Great Question vs Maze: prototype testing and beyond

If you're comparing Maze and Great Question, you're probably trying to figure out whether you need a prototype testing tool or a full research platform.

The short version: Maze is fast for unmoderated prototype testing. Great Question does prototype testing plus CRM-based recruitment, focus groups, cross-study AI analysis, a connected repository, and MCP integration. If your research needs start and end with testing, Maze covers it. If you need to turn testing into product decisions at scale, keep reading.

Maze started as a prototype testing tool for designers and has expanded into surveys, card sorting, tree testing, and interviews. Great Question started as a research CRM and expanded into every research method: prototype testing, moderated interviews, focus groups, surveys, card sorting, tree testing, with AI that queries across all of it.

Both tools test prototypes. The difference is what happens before, after, and around the test.

What Maze does well

Paste a Figma prototype link, set up tasks, launch. It supports prototype testing, card sorting, tree testing, surveys, live website testing, and 1:1 moderated interviews with an AI moderator. For teams whose research needs start and end with unmoderated testing, it covers a lot of ground.

But most product teams outgrow that scope quickly. Here's where Great Question picks up.

What Great Question does that Maze doesn't

Great Question connects directly to your CRM so you can recruit the exact customer segment you need. Salesforce, Snowflake, Zapier — pull the power users who actually buy weekly, not strangers who match a demographic. Tag participants by behavior, set participation frequency controls so nobody gets over-surveyed, and distribute incentives without leaving the platform. ServiceNow cut recruitment from 118 days to 6 after connecting their CRM to Great Question.

Then there's focus groups. Maze handles 1:1 interviews. Great Question runs moderated interviews AND focus groups: multiple participants, live, with the same scheduling, recording, and analysis infrastructure. If your team runs any kind of group research, Maze can't cover it.

The biggest gap is AI scope. Great Question's Ask AI lets you query up to 50 hours of transcripts per study: ask for specific quotes, patterns, contradictions, or a custom summary in any format. Every AI-generated quote links back to the original source with timestamps. PII masking is built in. Maze has AI features too: theme detection, transcript highlights, sentiment analysis. But those work within individual studies. Great Question's AI works across studies, so you can ask "what have customers said about checkout friction in the last 6 months?" and get answers grounded in interviews, prototype tests, and surveys combined.

Everything connects in one repository. Run a prototype test, an interview, a survey, a card sort, and a tree test, all in one platform. Findings connect across methods so you spot contradictions and patterns instead of cross-referencing three tools. Brex went from single-digit researchers to 100+ people running research because everything lived in one place. Roller made the same move, with their Head of Product Design noting that Great Question's "AI stuff smashes Dovetail."

For enterprise teams: role-based access, team hierarchies, audit logs. When you have 50+ researchers and compliance requirements, governance isn't optional.

Great Question's MCP integration connects your research data to AI assistants like Claude via the open Model Context Protocol. Your research becomes queryable from wherever you work, not locked inside a single tool's UI.

Head-to-head: prototype testing

Maze built its name on prototype testing. It's what people know them for.

But "good at prototype testing" and "good at learning from prototype testing" are different things.

Test with your actual customers, not a generic panel

Import your Figma prototype, set a goal screen or let participants explore freely, and launch. Standard stuff: both platforms do this. The difference is who you're testing with.

Great Question connects to your CRM so you can pull the exact segment you need. Testing a checkout redesign? Recruit the power users who actually buy weekly, not strangers who match a demographic. Set participation frequency controls so nobody gets over-surveyed, and track who's participated in what.

Watch people think, not just click

Every prototype test in Great Question captures screen recording, audio, and camera simultaneously. Participants think aloud as they navigate, so you hear the hesitation, the confusion, the "wait, where did that go?" moments that click data alone doesn't capture.

You get the quantitative layer too: click maps per screen, success rates, misclick rates, time per screen. But you also get the why behind the numbers, in the participant's own words.

Let AI do the synthesis

After sessions come in, Great Question transcribes everything, in 30+ languages, and identifies themes across participants automatically. Instead of scrubbing through hours of recordings, you get patterns surfaced: "4 out of 6 participants couldn't find the settings icon" with the supporting clips tagged and ready to share.

Then Ask AI lets you query across those sessions and every other study you've run. "Did this same navigation issue come up in last quarter's interviews?" One question, one answer, with source links. Query up to 100 studies at a time, every quote linked back to the original source.

Connect findings to everything else

A prototype test in Great Question lives alongside the interview you ran last week, the survey from last month, the card sort from Q1, and the tree test you're planning next. One repository. When your prototype test reveals that 30% of users can't find the checkout button, you can check whether that matches what came up in customer interviews, without switching tools or cross-referencing spreadsheets.

Pipe research into your AI workflow

Great Question's MCP integration connects your research data to AI assistants like Claude. Ask questions across your entire research history and get answers grounded in actual study data, not summaries someone wrote three quarters ago.

When to choose each

Choose Maze

• Small design team (1-5 people), no research infrastructure
• Prototype testing and unmoderated surveys are 90%+ of your need
• You don't need your own customers, vendor panels work fine
• You don't run focus groups or need cross-study AI analysis

Choose Great Question

• Scaling research across teams (researchers, PMs, designers)
• You need your own customers, not generic panels
• Multiple methods: prototype tests AND interviews AND focus groups AND surveys AND card sorting
• You want to ask AI questions across hundreds of hours of research, not just within one study
• You need enterprise governance, a connected repository, or MCP integration

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FAQ

Does Great Question have a Figma integration?

Yes. Import your Figma prototype directly into a study and launch.

Can I use both?

Some teams use Maze for rapid design validation and Great Question for deeper research and their repository. Most eventually consolidate into one platform.

Does Maze have AI features?

Yes. Maze has AI theme detection, transcript highlights, sentiment analysis, and an AI moderator for interviews. The difference is scope: Maze's AI works within individual studies. Great Question's Ask AI queries across your entire research history.

The bottom line

Maze is a strong unmoderated testing tool. Fast prototype testing, solid analytics, decent breadth of methods.

Great Question is the research platform for product teams that need more than testing. Recruit your own customers, run every method from prototype tests to focus groups, query hundreds of hours of research with AI, and connect it all to your existing tools via MCP. Researchers, PMs, and designers use the same platform, with the governance controls to make that work at scale.

The question isn't which tool tests prototypes faster. It's whether you need a singular testing tool or a research platform.