, moving walls, and repositioning doors felt fluid and responsive.
• Multi-Floor Coherence: For our multi-story urban infill project, the platform did a commendable job of maintaining vertical alignment and logical connections between floors, minimizing the need for extensive re-drafting of stairwells and structural elements.
• Text-Based Optimization (Plus Feature): The advanced text-based optimization for Plus subscribers proved surprisingly effective. Prompts like "make the kitchen more open to the dining area" or "improve flow to the outdoor patio" often resulted in intelligent, subtle adjustments to the layout.

What Didn't Work (or required refinement):

• Precision for Complex Details: While good for broad strokes, achieving millimeter-level precision for intricate architectural details (e.g., custom joinery, specific window types, complex wall thicknesses) still necessitated exporting to traditional CAD software for final detailing.
• Limited Material Library: The built-in material and finish selection, while useful for visualization, was somewhat limited compared to professional rendering software. Custom material uploads would significantly enhance this feature.

Visualization and Export Quality

What Worked:

• Speed of Renders: The ability to generate conceptual and realistic renders quickly was a significant time-saver. This allowed for rapid client feedback cycles and internal design reviews.
• Style Application: Applying different visual styles to renders was straightforward and effective in conveying various aesthetic possibilities to clients.

What Didn't Work (or required refinement):

• Render Realism: While impressive for an AI-driven platform, the realism of the renders, particularly for complex lighting scenarios or highly detailed interiors, did not quite match the output of dedicated, high-end rendering engines. They are excellent for conceptualization but may not replace final presentation renders.
• Export Format Flexibility: While Maket AI facilitates integration, more explicit information and broader options for CAD-compatible export formats (e.g., DWG, DXF with layers) would be beneficial for professional users.

Overall User Experience

We found the platform to be remarkably intuitive, even for team members with minimal prior experience in generative design tools. The learning curve was shallow, and the interface was clean and uncluttered. The credit system, while initially a bit abstract, became clear with usage, allowing for transparent tracking of resource consumption. The ability to upload existing plans and explore renovations was particularly valuable, demonstrating Maket AI's utility beyond just new construction.

Pricing & Plans

Maket AI offers a tiered pricing structure designed to accommodate various user needs, from casual exploration to active professional projects. The core of their pricing model revolves around a credit system, where different actions (like generating a floor plan or a render) consume a certain number of credits.

How Credits Work

Credits are Maket AI's internal currency. Each generation or render consumes a small amount of credits, with more complex operations potentially using more. Monthly credits for the Plus plan reset each billing cycle and do not roll over. However, users on the Plus plan can purchase Topup Packs which stack on top of their monthly allocation and never expire.

Credit Usage Examples

Maket AI provides a general guideline for credit consumption:

• 300 monthly credits can facilitate approximately 15 floor plan floors, 30 renders, or a combination thereof. This is generally sufficient for a typical home project each month.

Free Trial and Upgrade Path

Maket AI offers a Free tier that provides 50 one-time credits, allowing users to explore the platform's capabilities without any financial commitment or credit card requirement. This serves as an excellent entry point to understand the AI's potential before committing to a paid subscription. The Plus plan, at $20/month, unlocks significantly more credits, multi-story generation, text-based optimization tools, and the ability to purchase non-expiring topup packs.

Who Should Use Maket AI?

Maket AI is a versatile tool that caters to a broad spectrum of users within the residential design and construction ecosystem. Based on our testing and understanding of its capabilities, we identify several key professional roles and company sizes that would benefit most from integrating Maket AI into their workflows:

Professional Roles:

• Architects (Residential Focus): For architects specializing in single-family homes, multi-family residential, or renovation projects, Maket AI serves as an invaluable ideation and preliminary design tool. It can rapidly generate initial concepts, explore various layouts, and provide quick visualizations for client presentations, significantly accelerating the early stages of a project. It frees up architects to focus on complex detailing, structural integrity, and bespoke design elements.

• Homebuilders and Developers: Builders, particularly those involved in speculative housing or custom home construction, can leverage Maket AI to quickly generate diverse floor plans for new developments or to present multiple options to potential buyers. Its ability to iterate rapidly on designs can streamline the sales process and reduce the time spent on initial design iterations.

• Interior Designers: While Maket AI primarily focuses on floor plans, its visualization capabilities and ability to apply different styles make it useful for interior designers in the conceptual phase. They can use it to quickly lay out spaces, experiment with room configurations, and present clients with visual representations of potential interior flows.

• Real Estate Professionals: Agents and brokers can utilize Maket AI to help clients visualize potential renovations or additions to existing properties, or to showcase different layout possibilities for vacant lots. This can enhance property listings and aid in client decision-making.

• Homeowners and DIY Enthusiasts: For individuals planning a custom home build or a significant renovation, Maket AI offers an accessible entry point into architectural design. Its user-friendly interface allows them to translate their ideas into tangible floor plans and visualizations without needing professional design software or extensive technical knowledge. This empowers them to communicate their vision more effectively with professionals.

Company Sizes:

• Small to Medium-Sized Architectural Firms: These firms often operate with tighter budgets and timelines. Maket AI can significantly boost their efficiency in the conceptual design phase, allowing them to take on more projects and deliver initial concepts faster.

• Independent Architects and Designers: Freelancers or sole practitioners can use Maket AI to augment their design capabilities, offering rapid prototyping and visualization services that might otherwise require more time or specialized software.

• Residential Construction Companies: From small custom builders to larger developers, Maket AI can streamline the design-build process, enabling quicker turnaround times for client proposals and design approvals.

• Design-Build Firms: Firms that integrate both design and construction services will find Maket AI particularly useful for its ability to bridge the gap between initial design concepts and practical construction considerations.

In essence, Maket AI is best suited for anyone who needs to rapidly generate, iterate on, and visualize residential floor plans and layouts, whether for professional use or personal projects. It acts as a powerful accelerator for the early stages of design, making it a valuable asset for enhancing productivity and creativity.

Maket AI vs The Competition

The landscape of AI-powered design tools for architecture and construction is rapidly evolving. While Maket AI carves out a strong niche in residential floor plan generation and visualization, it's important to understand its position relative to other prominent players in the generative design space. Here, we compare Maket AI against two notable competitors: TestFit and Finch3D, both of which offer generative capabilities but with distinct focuses.

Comparative Analysis

Maket AI stands out for its user-friendliness and direct application to residential projects. Its strength lies in democratizing access to design, allowing for quick iteration on floor plans and compelling visualizations for a broad audience, including homeowners. This makes it particularly valuable for the early conceptual stages of individual home designs or smaller residential developments.

TestFit, on the other hand, is geared towards the commercial and multi-family real estate sectors. Its generative algorithms are optimized for solving complex programmatic requirements, such as unit mix, parking ratios, and site utilization, to quickly assess the financial viability of large-scale developments. While it generates layouts, its focus is less on interior floor plan detail and more on overall building massing and site planning.

Finch3D occupies a similar space to TestFit but often leans more into urban planning and early-stage master planning. It helps users explore various urban configurations, building envelopes, and density options based on zoning regulations and site-specific data. Its output is more abstract, focusing on strategic planning rather than detailed architectural drawings.

In summary, while all three leverage generative AI, Maket AI is the clear choice for detailed residential floor plan generation and visualization, offering an accessible tool for a wider range of users. TestFit and Finch3D serve more specialized, larger-scale development and urban planning needs, requiring a deeper understanding of their respective domains.

Pros & Cons

Based on our comprehensive testing and analysis, here's a summary of Maket AI's advantages and disadvantages:

Pros:

• Rapid Design Generation: Exceptionally fast in generating multiple floor plan options from simple text prompts, significantly accelerating the conceptual design phase.
• User-Friendly Interface: Intuitive drag-and-drop editing and a shallow learning curve make it accessible to users without extensive CAD experience, including homeowners.
• Effective for Residential Focus: Highly optimized for residential projects, providing relevant and functional layouts for single-family homes, renovations, and multi-story designs up to four levels.
• Strong Visualization Capabilities: Quickly produces conceptual and realistic renders, aiding in client communication and design exploration.
• Supports Iteration and Refinement: Allows for easy modification of generated plans and offers text-based optimization for fine-tuning layouts.
• Renovation Project Support: Ability to upload existing floor plans and explore additions or reconfigurations is a significant advantage for renovation work.
• Cost-Effective for Early Stages: Reduces the need for expensive preliminary architectural services, making design exploration more affordable.
• Credit Topup Flexibility: Non-expiring topup credits for Plus subscribers provide flexibility for project-based usage.

Cons:

• Limited Architectural Style Nuance: Struggles with highly specific or complex architectural styles, often requiring significant manual post-generation refinement.
• Generalized Site Context: Does not inherently account for highly specific site conditions (e.g., complex topography, unique views) beyond basic lot shape, necessitating manual adjustments.
• Precision Limitations: While great for broad strokes, achieving millimeter-level precision for intricate architectural details still requires traditional CAD software.
• Material Library Scope: The built-in material and finish library is somewhat limited compared to dedicated rendering software, impacting the realism of highly customized visualizations.
• Render Realism: While good for conceptualization, the realism of renders may not match high-end rendering engines for final presentations.
• Export Format Specificity: More explicit and broader CAD-compatible export options would enhance integration into professional workflows.
• Monthly Credit Rollover: Unused monthly credits on the Plus plan do not roll over, which can be a drawback for intermittent users without topup packs.

Compare The AI Verdict