Introduction
Aytm is an AI-assisted consumer insights platform built around its own respondent panel, a deep method library and an AI assistant called Skipper. Sprig is an enterprise research platform built around AI agents that reaches known users in the product, by email, by link and through panels. Choose Aytm for panel-led consumer research. Choose Sprig when the same team also studies its own product users.
Last updated October 2026.
Why Choosing Between Sprig and Aytm Is a Question About Audience
Aytm and Sprig both sell survey software with AI drafting, respondent panels and crosstab analysis, which makes them look interchangeable on a feature list. They aren’t, because each one is built around a different audience.
For years, Aytm has been one of the more recognizable agile consumer insights platforms. It runs its own respondent panel, says it became one of the first market research platforms to receive ISO/IEC 27001 certification, in 2017, and offers a method library that covers conjoint analysis, Maximum Difference Scaling (MaxDiff), Total Unduplicated Reach and Frequency (TURF) analysis, shelf tests and ad testing.
Brand managers and market researchers at consumer companies have good reasons to shortlist it.
The shortlist question has changed, though. Many teams now run two kinds of research at once. One kind asks strangers about categories, brands, prices and concepts. The other asks known customers about the product they’re using right now. The first needs a panel and a deep method library. The second needs targeting by user attributes and behavioral events, and delivery inside the product.
That’s the real difference between the two platforms. Aytm is organized around reaching a market. Sprig is organized around reaching both a market and the people already using your product. The sections below compare them across eight dimensions and say plainly where each one wins.
Sprig vs. Aytm at a Glance
| Category | Sprig | Aytm |
|---|
| Primary focus | Customer, product and market research | Consumer and market research |
| Respondent sources | Panels, customer email lists, links, QR codes, in-product | Own panel, partner panels, own-list links |
| In-product surveys | Web and mobile apps | Not described in the pages reviewed |
| Method depth | Conjoint, MaxDiff, Van Westendorp | Conjoint, MaxDiff, TURF, Kano, shelf tests, Van Konan |
| AI assistant | Design, Field, Synthesize and Analyze agents | Skipper Draft, Translate, Explore, Autocode, Conversation AI |
| Analysis | Open-text themes, Analyze Agent (preview) | Banner tables, significance testing, SPSS and PowerPoint exports |
| Security | SOC 2 Type II, HIPAA, GDPR, SAML single sign-on | ISO/IEC 27001, 42001, 20252 and 27701, GDPR |
| Research services | Software with enterprise onboarding and support | Do-it-yourself, assisted and full-service options |
| Pricing | Doesn't publish prices, book a demo | Lite (self-serve, per project) or custom Enterprise subscription, no prices shown |
| G2 rating (retrieved October 14, 2026) | 4.3 out of 5 across 199 reviews | 4.4 out of 5 across 56 reviews |
Which Platform Should You Choose?
Choose Sprig if
- You study your own users and want surveys triggered by events and attributes inside a web or mobile product
- You want in-product, email, link and panel studies in one workspace, with AI agents that draft, field and synthesize
- You want to query survey responses from Claude, ChatGPT or other assistants through governed access
- You want to tie a survey response to what the respondent did, using session replay around the answer
Choose Aytm if
- You research consumers outside your customer base and want a large owned panel with deep psychographic targeting
- You need methods like TURF analysis, Kano analysis, shelf tests or choice-based conjoint with service support
- You test ad creative, concepts or packaging before launch
- You need certifications such as ISO 20252 and ISO 42001 on file for a procurement review
Where Each Platform Falls Short
A fair comparison states limits as plainly as strengths. These are the gaps a buying committee should know about before a demo.
Sprig's Limitations
- Sprig doesn’t publish TURF analysis, Kano analysis, shelf tests, menu tests or an ad testing simulator
- Sprig’s panel page describes panel scale in different terms across surfaces, and it doesn’t publish psychographic profile counts
- Sprig’s Analyze Agent is labeled as in preview, and Sprig’s reviewed pages don’t describe weighting
- Sprig hosts in the United States and doesn’t document SCIM provisioning or ISO 42001
- Sprig’s pricing page doesn’t describe managed fieldwork or full-service research
Aytm's Limitations
- Aytm’s reviewed pages don’t describe event-triggered in-product surveys or session replay
- Aytm’s reviewed pages name no application programming interface, assistant connector or third-party integrations
- Aytm’s dashboard page doesn’t name the significance tests it runs or mention weighting
- Aytm’s help center lists Skipper Autocode and verbatim translation as paid add-ons, and a list survey carries a technology fee
- Aytm’s target market page focuses on consumer demographics and doesn’t address business audiences, though its Sample Engine page describes specialty panels
Each limit is a question to ask in a demo, not a verdict. Sprig’s gaps matter most to consumer brands and method-heavy research teams. Aytm’s gaps matter most to product teams that research their own users.
What Are Sprig and Aytm?
Aytm's Overview
Aytm calls itself an AI-assisted insights platform. It combines a survey authoring tool, a respondent engine called Sample Engine, an insights dashboard and its Skipper assistant. Its proprietary panel, PaidViewpoint, anchors a respondent community that Aytm says includes more than 100 million people. Aytm says its customers include companies in consumer packaged goods, technology, finance, healthcare and retail.
Aytm’s homepage also introduces The Bridge, which it calls an "AI-native insights operating system." This guide doesn’t evaluate The Bridge, because Aytm publishes no documentation for it that could be reviewed.
Sprig's Overview
Sprig is an enterprise research platform built around AI agents. The Design Agent drafts a study from a research goal or an uploaded document. The Field Agent generates follow-up questions as responses arrive. The Synthesize Agent turns responses into themes and reports, and the Analyze Agent runs statistical comparisons between groups.
Sprig delivers studies in web and mobile products, by email, by link and QR code, and through research panels.
Two Different Starting Points
Aytm’s organizing unit is the panel project: choose a target market, field a survey and read the results. Sprig’s organizing unit is a study connected to a product. The same study can go to customers inside the app, to a customer email list, or to a panel when the audience is outside the customer base.
Where Both Platforms Overlap
Both platforms offer AI-assisted survey drafting, translation support, theme detection for open-ended responses, crosstab-style comparison between groups, demographic quotas and enterprise security programs. The differences sit in who each platform can reach, how deep the method library goes, and how the results travel afterward.
Survey Creation: How Sprig and Aytm Compare
Survey creation decides how long it takes to get from a research question to a study that’s ready to field, and how much programming control the researcher keeps. Both platforms let teams build surveys by hand and with AI help, but they differ in how much logic they expose and where the AI starts.
Survey Creation at a Glance
| Category | Sprig | Aytm |
|---|
| Starting point | Design Agent drafts from a goal or uploaded document | Skipper Draft builds a survey from your objectives |
| Question types | 14 documented, including rating scale, NPS, matrix, rank order, MaxDiff, conjoint | Includes matrix, open-ended, heatmap, video, conjoint, MaxDiff, shelf test |
| Logic | Skip, display, response piping, attribute piping | Group logic, Smart Loops, masking, piping, scoring |
| Randomization | Options, questions, pages | Not named on the page reviewed |
| Quotas | Enterprise plan | Custom quota logic and demographic quota groups |
Aytm's Survey Authoring Tool
Aytm’s authoring tool names an unusually deep logic set, and Aytm’s plan comparison marks its advanced logic as unavailable on the Lite plan. Group Logic and nested group logic inside Smart Loops let a researcher repeat a block of questions for each item a respondent selected. Masking, piping and advanced operators such as arrays and scoring let a programmer shape complex routing.
Custom screening logic and custom quota logic sit beside them.
For teams with mature research operations, that’s a real advantage. A researcher who wants to program a multi-stage concept screen can do it without leaving the tool.
Sprig's Design Agent and Question Library
Sprig documents 14 question types, including rating scale, Net Promoter Score (NPS), matrix, rank order, MaxDiff and conjoint. Video and voice responses and a recorded task that captures screen, voice and video during a prototype test are also documented. Logic includes skip, display and piping of both responses and user attributes, with randomization of response options, questions and pages.
The Design Agent starts earlier in the process. It drafts a programmed study from a research goal or an uploaded document, flags broken or conflicting logic, estimates completion time and can simulate how different personas would answer. Persona simulation checks question wording and routing. It isn’t evidence of how real respondents would answer. Researchers remain responsible for reviewing the study before launch.
Multilingual Studies
Aytm’s help center says researchers can field in more than 30 languages across more than 50 countries, and Skipper Translate prepares survey text for other languages from the survey editor. Sprig’s Design Agent page describes AI translations, while its documentation describes a translation file that a team downloads and completes for each language. Teams running many languages should test the translation workflow on a real study before committing.
Where Each Approach Fits
Aytm gives a programmer more routing primitives. Sprig’s Design Agent gives a product manager or marketer a drafted study with logic checks, for the researcher to review.
Survey Creation Verdict
Winner: Depends on how much programming control you need
Both platforms support AI-drafted surveys, piping, quotas and conjoint and MaxDiff question types. Sprig places more emphasis on agent-led drafting and logic checking, which can reduce the manual configuration before launch.
Aytm exposes a deeper logic toolkit, which suits research teams with dedicated programmers and complex multi-stage designs. A team of programmers building multi-stage designs should shortlist Aytm, and a product or marketing team that wants a drafted, logic-checked study should shortlist Sprig.
AI Capabilities: How Sprig and Aytm Compare
AI shows up in four places across the research lifecycle: drafting the study, running it, analyzing open-ended responses, and connecting results to other tools. The platforms invest in different places.
AI Capabilities at a Glance
| Category | Sprig | Aytm |
|---|
| Drafting | Design Agent | Skipper Draft, with modify |
| Fielding | Field Agent follow-up questions | Conversation AI question type |
| Open-ended analysis | Synthesize Agent themes | Skipper Autocode (paid add-on) |
| Statistical summaries | Analyze Agent (preview) | Skipper Explore |
| Assistant access | Model Context Protocol (MCP) server | Not named on the pages reviewed |
Aytm's Skipper
Skipper is Aytm’s AI assistant. Skipper Draft writes or modifies a survey. Skipper Translate prepares survey text for other languages. Skipper Explore writes editable notes on the results page that summarize significant differences. Skipper Autocode finds themes in open-ended responses and builds a codebook. Aytm’s help center says Autocode and verbatim translation are paid add-ons and says coding runs are always triggered by the user.
Conversation AI is a separate question type. Respondents chat with Skipper about talking points the researcher defines, which Aytm describes as conversational qualitative research at quantitative scale. It’s an AI-moderated conversation inside a survey, so this guide treats it as a feature of a survey platform rather than a replacement for one. The guide on AI-moderated interviews versus surveys covers when each instrument fits.
Sprig's AI Agents
Sprig applies agents across the lifecycle. The Field Agent generates follow-up questions in real time based on what a respondent just said, and teams can turn that off for any study. The Synthesize Agent produces themes, summaries and supporting quotes as responses arrive. The Analyze Agent compares groups with crosstabs, t-tests and analysis of variance, though Sprig’s own page labels it as in preview.
Sprig’s Model Context Protocol server connects Claude, ChatGPT, Gemini, Copilot and Cursor to survey data. According to Sprig’s data security post, it enforces the same role-based permissions as the Sprig app, returns up to 1,000 responses per call, doesn’t train on responses and has an org-wide admin kill switch. An agent can’t push a study live or change a running study.
Where AI Still Needs a Researcher
AI speeds up drafting and coding, but it doesn’t replace research judgment. A drafted survey can carry leading wording, and a coded theme can merge two distinct complaints. Both vendors say as much: Aytm tells researchers to review and modify Skipper’s drafts, and Sprig keeps a human approval step before a study created through its assistant access goes live.
AI Capabilities Verdict
Winner: Tie
Both platforms offer AI for drafting, translation support, open-ended analysis and statistical summaries, and Aytm holds an ISO 42001 certification for its AI management system. Sprig’s differentiator is that its agents act during fielding and its governed assistant access lets teams query responses from tools they already use.
Aytm’s differentiator is Conversation AI, which brings open conversation into a survey. Teams that want assistant access to survey data lean toward Sprig, and teams that want conversational probing inside a panel study lean toward Aytm.
Survey Distribution and Participant Recruitment: How Sprig and Aytm Compare
The Aytm panel and Sprig’s in-product software development kit (SDK) reach different people, so decide which people you need first.
Distribution at a Glance
| Category | Sprig | Aytm |
|---|
| Panel | Research panels, B2B and B2C | Proprietary panel plus partner panels |
| Panel size | Sprig's page cites its own participant figures | Aytm cites more than 100 million respondents |
| Targeting | 300+ demographic, professional, behavioral and firmographic attributes | Demographics, geography, 2,000+ psychographic profiles |
| Own customers | Email with custom sending domain, links, QR codes | Link and embed code from a list survey |
| In-product | Web, iOS, Android, React Native, Flutter | Not described in the pages reviewed |
Aytm's Panel and Sample Engine
Aytm’s panel is its center of gravity. Its pages cite a community of more than 100 million respondents across roughly 50 countries by Aytm’s own count, with more than 2,000 psychographic profiles available for targeting. A Sample Complexity Meter shows availability, demand and turnaround as a researcher changes the audience, and the Data Centrifuge runs automated checks for speeding, straight-lining and open-ended response quality.
Researchers can set up to two nested and six non-nested demographic quota groups, order 50 to 10,000 completes, and request a custom quote for narrow audiences. Aytm’s Sample Engine page also describes specialty panels for senior executives, information technology decision-makers and healthcare professionals.
For consumer research on categories, brands and purchase behavior, that’s a strong combination. Sprig doesn’t publish a comparable psychographic profile library, and its own pages describe panel scale in different terms across surfaces, so this guide doesn’t compare headcounts.
Data Quality Controls
Aytm’s Data Centrifuge checks for response patterns, speeding, straight-lining and the quality of open-ended and image responses. Sprig’s panel page names bot detection and prevention of repeat submissions. Aytm names more checks in the pages reviewed, and neither vendor publishes a rejection rate.
Sprig's Panels, Email and In-Product Delivery
Sprig recruits business-to-business (B2B) and business-to-consumer (B2C) participants from inside the platform. Its panel page describes more than 300 targeting attributes, automatic participant incentives, built-in quotas and screening, and bot detection.
The larger difference is what sits beside the panel. Sprig sends surveys by email from a custom sending domain, by link and QR code, and inside web and mobile products. In-product studies can trigger on specific events or user attributes, such as a completed onboarding step or a plan type, and session replay shows up to five minutes before and after a response.
Aytm's List Surveys
Aytm also supports surveys to your own contacts. A list survey produces a link and embed code, and Aytm states that it doesn’t send emails on a customer’s behalf, so teams distribute the link through their own email tool. Incentive management carries an extra cost. In the pages reviewed, Aytm describes sharing a link or embedding a survey in a web page, but it doesn’t describe event-triggered in-product surveys.
Which Audience Needs Which Channel
| Research question | Reach | Better fit |
|---|
| How do consumers in a category choose between brands? | Strangers | Aytm |
| Which feature should we build next? | Existing users | Sprig |
| How do buyers react to this ad? | Strangers | Aytm |
| Why did users abandon onboarding? | Existing users, in context | Sprig |
| Would a new segment pay for this concept? | Strangers, then users | Both |
The rule behind the table is simple: use a panel when you need responses from people outside your customer base, and use in-product or email delivery when you need responses from people you already know.
Distribution Verdict
Winner: Depends on who you need to reach
Both platforms can recruit panel respondents and send surveys to a customer list. Aytm provides greater panel scale and deeper psychographic targeting for consumer audiences, making it well suited for teams whose questions center on markets and categories. Sprig provides in-product delivery with event and attribute triggers, which Aytm’s reviewed pages don’t describe, making it the stronger fit for product and customer research.
Advanced Methods and Integrations: How Sprig and Aytm Compare
Advanced methods decide whether a platform can answer questions about price, trade-offs and portfolio design with defensible numbers. Integrations decide whether those numbers reach the tools a team already uses. Aytm leads the first half of this chapter and Sprig leads the second.
Methods and Integrations at a Glance
| Category | Sprig | Aytm |
|---|
| Conjoint | First-party question type | Choice-based conjoint, plus line optimization as a service |
| MaxDiff | First-party question type | MaxDiff, plus anchored MaxDiff as a service |
| TURF | Not published | Listed |
| Pricing research | Van Westendorp via template and Analyze Agent | Van Konan, monadic price and Van Westendorp |
| Concept and creative testing | Concept and prototype testing | Concept Lab, shelf test, menu test, Ad Testing Simulator |
| Assistant integrations | Claude, ChatGPT, Gemini, Copilot, Cursor | Not named on the pages reviewed |
Aytm's Method Library
Aytm’s solutions pages list choice-based conjoint, MaxDiff, TURF analysis, Kano analysis, key drivers analysis, shelf tests, menu tests and Van Konan, its extension of the Van Westendorp price model. Monadic and sequential monadic designs are both named.
Aytm’s plan comparison marks the Conjoint and TURF simulators and qualitative questions as unavailable on the Lite plan, so these methods sit with the Enterprise subscription. Several methods are sold as service solutions that Aytm says are set up in days, among them anchored MaxDiff, Kano analysis, key drivers analysis and line optimization conjoint.
The Ad Testing Simulator places a creative in a simulated Instagram, Facebook or TikTok feed and records views, dwell time, likes, shares and banner interactions. For a consumer brand testing creative before spending media budget, that’s a capability Sprig doesn’t publish.
Sprig's Methods
Sprig supports conjoint and MaxDiff as first-party question types, and depending on plan and configuration, teams can run both inside a study. Sprig doesn’t publish TURF analysis. Its Analyze Agent lists Van Westendorp price sensitivity among its analyses, and Sprig’s documentation describes how to analyze MaxDiff data with a sequential best-worst logit model and bootstrapped confidence intervals, and conjoint data with a conditional logit model that reports willingness to pay.
Those analysis recipes live in the guides on MaxDiff analysis with Claude and conjoint analysis with Claude. They help a team verify an estimate. They don’t make up for method breadth, since a team that needs TURF, Kano or a shelf test has to run them elsewhere.
Integrations and AI Workflows
Sprig connects survey data to the rest of a team’s stack through an application programming interface (API), webhooks and native connections such as Slack, Amplitude and Mixpanel, and through its Model Context Protocol server.
Aytm’s Info for AI page names no third-party integrations and says only that data can be integrated into workflows. Its reviewed dashboard page names no API or assistant connector. Aytm does export raw data to CSV, Excel, SPSS and JMP, and results to PowerPoint.
Methods and Integrations Verdict
Winner: Aytm
Both platforms offer conjoint and MaxDiff, which covers the two methods most pricing and feature-prioritization projects need. Aytm adds TURF, Kano, shelf tests and ad testing, and sells several as supported services, so Aytm is well suited for consumer teams running portfolio, packaging or creative studies.
Sprig pairs a smaller method set with documented assistant access and a published API and webhook layer, which suits product teams that want survey data flowing into the tools they already use. Sprig leads on integrations, and Aytm leads on methods by a wider margin.
Analysis and Reporting: How Sprig and Aytm Compare
Aytm’s banner tables and Sprig’s Analyze Agent take different routes to the same job: showing whether two groups answered differently.
Analysis at a Glance
| Category | Sprig | Aytm |
|---|
| Crosstabs | Analyze Agent crosstabs (preview) | Banner tables with adjustable confidence levels |
| Significance testing | t-tests and analysis of variance, preview | Real-time significance testing, tests not named |
| Open-ended analysis | Synthesize Agent themes | Skipper Autocode codebooks |
| Exports | Raw response export, MCP and API access | CSV, Excel, SPSS, JMP, PowerPoint |
| Reports | AI study reports | Insights dashboard, editable Explore notes |
Aytm's Insights Dashboard
Aytm’s dashboard produces banner tables, its name for crosstab analysis, and lets a researcher adjust primary and secondary confidence levels. Significance testing runs in real time for radio buttons, checkboxes and matrix sub-questions, and results export as a chart. Aytm’s exports reach SPSS and JMP, which matters to teams whose analysts work in statistical software, and PowerPoint exports can carry a brand’s colors.
The reviewed dashboard page doesn’t name the statistical tests it runs, and it doesn’t mention weighting. Teams that need a specific test or a weighting scheme should confirm both with Aytm.
Sprig's Synthesis and Analyze Agents
Sprig’s Synthesize Agent builds an evidence-backed report as responses arrive, with themes, summaries and supporting quotes. The Analyze Agent adds crosstabs, t-tests, analysis of variance and correlation, and calculates with the open-source Python libraries Pandas and SciPy. Sprig’s own page labels the Analyze Agent as in preview, so teams should treat it as newer than Aytm’s banner tables.
Sprig’s response-level analysis also ties to behavior. A team can open the session replay around a survey response and see what the respondent did before and after answering.
Open-Ended Response Analysis
Both platforms code open-ended responses with AI. Aytm’s Skipper Autocode identifies themes, generates a codebook and can apply an existing codebook, but coding runs only when a user triggers them, and Autocode is a paid add-on. Sprig’s Synthesize Agent groups responses into themes as they arrive and keeps supporting quotes attached.
Researchers on either platform should read a sample of raw responses against the themes before reporting them, since a theme can merge two distinct complaints.
Analysis Gaps to Plan Around
Aytm’s reviewed pages don’t describe behavior-linked analysis of the kind session replay provides. Sprig’s reviewed pages don’t describe weighting, and its Analyze Agent is still in preview. Teams that depend on weighting or a named significance test should confirm both with each vendor before buying.
Analysis and Reporting Verdict
Winner: Aytm
Both platforms cover open-ended theme analysis and group comparison. Aytm’s banner tables, real-time significance testing and SPSS and JMP exports are mature and documented, while Sprig’s Analyze Agent is newer. Sprig adds behavior-linked analysis and assistant access, so product teams that work with in-product responses may still prefer it. For a market research team that lives in crosstabs and statistical software, Aytm is the stronger fit today.
Enterprise Security, Governance and Administration: How Sprig and Aytm Compare
Aytm’s data security page names no SOC 2 report, and Sprig’s security page names ISO/IEC 27001 only in connection with Amazon’s documentation. A procurement form that requires either one will separate the two platforms quickly.
Security at a Glance
| Category | Sprig | Aytm |
|---|
| Information security | SOC 2 Type II, AWS-hosted | ISO/IEC 27001, covering its DIY platform and PaidViewpoint |
| AI governance | No ISO 42001 published | ISO/IEC 42001 certified (announced June 2025) |
| Research quality | Not published | ISO 20252 listed |
| Privacy | GDPR, CCPA, HIPAA, Data Privacy Framework | GDPR and California privacy law adherence, ISO/IEC 27701 listed |
| Single sign-on | SAML | Not named on the page reviewed |
| Hosting | AWS facilities in the USA | Not named on the page reviewed |
Aytm's Certifications
Aytm’s data security page lists ISO/IEC 27001 for information security, ISO/IEC 42001 for AI management systems, ISO 20252 for market, opinion and social research, and ISO/IEC 27701 for privacy information management. It names Mastermind, an accredited certification body, as the auditor. ISO 20252 is the one most specific to research, since it covers independently verified research and sampling methods.
The page doesn’t name SOC 2, single sign-on, audit logs or a hosting region. That doesn’t mean Aytm lacks them, only that procurement teams should ask.
Sprig's Security Program
Sprig holds SOC 2 Type II and publishes compliance for HIPAA, the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA) and the Data Privacy Framework. It supports single sign-on (SSO) through Security Assertion Markup Language (SAML) with providers including Okta and Microsoft Entra ID, role-based permissions and audit logging kept for at least one year.
Sprig hosts on Amazon Web Services facilities in the United States and doesn’t document a regional hosting option, which can matter to buyers with data residency requirements. Sprig also doesn’t document System for Cross-domain Identity Management (SCIM) provisioning, names ISO/IEC 27001 only in connection with Amazon’s documentation rather than as a Sprig certification, and doesn’t publish ISO/IEC 42001.
Security Verdict
Winner: Depends on which certifications your procurement team requires
Both platforms provide the controls large organizations expect, including GDPR programs and documented data protection. Aytm holds a broader set of ISO certifications, including the research-specific ISO 20252 and ISO 42001 for AI. Sprig publishes SOC 2 Type II and HIPAA, which Aytm’s reviewed page doesn’t name.
Aytm is the better fit for a team whose checklist asks for ISO 42001 or ISO 20252. Sprig is the better fit for a team whose checklist asks for SOC 2 Type II or HIPAA, which Aytm’s reviewed page doesn’t name.
Implementation, Switching and Cost Model: How Sprig and Aytm Compare
Most teams comparing these two platforms won’t choose one and delete the other. They’ll decide which studies move, which stay and how the work gets paid for.
Implementation at a Glance
| Category | Sprig | Aytm |
|---|
| Getting started | Enterprise onboarding and support included | Lite self-service plan sold per project, or custom Enterprise subscription with dedicated onboarding |
| In-product setup | One-time install of a software development kit (SDK), then researchers adjust targeting | Not described in the pages reviewed |
| Data export | Raw response export, API and webhooks | CSV, Excel, SPSS, JMP |
| Add-on costs | Priced by volume, capabilities and environments | Autocode and verbatim translation are paid add-ons, list surveys carry a technology fee |
Running Both Platforms Together
The most common pattern is a pairing, not a replacement. Picture a consumer subscription app that wants to launch a new tier. The team uses Aytm to run a pricing and feature study among category buyers who don’t use the app, using conjoint or Van Konan to find the bundle that wins.
It then takes the winning bundle into Sprig and asks current members, inside the app and right after a workout, how they’d react. The first study says whether the market wants the tier. The second says whether the existing base does. The scenario is illustrative.
Moving Studies From Aytm to Sprig
A switch usually covers one team’s survey work rather than the whole method library. Use this sequence:
- List active and recurring studies, and mark each by method.
- Keep any study that depends on TURF, Kano, shelf tests, menu tests or ad testing with Aytm, because Sprig doesn’t publish those methods.
- Rebuild questionnaire and logic studies in Sprig, using the Design Agent to draft from the existing document.
- Export historical results from Aytm as CSV, Excel or SPSS files so past data stays available.
- Run one overlapping wave before ending any tracker, because a change of panel or channel changes who answers.
How Each Platform Packages Cost
Aytm’s plans page describes Lite, an independent self-service plan sold per project, and a custom Enterprise subscription with dedicated onboarding and account management. It displays no prices. Its comparison marks advanced logic, qualitative questions, the Conjoint and TURF simulators and dedicated support as unavailable on Lite.
Sprig describes one enterprise offering whose cost scales with total response volume, activated research capabilities and deployment environments. Neither platform’s pricing is quoted in this guide, and total cost depends on volume, methods, audiences and services.
Implementation Verdict
Winner: Depends on whether you buy by the project or by the program
Both platforms support a managed start, and neither makes a team choose all-or-nothing. Aytm’s Lite plan suits teams that run a few studies a year and want to start without a subscription, though the advanced methods this guide credits to Aytm sit on its Enterprise plan. Sprig suits teams that plan continuous in-product research and want one program across channels.
Which Platform Is Right for Your Team?
Market Research Teams
Aytm fits a brief like "which of these four concepts wins among category buyers," which needs a panel, quotas and a concept test. Sprig fits a brief like "what do our current customers think of this change," which needs responses from known users.
Brand and Marketing Teams
Aytm fits pre-launch work on ad creative, packaging, naming and claims, where its Ad Testing Simulator and shelf tests apply. Sprig fits message testing against your own customers and prospects, run next to the product behavior that follows.
Product Management Teams
Sprig fits a question asked right after an event, such as a user finishing onboarding, with session replay around the answer. Aytm fits sizing demand among people who haven’t used the product yet.
User Research and User Experience Teams
Sprig fits studies that combine surveys, recorded prototype tasks and replay, and teams that want to query responses from an assistant. Aytm fits recruits larger than a customer list can supply, especially consumer audiences.
Research Operations and Security Teams
Sprig fits operations teams that want one program across in-product, email and panel studies, with SAML single sign-on and role-based permissions. Aytm fits operations teams that need ISO 20252, ISO 42001 or ISO/IEC 27701 on file.
Which Platform Fits Different Organizations?
| Organization type | Recommended platform | Why |
|---|
| Consumer brand testing creative and packaging | Aytm | Ad Testing Simulator, shelf tests and a large consumer panel |
| Software company researching its own users | Sprig | Event-triggered in-product surveys and session replay |
| Market research team running TURF or Kano studies | Aytm | Both methods are listed, and several run as services |
| Product team that wants survey data in Claude or ChatGPT | Sprig | Governed Model Context Protocol access |
| Team that needs ISO 42001 or ISO 20252 on file | Aytm | Both certifications are listed on Aytm's data security page |
| Program that needs both outside and inside perspectives | Both | Panel studies for the market, in-product studies for users |
The Bigger Strategic Question
Count the studies your team ran in the last year. If most were category, brand or concept studies among strangers, start with Aytm. If most were about your own product and customers, start with Sprig. If the count is close to even, expect to use both and plan the handoff, using the pairing described earlier.
Frequently Asked Questions
What Is the Difference Between Sprig and Aytm?
Aytm is an AI-assisted consumer insights platform built around its own respondent panel and a deep method library. Sprig is an enterprise research platform built around AI agents that delivers studies in products, by email, by link and through panels. Aytm is organized around reaching a market, and Sprig is organized around reaching both a market and a product’s users.
Is Sprig an Aytm Alternative?
Sprig is an alternative for teams whose research centers on their own customers and product, because it covers surveys, panels and in-product delivery. It isn’t a full replacement for Aytm’s consumer method library, which includes Total Unduplicated Reach and Frequency (TURF) analysis, Kano analysis, shelf tests and ad testing. Many teams run both.
Is Aytm Good for Market Research?
Yes, Aytm is a strong fit for consumer market research. It runs a proprietary panel called PaidViewpoint, says its community includes more than 100 million respondents, offers psychographic targeting and lists methods like conjoint, Total Unduplicated Reach and Frequency (TURF) analysis and shelf testing. Sprig supports panel studies, but its strengths sit in product and customer research.
Which Is Better for In-Product Research?
Sprig is better for in-product research. Sprig delivers surveys inside web and mobile products, triggers them on events and user attributes, and connects responses to session replay.
Does Aytm Support In-Product Surveys?
Aytm’s published pages, reviewed on October 14, 2026, don’t describe event-triggered in-product surveys. They describe sharing a survey link or embedding a survey in a web page. That’s not proof the capability is missing, so teams that need in-product triggers should ask Aytm directly.
Does Aytm Have a Research Panel?
Yes, Aytm operates PaidViewpoint, a proprietary panel, and says its community includes more than 100 million respondents across roughly 50 countries. Researchers can also bring their own list through a list survey, which produces a link and embed code the team distributes itself.
Does Sprig Have a Research Panel?
Yes, Sprig supports research panels for business-to-business and business-to-consumer audiences, with more than 300 targeting attributes, automatic incentives, quotas and bot detection. Sprig also sends surveys to customer lists by email and link. Sprig’s own pages describe panel size in different terms, so confirm current figures in a demo.
Does Sprig Support TURF Analysis?
Sprig doesn’t publish Total Unduplicated Reach and Frequency (TURF) analysis, while Aytm lists it among its methods. Teams that need TURF can run it with Aytm or an analysis tool and use Sprig for the in-product and customer research around it.
Does Sprig Support Conjoint Analysis and MaxDiff?
Yes, Sprig supports conjoint and Maximum Difference Scaling (MaxDiff) as question types, depending on plan and configuration, and documents how to analyze both with Claude. Aytm also supports both, and sells anchored MaxDiff and line optimization conjoint as services.
How Does Skipper Compare With Sprig's AI Agents?
Skipper is Aytm’s assistant for drafting, translating, summarizing results and coding open-ended responses, with Conversation AI as a separate question type. Sprig’s agents cover design, fielding, synthesis and analysis, and its Model Context Protocol server gives assistants governed access to responses. Both keep the researcher responsible for the final result.
Is Aytm's Conversation AI an Interview Platform?
Conversation AI is a question type inside a survey, where respondents chat with Skipper about topics the researcher defines. This guide treats it as a feature of a survey platform. Teams choosing between interviews and surveys can read the guide on when to run an AI-moderated interview versus a survey.
Which Platform Has Stronger Security Certifications?
Aytm and Sprig hold different security certifications. Aytm lists ISO/IEC 27001, 42001, 20252 and 27701. Sprig holds SOC 2 Type II and publishes Health Insurance Portability and Accountability Act (HIPAA), General Data Protection Regulation (GDPR) and California privacy compliance.
Sprig doesn’t publish ISO 42001 or System for Cross-domain Identity Management (SCIM) provisioning, and it hosts in the United States. Confirm each requirement with both vendors.
Can Sprig and Aytm Work Together?
Yes. A common pattern uses Aytm for outside-in category and pricing studies and Sprig for in-product studies with existing users. The two platforms don’t share data automatically, so teams export results from Aytm as CSV, Excel or SPSS files and compare them with Sprig results during analysis.
How Do Sprig and Aytm Price Their Platforms?
Neither platform shows prices on its plans page, and this guide doesn’t quote any. Aytm describes a Lite plan sold per project and a custom Enterprise subscription. Sprig describes one enterprise offering whose cost scales with response volume, activated capabilities and deployment environments. Ask each vendor for a quote based on the studies you plan to run.
Which Platform Should My Organization Choose?
Choose Aytm if your questions are mostly about consumers outside your customer base and you need its panel, its method library and its ISO certifications. Choose Sprig if your questions are mostly about your own product and customers and you want in-product delivery, event targeting and governed assistant access to responses. If you ask both kinds of questions, use each where it’s strongest and plan one clean handoff between them.
