Maze is unmoderated usability testing with quantitative metrics. Diaform is AI-led qualitative conversations. They sit in different categories, and most teams need both.
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Side by side
Research method
AI-led qualitative conversations. The AI listens, follows up, and synthesizes, answering the why behind user behavior.
Unmoderated task-based testing. Respondents complete tasks on a prototype or live page; metrics get captured automatically.
Live moderation
Adaptive AI follow-ups (1, 3, or 5 deep) per answer, the closest thing to a moderator without putting one in the room.
Unmoderated by design. No follow-ups; respondents complete the test without anyone (human or AI) intervening live.
Voice answers
Whisper voice input plus a natural ElevenLabs AI voice. Voice answers are typically 2-3× longer than typed responses.
Text-based responses on survey blocks. No conversational voice capture in the moderated-conversation sense.
Open-ended depth
Per-response summaries, sentiment, answer confidence, and optional notable quotes for qualitative review.
Open-text survey blocks exist, but there's no AI follow-up or built-in synthesis for qualitative answers.
Click / heatmap metrics
Diaform captures conversation, not clicks, no heatmaps, misclick maps, or interaction analytics on a prototype.
Misclick heatmaps, click paths, and visual analytics on prototype screens, purpose-built signal for usability decisions.
Prototype testing
Diaform doesn't host or test prototypes. Use Maze for the prototype task, then chain a follow-up conversation here.
Native Figma, Adobe XD, Sketch, and InVision prototype testing, first-class workflow for design validation pre-launch.
Task-completion metrics
No success-rate or time-on-task metrics, it's a conversation tool, not a usability instrument.
Success rate, time on task, mission paths, and direct/indirect success metrics, the quant backbone of unmoderated testing.
Synthesis
Per-conversation summaries, sentiment, answer confidence, and optional notable quotes generated alongside the transcript.
Reports aggregate quantitative results well; qualitative themes still require a researcher to read and code.
Pricing
14-day trial. Starter $49/mo ($40/mo annual), Standard $89/mo ($74/mo annual), Pro $199/mo ($165/mo annual), and Business $499/mo ($415/mo annual). Enterprise is custom.
Free plan with limits, Team ~$99/mo, Organization ~$208/mo (annual). Enterprise tier above that.
Best for
Churn conversations, onboarding feedback, concept tests, JTBD, the qualitative why behind a behavior or metric.
Pre-launch prototype validation, message tests, click tests, quantitative usability signal at scale.
Research method
AI-led qualitative conversations. The AI listens, follows up, and synthesizes, answering the why behind user behavior.
MAZE
Unmoderated task-based testing. Respondents complete tasks on a prototype or live page; metrics get captured automatically.
Live moderation
Adaptive AI follow-ups (1, 3, or 5 deep) per answer, the closest thing to a moderator without putting one in the room.
MAZE
Unmoderated by design. No follow-ups; respondents complete the test without anyone (human or AI) intervening live.
Voice answers
Whisper voice input plus a natural ElevenLabs AI voice. Voice answers are typically 2-3× longer than typed responses.
MAZE
Text-based responses on survey blocks. No conversational voice capture in the moderated-conversation sense.
Open-ended depth
Per-response summaries, sentiment, answer confidence, and optional notable quotes for qualitative review.
MAZE
Open-text survey blocks exist, but there's no AI follow-up or built-in synthesis for qualitative answers.
Click / heatmap metrics
Diaform captures conversation, not clicks, no heatmaps, misclick maps, or interaction analytics on a prototype.
MAZE
Misclick heatmaps, click paths, and visual analytics on prototype screens, purpose-built signal for usability decisions.
Prototype testing
Diaform doesn't host or test prototypes. Use Maze for the prototype task, then chain a follow-up conversation here.
MAZE
Native Figma, Adobe XD, Sketch, and InVision prototype testing, first-class workflow for design validation pre-launch.
Task-completion metrics
No success-rate or time-on-task metrics, it's a conversation tool, not a usability instrument.
MAZE
Success rate, time on task, mission paths, and direct/indirect success metrics, the quant backbone of unmoderated testing.
Synthesis
Per-conversation summaries, sentiment, answer confidence, and optional notable quotes generated alongside the transcript.
MAZE
Reports aggregate quantitative results well; qualitative themes still require a researcher to read and code.
Pricing
14-day trial. Starter $49/mo ($40/mo annual), Standard $89/mo ($74/mo annual), Pro $199/mo ($165/mo annual), and Business $499/mo ($415/mo annual). Enterprise is custom.
MAZE
Free plan with limits, Team ~$99/mo, Organization ~$208/mo (annual). Enterprise tier above that.
Best for
Churn conversations, onboarding feedback, concept tests, JTBD, the qualitative why behind a behavior or metric.
MAZE
Pre-launch prototype validation, message tests, click tests, quantitative usability signal at scale.
Maze is task-based. Surveys are static. There's no AI follow-up when an answer hints at something interesting.
Click data tells you where users got stuck. It rarely tells you why, which is the answer you need before you change anything.
Churn conversations, onboarding feedback, jobs-to-be-done, outside Maze's task-test wheelhouse.
Adaptive probing (1, 3, or 5 deep) on every answer, turns vague responses into specific insight.
Voice answers are 2-3× longer. Whisper input + ElevenLabs voice make it feel like a real conversation.
Per-conversation summaries, sentiment, answer confidence, and optional notable quotes alongside the transcript.
13 topic + 5 sentiment triggers, alert Slack on a churn risk, capture an email, offer a discount mid-conversation.
Upload product context (up to 50k tokens) so the AI can ask grounded questions about your roadmap, pricing, or features.
Run conversations in respondents' native languages, summaries back in English.
Maze is the right pick for prototype testing, click tests, and message tests, anywhere you need a success rate or a heatmap on a defined task. It also fits pre-launch usability checks where you need quantitative validation across a large sample quickly.
Diaform is built for open-ended qualitative work: churn, onboarding feedback, concept tests, and jobs-to-be-done conversations. Use it when you need contextual follow-ups plus summaries, sentiment, answer confidence, optional notable quotes, and a transcript.
The practical details behind setting up, running, and scaling this kind of research with Diaform.
Mostly complementary. Maze owns unmoderated usability and prototype testing; Diaform owns AI-led qualitative conversations. Many teams run both.
Explore the other ways Diaform can turn a static request for feedback into a useful conversation.
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