Maze

1. Multiple user research methods
   Supports common research methods including prototype testing, website testing, surveys, card sorting, tree testing, five-second tests, and interviews.
2. Participant recruitment and management
   You can use your own users or recruit qualified participants through Maze’s global participant pool.
3. Automated analysis reports
   After a test is completed, Maze automatically organizes success rates, task completion times, user paths, and heatmaps, then generates shareable research results.
4. AI-assisted research
   AI can help refine questions, organize open-ended responses, identify themes, and generate summaries, while final insights still require human judgment.
5. Figma integration
   Import Figma prototypes directly for testing without rebuilding the design as a separate testing experience.
6. Team collaboration
   Supports shared studies, comments, report viewing, and permission management, making it easier for design, product, and research teams to work together.

I ran a complete Maze test using a Figma prototype.

The setup was simpler than expected. Import the prototype, add tasks and questions, choose participants, and publish. Once the results come back, there is no need to rebuild everything manually in a spreadsheet.

The automatic reporting is the biggest time-saver.

Maze directly organizes metrics such as how many participants completed a task, how long they took on average, and where users clicked most often. In traditional usability testing, this data often has to be compiled manually. With Maze, much of it is available almost immediately after the test ends.

But it mainly tells you what happened, not necessarily why it happened.

For example, if the success rate suddenly drops at one step, Maze can make that anomaly easy to spot. Understanding why users got stuck still requires watching recordings, reading responses, or even running follow-up interviews.

The AI features are similar.

They can summarize open-ended responses and flag questions that may be leading. That is useful, but with complex feedback, a human still needs to interpret the context.

The limitation of the free plan is straightforward: you can only run one study per month.

That is enough to evaluate whether Maze fits your team, but once you start using it as part of an active product iteration cycle, one study per month is usually not enough.

One thing I liked is that different research methods are not just renamed versions of the same workflow.

When you create a tree test or card sort, the interface, guidance, and setup flow change accordingly. Even if you have not formally studied UX Research, the templates give you a reasonable sense of how each method should be configured.

Pros and Cons

Pros

• The workflow is centralized. Recruitment, testing, analysis, and reporting can largely happen without constantly switching tools.
• Automatic reports save time. Success rates, completion times, user paths, and heatmaps can be generated directly.
• The Figma workflow is smooth. Once the design is ready, it is easy to move quickly into validation.
• It supports a broad range of research methods. From simple surveys to tree testing, card sorting, and interviews.
• Non-researchers can use it. It is especially approachable for product managers and designers.

Cons

• Starter is not cheap. Around $100 per month can be a significant cost for individuals and small teams.
• The free plan is very limited. One study per month makes it feel more like a trial tier.
• AI only assists with analysis. It can organize results, but it does not replace real user insight.
• Research design still requires skill. A simple tool does not make good research questions easy to design.

Who It’s For / Who It’s Not For

Best for:

• Product managers and designers. Useful when there is no dedicated researcher but continuous validation is still needed.
• Fast-moving product teams. Well suited to teams releasing frequently and needing ongoing user feedback.
• Growing SaaS teams. A good fit for teams that are starting to invest in user research but do not want to build a complex research stack.
• Teams testing Figma prototypes. The transition from design to validation is relatively smooth.

Less suitable for:

• Individuals or small teams with limited budgets. The free tier runs out quickly, and Starter is not inexpensive.
• People who only need simple surveys. Tools like Google Forms or Typeform are more direct.
• Companies with mature research teams and established toolchains. Maze’s all-in-one advantage may matter less.
• Anyone expecting AI to make product decisions automatically. The data can be organized automatically, but the interpretation and decisions still need to come from people.

The most practical thing about Maze is not that it “automates” user research, but that it makes many of the traditionally cumbersome steps much lighter.

Import a Figma prototype, set up the tasks, let participants run through the test, and then review success rates, user paths, heatmaps, and recordings in one place. Teams do not need to build a complicated research process before they can start validating a product.

Maze still does not automatically answer the question, “Why did the user behave this way?” The real analysis, interpretation, and product decisions still need to come from people.

A sensible approach is to take one real feature that is currently in development and run a free test first. If the data from Maze genuinely helps the team identify issues faster, then it makes sense to consider Starter.

In user research, the tool’s job is to put the evidence on the table. Understanding what that evidence means is still a human responsibility.