Introduction
Sprig and Maze answer different research questions. Maze shows whether people can use a design and why they struggle. Sprig measures how many people share a need, which option wins, and whether a metric moved. Choose Maze for usability and interview evidence, and Sprig for measurement. Maze is a user testing and interview platform built on prototype tests, card sorts, tree tests, and an AI Moderator for interviews. Sprig is an enterprise survey platform powered by AI agents: it designs, fields, and synthesizes structured studies for a defined audience. Many product teams use both.
Last updated October 2026.
What is Maze?
Maze is a user research platform that brings participant recruiting, testing, and analysis into one workspace. Product designers and researchers use it to test Figma prototypes, live websites, and native mobile apps, to run card sorts and tree tests, and to interview users with or without a human moderator.
Maze's platform page lists nine methods: moderated interviews, AI Moderator interviews, prototype testing, live website testing, mobile testing, card sorting, tree testing, surveys, and unmoderated think-aloud studies. Results come back as automated reports with visuals, AI-generated themes, transcripts, highlights, and video clips.
Maze's March 2026 market research post says Maze is strongest for fast feedback on concepts and messaging, not for large, complex quantitative market studies or long-running tracking programs. For a full profile, see what Maze is and how it works.
What is Sprig?
Sprig is an enterprise survey platform powered by AI agents. It helps research, product, and marketing teams move from a research question to evidence: design a study, reach the right participants, collect structured responses, and synthesize the results into a report.
Sprig fields studies over email with a custom sending domain, through shareable links and QR codes, and through research panels of 300K+ verified B2B and B2C participants filterable by 300+ attributes. Sprig's August 2026 Surveys release added conjoint and MaxDiff question types, image questions, unbranded surveys, response-based quotas, and native email delivery.
Sprig also runs prototype studies. Teams can embed an interactive prototype in a study, ask participants to complete tasks, and capture screen activity with voice and video responses through the Recorded Task question type.
How do Sprig's Design, Field, and Synthesize Agents work?
Sprig uses three specialized agents, one for each stage of a study. The Design Agent builds the study, the Field Agent runs it with participants, and the Synthesize Agent turns responses into a report. A researcher reviews and approves the study before it launches, and every generated output stays editable.
Design Agent: from research goal to programmed study
The Design Agent turns a research brief, an uploaded survey document, pasted questions, or a plain-language objective into a structured study draft with questions, logic, and sections. Before launch it checks logic for conflicts and dead ends, assesses question clarity, estimates time to complete, simulates responses across custom personas, and translates the study into other languages.
The page states that teams "approve the final study before launch." The agent speeds up programming; the researcher decides what goes live.
Field Agent: conversational delivery with follow-ups
The Field Agent delivers a survey in a chat-style format and asks contextual follow-ups based on each response. It supports question logic based on user attributes and branching by lifecycle stage, plan type, or usage behavior.
Sprig reports "up to 2x improvement in completion rates when moving from static forms to conversational delivery." That is Sprig's own figure, and Sprig does not publish the baseline. Researchers can turn AI follow-ups off per study.
Synthesize Agent: from responses to a report
The Synthesize Agent groups open-text responses into named themes, shows how many responses support each theme, and lets researchers open the underlying responses to check them. It produces executive summaries, key takeaways, and recommendations aligned to the research goal, in formats that can be exported and shared.
Sprig's page states the limit plainly: "AI assists analysis. It does not replace your judgment."
What AI capabilities does Maze offer?
Maze offers AI in four places: the AI Moderator for interviews, the AI Study Builder for setup, Maze AI for analysis, and an MCP server for querying research from AI assistants. Maze's pricing page places the AI Moderator, AI Study Builder, and Maze AI analysis on Enterprise plans, with the AI Moderator sold as an add-on.
The AI Moderator runs interviews from a stated research goal. Maze describes it as probing "deeper when it matters" and running "any time in any language," and says every conversation is evaluated against 25 quality metrics. Outputs are traceable quotes, synthesized themes, and auto-generated reports.
In surveys, Maze AI can generate up to three follow-up questions from an open-text answer, and an AI Conversation block asks voice-based follow-ups. Maze also lists AI question phrasing support, a question quality checker, transcription, interview analysis, summaries, and sentiment analysis.
Does Maze or Sprig have broader AI support?
Maze's AI is broader for interviews, and Sprig's AI is broader across the survey workflow. Maze automates moderation, transcription, and interview analysis, which Sprig does not attempt. Sprig automates study programming, conversational fielding, synthesis, and study creation through MCP, which Maze does not document in the same depth.
The two AI layers are built for different jobs:
| AI job | Maze | Sprig |
|---|
| Draft a study | AI Study Builder (Enterprise) | Design Agent, with logic checks and persona simulation |
| Moderate an interview | AI Moderator (Enterprise add-on) | Not offered |
| Follow up with a respondent | Up to three AI follow-ups; voice follow-ups | Field Agent contextual follow-ups |
| Transcribe and analyze interviews | Yes | Not offered |
| Theme open-text responses | Yes, with sentiment | Yes, with response counts per theme |
| Write a report | Auto-generated reports | Executive summary, takeaways, recommendations |
| Work from an AI assistant | MCP queries existing studies, by request | MCP creates draft studies and queries data |
A team that needs 200 interviews in two weeks will get more from Maze's AI. A team that needs a programmed MaxDiff study fielded to a panel this week will get more from Sprig's. On both platforms, AI output is a draft that a researcher checks against the raw evidence.
Where is Maze the better fit?
Maze is the better fit when the research question is about behavior with a design or the reasons behind it. That covers most evaluative design research: task success on a prototype, where people look for content, and what users say when an interviewer keeps asking why. Sprig does not match Maze on these jobs.
Six situations favor Maze:
- Prototype validation every sprint. Designers test Figma prototypes before engineering builds them, and Maze reports task completion and paths.
- Information architecture work. Card sorts and tree tests show how people group content and where they expect to find it. Sprig does not document either method.
- Interview volume. The AI Moderator runs interviews without a researcher on the call. Sprig runs no moderated or AI-moderated interviews.
- Live website and native app testing. Maze tests production sites and native apps through its Maze Participate app.
- Reasoning as the deliverable. Early concept reactions, where the explanation matters more than the share.
- Designer-led research. Teams spreading evaluative research across designers and product managers who are not trained researchers.
Maze also claims a larger panel, at more than 6 million participants on its homepage, against Sprig's 300K+.
Where is Sprig stronger?
Sprig is stronger when the research question needs a number that holds for a defined audience, repeated over time. That covers prioritization, pricing trade-offs, satisfaction tracking, and segment comparisons, where a stakeholder will ask for the base size and a stable instrument.
Six situations favor Sprig:
- Ranking many options. MaxDiff estimates relative preference across a long list of features or messages. Maze documents ranking questions but not MaxDiff.
- Pricing and bundle trade-offs. Conjoint analysis estimates how attributes and price points drive preference. Maze does not document conjoint.
- Balanced samples. Response-based quotas hold a sample balanced across segments such as plan type or region.
- Tracking over time. Multiple runs of one study keep the wording identical across waves, which is what makes a brand tracking or satisfaction trend comparable.
- Surveying your own customers at scale. Native email delivery with a custom sending domain reaches a customer list without an external tool.
- Agent-driven research workflows. Sprig's MCP connector lets an AI assistant create a draft study for human approval, which Maze's MCP server does not document.
Which claims on Maze's comparison page are out of date?
Several claims about Sprig on Maze's "Maze vs. Sprig" page do not match Sprig's current documentation. The page shows no last-updated date, and its Sprig column appears to describe the product before Sprig's 2026 agent and survey releases. The table checks each claim against Sprig's own pages, retrieved September 28, 2026.
| Claim on Maze's page | What Sprig documents today | Verdict |
|---|
| Sprig captures feedback "primarily through live intercept testing," using surveys and session replays | Sprig fields structured studies by email, link, QR code, and research panel, with MaxDiff and conjoint | Out of date |
| The AI capabilities table lists no AI features for Sprig | Design, Field, and Synthesize Agents (announced April 2026) and MCP study creation | Out of date |
| Sprig's features are surveys, a tester panel, and Figma and Axure integrations | 14 question types, response-based quotas, prototype studies with Recorded Task, email delivery | Incomplete |
| Sprig offers 6M+ vetted panelists | Sprig publishes 300K+ verified participants | Incorrect, and it overstates Sprig |
| Sprig rates 4.5 out of 5 from 100+ G2 reviews | 4.3 out of 5 across 199 reviews on Sprig's G2 product page | Different G2 aggregation |
| Maze's reporting is far stronger than Sprig's | Maze's usability metrics and clips have no Sprig equivalent; Sprig reports survey themes and recommendations | Fair for usability reporting |
| Maze provides broader AI support than Sprig | Broader for interviews; Sprig's agents cover design, fielding, and synthesis | Depends on the job |
| Maze allows "easy rollout and adoption at all levels" | Not independently measured for either vendor | Test in a trial |
The last three rows are not errors. They are claims a buyer should test against their own research questions, which the evaluation checklist below is designed to do.
How do Sprig and Maze compare on reporting depth?
Maze's reporting is deeper for usability studies, and Sprig's reporting is built for survey studies. Maze's comparison page describes reports that combine quantitative usability data, such as time on task and completion, abandonment, and error rates, with behavioral data, in automated, visual, customizable reports. Sprig has no equivalent for usability metrics.
Sprig's Synthesize Agent reports what a survey found: named themes with response counts, traceable responses, an executive summary, takeaways, and recommendations. Through Sprig's MCP connector, teams can pull study data into Claude or ChatGPT for further analysis and charts.
Neither vendor publishes a weighting approach, sample-size guidance, or a significance-testing method. Teams that must defend a difference between segments to a finance audience typically finish that analysis in a statistical package.
Verdict: Maze for usability reports; Sprig for survey reports.
How do Sprig and Maze compare on research methods?
Maze covers more methods, and Sprig goes deeper on structured survey methods. Maze's nine listed methods span usability testing, information architecture, surveys, and interviews. Sprig centers on surveys and adds prototype studies, but it documents question types Maze does not.
Sprig documents 14 question types, including rating scales, open text, matrix, rank order, Net Promoter Score (NPS), video and voice responses, Recorded Task, MaxDiff, and conjoint. Logic, piping, three levels of randomization, quotas, and repeated runs support longitudinal studies.
Maze surveys support opinion scales, multiple choice, multi-select, ranking, yes or no, number inputs, open text, and demographic fields. Maze's survey page does not document MaxDiff, conjoint, quotas, weighting, or significance testing.
The methods overlap in two places. Both run surveys, and both test prototypes. Maze reports task metrics and paths, while Sprig captures screen, voice, and video through Recorded Task alongside survey questions.
Verdict: Maze for method breadth; Sprig for quantitative depth.
How do Sprig and Maze compare on recruitment and deployment channels?
Maze claims the larger panel and adds interview scheduling. Sprig adds native email delivery to a company's own customers. Both support shareable links.
- Maze recruits through its panel, which its homepage describes as more than 6 million participants, through Reach for a team's own participant database, through prompts on a website, and through links. Interview studies connect to calendars and video calls such as Google Meet, Microsoft Teams, and Zoom. Native app tests run in the Maze Participate app.
- Sprig recruits through research panels of 300K+ verified B2B and B2C participants with 300+ targeting attributes, automatic incentives, instant feasibility estimates, and bot detection. It reaches a company's own customers by email with a custom sending domain, and by link or QR code.
Maze does not state on its pricing or panel pages how much of its panel is owned rather than sourced from partners. Buyers with niche B2B targeting needs should ask both vendors for a feasibility estimate on their exact audience.
Verdict: Maze for panel scale and interview scheduling; Sprig for surveying an existing customer list.
How do Sprig and Maze compare on governance and enterprise adoption?
Both publish SOC 2 Type II. Sprig's security page lists more certifications, and Maze routes detail to a compliance portal. For rollout across many teams, the difference is less about certificates than about who controls what goes live.
- Sprig publishes SOC 2 Type II, HIPAA, GDPR, CCPA, and Data Privacy Framework compliance, SAML single sign-on, user roles and permissions, and audit logs kept for at least one year, with hosting on Amazon Web Services in the United States.
- Maze states on its security page that it is SOC 2 Type II certified (Security) and GDPR compliant, encrypts data with AES-256 at rest and TLS 1.2 in transit, and runs on Amazon Web Services. Maze's pricing page places SSO and compliance features on Enterprise plans.
Sprig does not publish SCIM provisioning or regional hosting options. Maze's security page does not state hosting regions or HIPAA. Confirm both in procurement.
On AI governance, Sprig's MCP controls scope access to the user's role, cap each call at 1,000 responses, exclude response data from model training, and let admins revoke all connections at once. Agents cannot launch or change a live study; a human approves every study in the Sprig app.
Verdict: depends on the review. Sprig publishes more controls on its public pages; Maze's comparison page emphasizes ease of rollout for designers and product managers.
How do Sprig and Maze compare on integrations?
Maze's integrations center on design and interview work. Sprig's center on moving research data into the tools and AI assistants a company already uses. Both connect to the major design tools.
- Maze lists Figma, Sketch, Adobe XD, and Axure; AI prototyping tools such as Bolt, Figma Make, Lovable, and Replit; video conferencing apps; and calendars for interview scheduling. Its MCP server connects Claude, ChatGPT, and Cursor to existing studies, with access by request.
- Sprig lists Figma, Sketch, Adobe XD, Axure, and other prototyping tools; Slack, Notion, Dovetail, and Zapier; a public API, a Data Export API, and webhooks; and an MCP connector for Claude, ChatGPT, Gemini, Copilot, and Cursor.
Verdict: Maze for interview logistics and AI prototyping tools; Sprig for data flow and AI assistant access.
How can teams use Sprig and Maze together?
Teams use Sprig and Maze together by routing each question to the tool that produces the right evidence. Maze finds a problem and explains it. Sprig measures how common it is and whether a fix moved it. The router below assigns common questions.
| Question a product team asks | Tool | Evidence type |
|---|
| Can users finish checkout in the new prototype? | Maze prototype test | Behavioral, directional |
| Where do users expect to find billing settings? | Maze tree test | Findability |
| Why do trial users stall during setup? | Maze AI Moderator | Qualitative reasoning |
| How many customers hit the setup problem? | Sprig email survey | Incidence among respondents |
| Which of twelve roadmap features matters most? | Sprig MaxDiff | Relative preference |
| Which plan bundle do buyers prefer? | Sprig conjoint | Trade-off estimate |
| Did satisfaction move after the redesign? | Sprig repeated runs | Tracked measurement |
| Does the new positioning land, and why? | Both | Share from Sprig, reasons from Maze |
Questions that start with "can," "where," and "why" usually belong to Maze. Questions that start with "how many," "which," and "did it move" usually belong to Sprig.
Worked example: from a usability test to a ranked fix list
The figures below are illustrative. A fintech team runs a Maze prototype test of a new onboarding flow with 40 panel participants and finds six friction points. It can fix two this quarter.
- The team writes the six friction points as MaxDiff items in Sprig, using participants' own words from the Maze clips.
- It fields the study by email to 5,000 customers who onboarded in the last 90 days, with quotas balancing free and paid plans.
- Suppose 600 customers respond. The MaxDiff scores rank the six items, and the top two account for most of the preference share.
- The team fixes the top two, then re-runs one Sprig question with identical wording after release to check whether reported friction fell.
The Maze test defined what to measure. The Sprig study decided what to fix. Because respondents select themselves, the ranking describes respondents, not every customer.
How should teams evaluate Sprig and Maze in a trial?
Run the same real question through both tools and compare the output, not the demo. Eight tests settle most evaluations:
- Name the question type. List your last ten research questions and mark each "can, where, why" or "how many, which, did it move."
- Build the same study twice. Time how long each tool takes from goal to launch-ready study, and count the edits needed.
- Check the audience. Ask each vendor for a feasibility estimate on your hardest-to-reach segment.
- Read the report cold. Give each report to a stakeholder who did not run the study, and ask what decision it supports.
- Trace a theme. Open three AI-generated themes in each tool and check them against the raw responses.
- Ask for the base. For any percentage, confirm the population and sample size behind it.
- Check plan gating. List which features you need that sit on Enterprise plans or add-ons in each tool.
- Test AI access. Connect each MCP server to your AI assistant and note what it can read and what it can create.
How do Sprig and Maze compare on independent reviews?
Both vendors are well reviewed, in different G2 categories. Maze held 4.5 out of 5 across 111 reviews in G2's User Research Tools listing as of September 24, 2026. Sprig holds 4.3 out of 5 across 199 reviews on its G2 product page as of September 28, 2026.
Maze's comparison page shows Sprig at 4.5 from 100+ reviews, which matches a narrower seller-level aggregation rather than the product page. Reviewers commonly praise Maze for quick setup and Figma prototype testing, and some report limits on mobile testing and branching logic.
For comparisons with survey platforms that overlap Sprig more closely, see Sprig's comparison hub.
Frequently asked questions
The answers below cover the questions buyers most often ask when comparing Sprig and Maze.
What is the difference between Sprig and Maze?
Maze is a user testing and interview platform that shows whether people can use a design and why they struggle. Sprig is an enterprise survey platform powered by AI agents that measures how many people share a need, which option wins, and whether a metric moved. Maze leads on usability and interviews; Sprig leads on structured measurement.
Is Sprig a Maze alternative?
Sprig is an alternative to Maze only for survey work. For prototype testing, card sorting, tree testing, and interviews, Maze does jobs Sprig does not. For MaxDiff, conjoint, quotas, and tracking, Sprig does jobs Maze does not document. Many teams use both.
Does Maze have better reporting than Sprig?
Maze has deeper reporting for usability studies, with task metrics, visuals, and clips. Sprig's Synthesize Agent reports survey results as named themes with response counts, executive summaries, takeaways, and recommendations. Choose by study type: Maze for usability reports, Sprig for survey reports.
Does Maze or Sprig have better AI?
Neither is better overall. Maze's AI is broader for interviews, with an AI Moderator, transcription, and interview analysis. Sprig's agents are broader across the survey workflow: the Design Agent programs studies, the Field Agent asks follow-ups, and the Synthesize Agent writes reports. Sprig's MCP connector can also create draft studies.
Does Sprig have AI agents?
Yes, Sprig has three: the Design Agent, the Field Agent, and the Synthesize Agent, announced in April 2026. They design a study from a research goal, deliver it conversationally with follow-ups, and synthesize responses into a report. A researcher approves every study before launch.
Does Sprig do usability testing?
Sprig runs prototype studies: teams embed an interactive prototype, assign tasks, and capture screen activity with voice and video responses through Recorded Task. Sprig does not document card sorting, tree testing, or moderated sessions. Teams whose research is mostly usability testing will find Maze covers more of it.
Does Maze support MaxDiff or conjoint analysis?
Maze does not document MaxDiff or conjoint analysis on its survey page. Maze surveys include ranking questions, which order a short list but do not estimate relative preference the way MaxDiff does. Sprig ships MaxDiff and conjoint as question types.
Does Sprig run user interviews?
No, Sprig does not run moderated or AI-moderated interviews. Sprig collects asynchronous video and voice responses inside surveys, and its Field Agent asks follow-up questions, but neither is an interview. Maze's AI Moderator and moderated interview studies cover that job.
Which is better for enterprise rollout?
It depends on who runs research. Maze emphasizes easy adoption for designers and product managers. Sprig publishes SOC 2 Type II, HIPAA, GDPR, CCPA, SAML SSO, role permissions, audit logs, and MCP governance, and requires human approval before any study launches. Confirm SCIM and hosting region with both vendors.
How big are the Sprig and Maze panels?
Maze's homepage claims a panel of more than 6 million participants. Sprig publishes 300K+ verified B2B and B2C participants with 300+ targeting attributes. Neither figure says how many people match a specific audience, so ask both vendors for a feasibility estimate on your segment.
How much do Sprig and Maze cost?
This page does not restate prices, because each vendor's own pricing page is the current source. Maze places the AI Moderator, AI Study Builder, interview studies, card sorting, mobile and live website testing, and SSO on Enterprise plans. Sprig prices by response volume, capabilities, and deployment environments, with no self-serve signup.
Can Sprig and Maze be used together?
Yes, and it is a common pairing. A typical workflow uses a Maze prototype test or interviews to find friction points, a Sprig MaxDiff survey to rank them across customers, and a repeated Sprig survey after release to check whether reported friction fell.
Which should you choose?
Choose by the question, not the feature list. If your open questions start with "can," "where," and "why," Maze covers more of the job. If they start with "how many," "which one," and "did it move," Sprig does.
If your team asks both kinds of questions, run both, and let the Maze study define what the Sprig study measures.
A design team that tests prototypes every sprint and rarely needs a population number may find Maze covers the whole job. A product or research team that ranks roadmaps, tests pricing, and tracks satisfaction across quarters will need a survey platform built for measurement. Start with the router and the eight trial tests above, using questions from your own backlog.
