Sprig vs. Forsta: Which Enterprise Survey Platform Is Right for Your Organization? (2026)

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Introduction

Sprig and Forsta are both enterprise survey platforms, but they fit different operating models. Choose Sprig if you want AI agents to design, field, and synthesize studies, need product and market research in one place, and want research adopted beyond a central insights team. Choose Forsta, now part of Qualtrics following a $6.75 billion acquisition that closed in May 2026, if you run a mature, centralized research operation with highly customized, long-running programs. Both support advanced methods and enterprise governance. The real difference is workflow speed versus configuration depth.

Key takeaways

Sprig fits speed and cross-functional adoption; Forsta fits centralized, highly customized research and is now part of Qualtrics. The five points below summarize the decision.

Sprig is AI-native; Forsta is AI-added. Sprig runs three research agents (Design, Field, Synthesize) across the study lifecycle. Forsta layers AI onto a platform built from Confirmit, FocusVision, and Dapresy.
Forsta is now part of Qualtrics. Qualtrics closed its $6.75 billion acquisition of Press Ganey Forsta on May 18, 2026, which is worth weighing if roadmap stability or integration direction matters to you.
Both do advanced quant. Conjoint, MaxDiff, monadic testing, and pricing research run on either platform. Sprig pairs them with AI-assisted design; Forsta with specialist survey programming.
Both do in-product feedback. Forsta offers digital feedback through website intercepts and a mobile SDK. Sprig is built around continuous in-product discovery as a primary workflow.
The decision is operating model, not company size. Cross-functional, speed-driven teams tend toward Sprig; centralized insights teams with deep customization tend toward Forsta.

How do Sprig and Forsta compare at a glance?

Sprig and Forsta differ most on speed and adoption versus customization and depth, not on whether a given capability exists. Both platforms support enterprise surveys, native email delivery, advanced methods, and enterprise governance. The table below compares how each one handles the research workflow.

Is Forsta still an independent platform, or part of Qualtrics now?

Forsta is now part of Qualtrics. Qualtrics closed its $6.75 billion acquisition of Press Ganey Forsta on May 18, 2026, combining Qualtrics's experience-management platform with Press Ganey Forsta's research and healthcare-experience datasets. Forsta continues to exist as a named product, but buyers evaluating it in 2026 are effectively evaluating a platform inside the Qualtrics portfolio.

This matters for a platform decision in two practical ways. First, roadmap and integration direction for an acquired product can shift during portfolio consolidation, so teams making a multi-year commitment should ask direct questions about continuity. Second, organizations that specifically want an alternative to the Qualtrics ecosystem should factor the new ownership into their shortlist. None of this makes Forsta a weaker platform. It is a material fact that a current comparison should state plainly rather than omit.

What is the difference between Sprig and Forsta?

The core difference is workflow philosophy. Forsta is a comprehensive experience-management and market-research platform built for centralized insights teams that value customization and configurability. Sprig is an AI-native enterprise survey platform built to move a team from research question to defensible evidence quickly, with AI agents handling much of the design, fielding, and analysis work.

Both platforms collect customer feedback and turn it into decisions, and both were engineered for sophisticated enterprise research. Sprig and Forsta diverge on who does the work and how fast. Forsta assumes trained researchers or survey programmers configure studies and reporting to exact specifications. Sprig assumes researchers, product managers, marketers, and customer experience teams all need to run rigorous studies without specialized programming expertise, and uses agents to reduce the manual steps in between.

What is Sprig?

Sprig is an enterprise survey platform powered by AI agents. Sprig helps teams design studies, reach the right participants, collect responses, analyze results, and share defensible insights from one platform. Sprig distributes across in-product, email, link, and panel channels.

Three specialized agents work across the research lifecycle. The Design Agent clarifies the objective, recommends a methodology, and drafts unbiased questions and logic. The Field Agent handles targeting, quota management, and response-quality monitoring during fielding. The Synthesize Agent runs thematic analysis, summarizes responses, compares segments, and drafts reports.

Sprig is built for cross-functional use, so product managers, UX researchers, marketers, and customer experience teams can run studies alongside dedicated researchers. Available capabilities include advanced methods such as conjoint, MaxDiff, and Gabor-Granger pricing, plus MCP and API access for agent-driven research automation.

What is Forsta?

Forsta is an enterprise experience-management and market-research platform. Forsta was formed in 2021 by combining Confirmit, FocusVision, and Dapresy. Forsta carries decades of experience across customer experience, employee experience, market research, and online research communities, and is now part of Qualtrics as of May 2026.

Forsta's strength is depth and configurability for mature research operations. Forsta supports complex questionnaire logic, longitudinal and global tracking studies, multilingual research, panel and community management, and detailed reporting environments. Forsta offers digital feedback for websites and mobile apps through intercepts and a mobile SDK. Because of this breadth, Forsta typically involves more implementation effort and specialized expertise, and many Forsta organizations run dedicated research operations or survey-programming teams to build studies and maintain reporting infrastructure.

How do Sprig and Forsta differ in philosophy?

The two platforms optimize for different priorities: Sprig for speed and broad adoption, Forsta for customization and configurability. Neither priority is inherently better. The right fit depends on whether your organization runs research through a central specialist team or wants many teams gathering evidence directly.

Which platform is better for advanced quantitative research?

Both platforms support advanced quantitative research; the difference is who can run it. Conjoint analysis, MaxDiff, monadic and sequential monadic testing, matrix and ranking questions, quotas, and randomization run on either platform. Sprig pairs these methods with AI-assisted study design so non-specialists can field them. Forsta exposes them through survey programming built for experienced researchers.

Each method below has its own section covering what it measures, when to use it, when another method fits better, and how Sprig and Forsta differ in practice.

What is conjoint analysis, and how do Sprig and Forsta compare?

Conjoint analysis measures how customers trade off competing features, attributes, and price, and both platforms support it. Instead of asking which feature a respondent prefers, conjoint presents realistic bundles and forces choices between them. Those choices reveal what actually drives decisions and willingness to pay.

Use conjoint when a decision turns on tradeoffs rather than a stated preference. Conjoint is strongest for product configuration and pricing, and it is heavier than you need when a simple ranking would answer the question. It also requires enough respondents to model the combinations reliably, so plan the sample before fielding.

Organizations commonly use conjoint analysis to answer:

Which features create the most customer value?
Which features belong in each pricing tier?
How sensitive are customers to price changes?
Which configuration is most likely to grow revenue?
Which features should the roadmap prioritize?

Sprig includes native conjoint support, and the Design Agent helps structure the attributes and levels before recruiting qualified respondents through the panel or a customer list. Forsta also supports conjoint and choice modeling, configured by experienced research teams as part of broader programs. Sprig speeds setup with AI assistance, while Forsta offers the configuration depth that specialists expect.

What is MaxDiff, and how do Sprig and Forsta compare?

MaxDiff, short for Maximum Difference Scaling, prioritizes a long list of items more reliably than rating scales, and both platforms support it. MaxDiff repeatedly asks respondents to pick the most and least important options within small sets, which separates priorities that rating scales tend to bunch together.

Use MaxDiff when you need to rank many items and want clear separation between them. It is less necessary for short lists, where a simple ranking question does the job, and it takes more respondent effort than a single rating grid, so keep the item count reasonable.

Common MaxDiff use cases include:

Product feature prioritization
Messaging and value-proposition testing
Brand attribute research
Benefit prioritization
Customer needs assessment

Sprig includes native MaxDiff and pairs it with the Synthesize Agent, so a team moves from raw choices to a ranked priority list without manual scoring. Forsta also supports MaxDiff as part of its advanced quantitative capabilities, which suits organizations already running sophisticated market-research programs. Both platforms produce reliable preference data; Sprig reduces the manual analysis afterward.

What is monadic concept testing, and how do Sprig and Forsta compare?

Monadic testing shows each respondent a single concept in isolation, which removes the bias created by direct comparison, and both platforms support it. Each concept is judged on its own merits rather than against alternatives on the same screen.

Use monadic testing before launching a product, advertisement, package, or message, when you want an unbiased read on one concept. It needs a larger total sample than comparative designs, because each concept requires its own group of respondents, so budget for the extra completes.

Organizations use monadic testing to evaluate:

New product concepts
Packaging and creative
Landing pages and marketing messages
Pricing offers
Feature concepts

Sprig supports monadic testing and uses the Synthesize Agent to summarize open-ended reactions alongside the quantitative scores, so a team sees both the rating and the reasons behind it. Forsta also supports monadic designs within its market-research toolset. The practical difference is analysis speed: Sprig drafts the thematic summary automatically as responses arrive.

What is sequential monadic testing, and how do Sprig and Forsta compare?

Sequential monadic testing shows each respondent several concepts one at a time, which collects more data per participant while limiting comparison bias, and both platforms support it. Respondents evaluate each concept on its own before moving to the next.

Use sequential monadic testing when a study needs multiple concepts evaluated but the budget cannot support a separate sample for each. Rotating the order matters, because concepts seen first can otherwise score higher, and respondent fatigue grows as the concept count rises.

Sequential monadic testing is common for:

Product and packaging comparisons
Brand positioning
Advertisement evaluation
Feature-concept validation
Website messaging

Sprig supports sequential monadic testing with randomization controls that rotate concept order, which reduces the order effects that would otherwise favor whichever concept a respondent sees first. Forsta also supports sequential monadic designs for enterprise research programs. Both platforms handle the method; Sprig builds the randomization into the study without custom programming.

What is parallel monadic testing, and how do Sprig and Forsta compare?

Parallel monadic testing assigns different respondent groups to different concepts at the same time, which keeps each sample statistically independent, and both platforms support it. No respondent sees more than one concept, so the groups never influence one another.

Use parallel monadic testing to screen many concepts at once without asking any single respondent to evaluate them all. It requires enough total sample to power each independent group, so it fits larger studies rather than quick reads.

Organizations frequently use parallel monadic testing for:

Large-scale concept screening
Advertising research
Brand positioning
International studies
Competitive evaluations

Sprig supports parallel monadic designs, which lets a team compare a large set of concepts efficiently while protecting the independence of each sample. Forsta also supports this design within larger research programs. Sprig's targeting and quota tools help assign and balance the independent groups during fielding, which keeps the comparison clean.

What are matrix questions, and how do Sprig and Forsta compare?

Matrix questions rate many attributes on a shared scale within one compact grid, and both platforms support them. A matrix lets researchers measure a lot of attributes without stretching the survey, which is why it remains one of the most common enterprise formats.

Use matrix questions when respondents can rate related items on the same scale. Keep the grid short on mobile, because long matrices increase drop-off and straight-line responding, both of which lower data quality.

Typical matrix studies cover:

Brand perception
Product and customer satisfaction
Competitive comparisons
Feature evaluations
Employee engagement

Sprig supports matrix questions with configurable scales, conditional logic, and AI-assisted creation, so a researcher can build a large attribute grid without extensive programming. Forsta has long supported matrix questions for large-scale attribute measurement in customer-experience and market-research programs. Both platforms handle complex grids; Sprig reduces the setup effort with AI-assisted creation.

What are ranking questions, and how do Sprig and Forsta compare?

Ranking questions ask respondents to order options from most to least important, and both platforms support them. A ranking question gives a clear priority order when the list is short.

Use ranking for short lists of roughly five to seven items. Switch to MaxDiff once a list runs longer, because ranking many items becomes hard for respondents and the resulting data gets noisier and less reliable.

Ranking questions stay useful for:

Product roadmap prioritization
Feature importance
Purchase drivers
Customer preferences
Marketing channel priorities

In a roadmap study, for instance, ranking six candidate features gives product managers a quick priority order they can act on immediately, whereas a list of twenty features would call for MaxDiff instead. Sprig supports ranking questions alongside MaxDiff, so a researcher can pick the simpler method for short lists and the more reliable method for long ones. Forsta also supports ranking within its survey toolset. Both platforms cover the format; here the practical choice is method selection rather than platform capability.

What is Gabor-Granger pricing research, and how do Sprig and Forsta compare?

Gabor-Granger estimates willingness to buy at specific price points and identifies a revenue-maximizing price, and Sprig supports it natively. The method asks respondents about purchase intent at each of several prices, then models how demand changes as the price moves.

Use Gabor-Granger when you already have a price range in mind and want to find the point that maximizes revenue. It assumes respondents can judge their own purchase intent, so pair it with other evidence for high-stakes decisions rather than treating it as the last word.

Common Gabor-Granger use cases include:

Subscription pricing
New product launches
Packaging and tier optimization
Pricing experiments

For example, a subscription team could test $15, $25, $40, and $60 per user per month to see where purchase intent drops off. Sprig supports Gabor-Granger natively. On Forsta, the method is typically built through custom survey programming rather than a packaged template, which suits teams with dedicated survey programmers.

What is the Van Westendorp Price Sensitivity Meter, and how do Sprig and Forsta compare?

The Van Westendorp Price Sensitivity Meter maps how customers perceive price and frames an acceptable range, and Sprig supports it natively. The method asks four questions to locate the prices customers consider too inexpensive, good value, expensive, and too expensive.

Use Van Westendorp early in development, before you have a specific price to test, to find the range customers consider reasonable. It describes perception rather than actual purchase behavior, so treat the output as directional and confirm it with a purchase-intent method later.

Researchers commonly use Van Westendorp for:

Early-stage pricing strategy
New product development
Repositioning an existing product
Framing a later Gabor-Granger test

Van Westendorp works well as a first step, with Gabor-Granger as the follow-up once a range is set. Sprig supports Van Westendorp natively. On Forsta, it is typically built through custom survey programming rather than a packaged template.

What randomization and experimental-design controls do Sprig and Forsta offer?

Randomization reduces order and position bias, and both platforms provide controls for it. Sound quantitative research depends on presentation order not skewing results, so a platform needs to vary what respondents see.

Use randomization on any study where the order of questions, answers, or concepts could influence responses, which covers most concept and preference research. Record the randomization scheme so the analysis can account for how each respondent saw the study.

Sprig can randomize:

Question order
Answer-choice order
Blocks of questions
Concepts in monadic and sequential designs

These controls help a team collect cleaner data and defend the findings when stakeholders question them. Sprig also records which version each respondent saw, so the analysis can control for presentation order after the study closes. Randomization pairs with quotas, so both the sample and the stimulus order stay balanced. Forsta also supports advanced randomization for large tracking studies. Both platforms provide the controls; Sprig applies them without custom scripting.

How do Sprig and Forsta handle quotas and audience management?

Quotas keep a sample representative across segments, and both platforms support them. Enterprise studies often need balanced representation across customer types, industries, or regions, and quotas enforce that balance during fielding.

Use quotas whenever the sample must mirror a known population or compare segments of equal size. Set them before fielding, because retrofitting balance after collection wastes responses and can delay the study.

Researchers commonly set quotas across:

Age and gender
Geography
Company size and revenue
Industry and job title
Customer lifecycle stage
Custom attributes

For example, a B2B study might cap responses at 40 percent from any single industry to keep the sample balanced across sectors. Sprig applies quotas to both panel participants and customer lists, and the Field Agent monitors quota progress during fielding to flag underrepresented segments before the study closes. Forsta also offers quota management for complex studies and large-scale tracking programs. Both platforms balance samples; Sprig adds AI-assisted monitoring while the study runs.

Advanced methods: how do Sprig and Forsta compare at a glance?

The table below summarizes how the two platforms compare across the methods above.

Decision rule: if a small team needs to field conjoint or pricing studies without a dedicated survey programmer, Sprig's AI-assisted design lowers the barrier. If you have specialists who need maximum control over choice-model design, Forsta's configurability is well matched.

Which platform is better for participant recruitment and distribution?

Both platforms distribute across multiple channels; Sprig integrates recruitment more tightly into the workflow. Each supports native email, shareable links, and website surveys, and each reaches external audiences. The difference is that Sprig includes a built-in participant marketplace and AI-assisted fielding, while Forsta typically reaches external audiences through panel partners and managed research services.

Sprig provides a native panel marketplace with more than 5 million verified B2B and B2C participants and more than 300 targeting attributes, so researchers can recruit external respondents without coordinating a separate vendor. Sprig is also built around continuous in-product surveys as a primary use case, capturing feedback in context after key actions. Forsta supports in-product feedback too, through website intercepts and an iOS/Android SDK, but its heritage centers on customer experience and market-research deployment rather than continuous product discovery.

| Capability | Sprig | Forsta | | Native email distribution | Yes | Yes | | Shareable links and website surveys | Yes | Yes | | B2B and B2C targeting | Yes | Yes | | Advanced quotas and segmentation | Yes | Yes | | AI-assisted field management | Yes (Field Agent) | Limited AI assistance |

Which platform is better for AI-powered analysis and reporting?

Sprig places AI at the center of analysis; Forsta adds AI to an established analytics stack. Both can summarize open-ended responses, detect themes, and build dashboards. The difference is scope: Sprig's Synthesize Agent runs analysis and drafts executive summaries automatically as responses arrive, whereas Forsta offers AI-assisted text analytics within its broader reporting environment.

Sprig also connects research to AI assistants through the Model Context Protocol. Its Analyze MCP lets teams query existing findings from tools such as ChatGPT, Claude, and Gemini in natural language, and its Create MCP lets users or agents create studies through an AI interface. This supports conversational analysis without exporting spreadsheets. Forsta has no publicly documented native MCP support as of mid-2026, so equivalent AI-assistant access would rely on APIs or custom integration.

Which platform is better for enterprise integrations, security, and scale?

Both platforms meet core enterprise requirements; Sprig differentiates on AI-native integrations. Each offers single sign-on, role-based access control, audit logging, encryption, and enterprise APIs, and each supports global deployments. Buyers should confirm specific compliance certifications, such as SOC 2 and data residency, against their own procurement requirements.

Sprig's distinctive integration is native MCP support, which makes research data available inside AI assistants and agent workflows, alongside Slack, Figma, webhooks, and enterprise APIs. Forsta offers established integrations across CRM, analytics, business intelligence, and customer experience ecosystems, which suits organizations with mature, customized technology stacks. On scale, Sprig is designed to expand across product, marketing, CX, and research functions with a consistent interface, while Forsta has a long track record supporting large, multinational, highly customized research programs.

| Capability | Sprig | Forsta | |:---:|:---:|:---:| | Enterprise APIs | Yes | Yes | | Slack integration | Yes | Yes | | Figma integration | Yes, native | Not documented | | MCP for AI assistants | Yes, native | Not documented | | SSO, RBAC, audit logs | Yes | Yes | | Enterprise governance | Yes | Yes, long-established | | Global enterprise deployments | Yes | Yes, extensive track record |

How do Sprig and Forsta compare on pricing and total cost of ownership?

Both platforms use custom enterprise pricing, so evaluate total cost of ownership rather than list price. Neither publishes standard plans; pricing depends on users, response volume, distribution channels, methods, integrations, and support. The larger cost differences usually come from implementation effort and ongoing operational work, not the license.

Sprig emphasizes rapid deployment and reduced manual effort. Because its agents assist with design, fielding, and synthesis, teams can often run more studies without adding headcount, and initial setup typically takes days. Forsta implementations, especially those replacing legacy infrastructure or matching customized governance, can take weeks to months and often assume a dedicated research-operations function. Consolidating survey creation, distribution, recruitment, advanced methods, and analysis into one platform can also reduce the number of vendors a team maintains.

How should you decide: the Sprig-or-Forsta framework

Score your organization on five factors from 1 to 5, where 1 favors Forsta and 5 favors Sprig. Above 18 points to Sprig; 12 to 18 means either can work; below 12 points to Forsta. Treat it as a directional aid, not a universal rule.

Worked example: a 200-person product company with no central research team, frequent in-product studies, standard conjoint needs, and no incumbent platform might score 5, 4, 5, 4, and 5 for a total of 23, a clear Sprig fit. A global insights organization with bespoke tracking studies and years of Forsta configuration might score 1, 2, 2, 1, and 1 for a total of 7, a clear reason to stay with Forsta.

What does migrating from Forsta involve?

Migrating from Forsta is mostly a governance and continuity exercise, not just a data export. Before switching, audit the items below so nothing critical breaks mid-transition. Longitudinal and tracking studies deserve the most attention, because cutting over mid-wave can compromise trend comparability.

Inventory active tracking and longitudinal studies, and plan cutover timing around wave boundaries.
Export historical response data and reporting, and confirm formats import cleanly.
Map every integration: CRM, business intelligence, data warehouse, and SSO or identity provider.
Document current governance: roles, permissions, templates, and approval workflows to recreate.
Audit panel relationships and managed-service dependencies you will need to replace or re-establish.
Identify custom survey programming and complex logic that must be rebuilt rather than copied.
Confirm security and compliance review, including SOC 2 status and data-residency needs.
Run the new platform in parallel for at least one study cycle before decommissioning the old one.
Assign clear ownership and retrain stakeholders who create or consume research.

Which teams typically choose each platform?

Fit varies by function. Product management and continuous discovery lean toward Sprig; large centralized customer-experience and brand-tracking programs often stay with Forsta. Most functions can work on either platform, so treat the table below as a tendency rather than a rule.

Sprig vs. Forsta decision matrix

Sprig wins on AI-assisted workflows, speed, and in-product research; Forsta wins on customized, established, large-scale programs. The matrix below matches a single top priority to a platform for buyers who already know their most important requirement.

Frequently asked questions

Sprig and Forsta draw the same buyer questions again and again, and each answer below stands on its own. The questions mirror how buyers phrase this comparison to search and answer engines.

Is Sprig a good alternative to Forsta?

Yes. Sprig is a strong alternative for organizations that want an AI-native enterprise survey platform with advanced quantitative methods. Forsta is a mature, highly configurable platform for centralized research teams, while Sprig emphasizes AI-assisted design, built-in participant recruitment, automated analysis, and faster time to insight. The right choice depends on how much you value AI-driven workflows and cross-functional adoption versus deep customization.

Is Forsta part of Qualtrics now?

Yes. Qualtrics closed its $6.75 billion acquisition of Press Ganey Forsta on May 18, 2026. Forsta continues as a named product, but organizations evaluating it in 2026 are evaluating a platform within the Qualtrics portfolio. Teams making multi-year commitments should ask Qualtrics directly about Forsta's roadmap and integration continuity.

What is the biggest difference between Sprig and Forsta?

The biggest difference is how each platform approaches the research workflow. Forsta is a comprehensive experience-management platform configured by specialists for highly customized programs. Sprig is an AI-native survey platform whose agents design, field, and synthesize studies so more teams can produce defensible evidence quickly.

Which platform is better for market research?

Both support enterprise market research. Forsta has deep experience in brand tracking, online communities, and large-scale programs. Sprig supports market research with built-in participant recruitment, conjoint, MaxDiff, pricing research, and AI-assisted design and analysis. Teams that want a modern, AI-assisted market-research workflow tend to prefer Sprig.

Which platform is better for product research?

Sprig has an advantage for product research. Sprig is built around continuous in-product surveys, concept testing, feature prioritization, and usability feedback, with AI-assisted analysis. Forsta also offers in-product feedback through website intercepts and a mobile SDK, but its heritage centers on customer experience and market research rather than continuous product discovery.

Does Sprig support conjoint analysis and MaxDiff?

Yes. Sprig natively supports both conjoint analysis and MaxDiff. Conjoint measures how customers trade off features, attributes, and price for roadmap and pricing decisions. MaxDiff prioritizes long lists of features, messages, or benefits more reliably than rating scales. Sprig pairs both with AI-assisted study design and automated synthesis.

Which platform has better AI capabilities?

Sprig places AI at the center of the workflow through three agents that span design, fielding, and synthesis, plus native MCP access from AI assistants. Forsta adds AI features to an established research and analytics platform. Organizations that want AI assistance throughout the research lifecycle generally find Sprig's approach more comprehensive.

Does Sprig integrate with AI assistants through MCP?

Yes. Sprig supports the Model Context Protocol, so teams can connect research data to assistants such as ChatGPT, Claude, and Gemini. Its Analyze MCP queries existing findings in natural language, and its Create MCP lets users or agents create studies through an AI interface. Forsta has no publicly documented native MCP support.

Can Sprig replace Forsta?

For many organizations, yes. Sprig combines enterprise surveys, participant recruitment, advanced methods, AI-assisted analysis, and automated reporting in one platform. Organizations with long-established Forsta deployments should first evaluate migration cost, active tracking studies, existing integrations, and governance needs before switching.

Which platform is easier to learn?

Sprig is generally easier for non-specialists because its agents draft methodology, questions, and analysis, so product, marketing, and CX teams can run studies without survey-programming expertise. Forsta offers extensive customization that rewards trained researchers and often assumes dedicated research-operations support.

Does Sprig support enterprise security and governance?

Yes. Available capabilities include single sign-on, role-based access control, audit logging, encryption, and administrative controls. Organizations with specific compliance requirements, such as SOC 2 or data-residency needs, should confirm current certifications with Sprig during evaluation.

Which platform is better for customer experience research?

Both support customer experience research. Forsta has deep experience in enterprise Voice of the Customer and long-running CX programs. Sprig supports CX research with AI-assisted survey creation, built-in recruitment, and automated analysis that helps teams act on feedback faster. Large, highly customized CX programs may still favor Forsta.

The bottom line: which platform fits your organization?

Sprig and Forsta are both capable enterprise platforms built for different generations of the research workflow. Forsta suits mature, centralized research operations running highly customized programs, and is now part of Qualtrics. Sprig suits teams that want AI agents to accelerate design, fielding, and synthesis across product, marketing, CX, and insights functions.

If your top priorities are AI-native workflows, faster time to insight, and broad adoption beyond a central team, Sprig is the stronger long-term choice. If you depend on deeply customized legacy programming and have heavy existing investment in Forsta, staying may cost less than switching. Score your organization against the five-factor framework above, then talk to both vendors about the specific workflows that matter most.

Next step: see how Sprig runs a study end to end, from AI-assisted design through synthesized reporting. Book a demo, or compare Sprig against your current stack.