If you're looking for tools to understand what your users want, both Pelin and Sprig aim to surface user insights—but they take very different approaches to collecting and analyzing feedback.
This comparison will help you understand which approach fits your needs.
The Quick Answer
Choose Pelin if you want AI to automatically analyze feedback from your existing touchpoints (support, sales, Slack, etc.) without deploying new surveys.
Choose Sprig if you want to run targeted in-product surveys and capture feedback at specific moments in the user experience.
Now let's dig into the details.
What Each Platform Does
Pelin: Automated Customer Insights
Pelin is an AI-powered platform that automatically analyzes customer feedback from multiple sources. The key insight: customers are already telling you what they think—through support tickets, sales calls, Slack messages, and more. Pelin captures and analyzes this existing feedback.
Key characteristics:
- Connects to 15+ data sources (Intercom, Zendesk, Slack, Gong, Linear, HubSpot, and more)
- Automated categorization into insight types (pain points, feature requests, churn signals, etc.)
- Real-time analysis without deploying surveys
- No manual tagging or organization required
Sprig: In-Product Research Platform
Sprig is an in-product research platform focused on capturing feedback through targeted surveys deployed within your application. It's about asking users questions at the right moment.
Key characteristics:
- In-app surveys triggered by user behavior
- Video interviews and user clips
- AI analysis of survey responses
- Targeting based on user attributes and actions
Head-to-Head Comparison
Feedback Collection Philosophy
This is the fundamental difference between the platforms.
Pelin is passive collection. It analyzes feedback that already exists:
- Support tickets customers submit
- Sales calls that happen naturally
- Slack messages in community channels
- Comments in GitHub or Linear
- Email threads
You don't ask for feedback—you analyze what customers volunteer.
Sprig is active collection. It creates new feedback touchpoints:
- Pop-up surveys in your product
- Targeted questions after specific actions
- Video research prompts
- Scheduled user interviews
You ask users directly and capture their responses.
Winner: Depends on your situation. Pelin if you have high-volume existing feedback. Sprig if you need to ask specific questions.
Data Sources
Pelin connects to your existing tools:
- Support: Intercom, Zendesk, Freshdesk, Front
- Communication: Slack, Gmail, Gong
- Product tools: Linear, Jira, GitHub
- CRM: HubSpot, Salesforce
- Docs: Notion, Confluence
- Surveys: Typeform (if you use external surveys)
Sprig is the data source itself:
- In-product surveys you create
- Video responses from users
- Session replays
- Heatmaps (in some plans)
Winner: Pelin for breadth of existing feedback. Sprig for controlled, targeted collection.
Analysis Capabilities
Pelin's AI is built for high-volume, unstructured feedback:
- Automatic categorization (pain points, feature requests, etc.)
- Clustering similar feedback into themes
- Sentiment detection
- Trend analysis over time
- Cross-source pattern recognition
Sprig's AI is built for survey response analysis:
- Themes in open-ended responses
- Sentiment analysis
- Highlights from video responses
- Aggregation across survey responses
Both use AI, but optimized for different input types.
Winner: Pelin for unstructured feedback across sources. Sprig for survey response analysis.
User Experience Impact
Pelin has zero user-facing footprint. Your customers don't see Pelin, don't interact with it, and don't know it exists. No surveys, no pop-ups, no friction.
Sprig requires user interaction. Users see surveys in your product, which can:
- Capture valuable targeted feedback
- Create survey fatigue if overused
- Interrupt user workflows
- Have lower response rates over time
Winner: Pelin for zero user friction. Sprig for targeted question-asking.
Types of Insights
Pelin surfaces insights from organic conversations:
- Pain points (what frustrates users)
- Feature requests (what users want)
- Confusion points (where users get stuck)
- Churn risk signals (warning signs)
- Competitive mentions (what competitors do)
- Positive feedback (what's working)
- Power user patterns (successful behaviors)
Sprig surfaces insights you ask about:
- Why users took a specific action
- How users feel about a feature
- What users would change
- Whether users would recommend you
- User satisfaction at key moments
Winner: Pelin for discovering unknown problems. Sprig for validating specific hypotheses.
Setup & Maintenance
Pelin setup is connection-based:
- Connect your data sources (OAuth typically)
- AI starts analyzing immediately
- No ongoing maintenance required
Sprig setup is deployment-based:
- Install SDK in your application
- Create and deploy surveys
- Maintain targeting rules
- Update surveys as product changes
Winner: Pelin for simplicity. Sprig if you want control over survey design.
Use Case Comparison
Best for Pelin
- Teams with high volumes of support tickets, sales calls, or community messages
- Product managers who want continuous insight without survey management
- Companies where feedback is already flowing through various channels
- Leaders who want to understand patterns across the customer journey
- Organizations concerned about survey fatigue
Best for Sprig
- Teams who need answers to specific questions at specific moments
- Product managers validating hypotheses about user behavior
- Companies with lower organic feedback volume
- Researchers conducting structured in-product studies
- Organizations building new features and wanting targeted input
Real-World Scenarios
Scenario 1: "We get 1,000 support tickets per month—what are the main issues?"
→ Pelin wins. Connect your support platform, and Pelin automatically clusters and categorizes issues. No surveys needed.
Scenario 2: "We launched a new feature—why aren't users adopting it?"
→ Sprig wins. Deploy a targeted survey to users who saw but didn't use the feature. Ask them directly.
Scenario 3: "Our sales team hears objections we never address"
→ Pelin wins. Connect Gong or your CRM, and Pelin extracts objections and competitive mentions from sales conversations.
Scenario 4: "We want to know if users would pay more for a premium feature"
→ Sprig wins. Run a targeted survey to your power users with willingness-to-pay questions.
Scenario 5: "We need to reduce churn but don't know why people leave"
→ Both help differently. Pelin analyzes churn signals in existing conversations (support, cancellation flows, sales). Sprig can deploy exit surveys to churning users. Consider using both.
Scenario 6: "We want to understand user sentiment across all touchpoints"
→ Pelin wins. A single survey can't capture sentiment across support, sales, community, and product interactions. Pelin aggregates all of it.
The Complementary Approach
Pelin and Sprig can work well together:
Pelin tells you what users are already saying, surfaces unknown problems, and identifies patterns across high volumes of organic feedback.
Sprig lets you dig deeper on specific questions, validate hypotheses that emerge from Pelin's analysis, and capture feedback at precise moments.
Workflow example:
- Pelin surfaces a cluster of complaints about onboarding
- You deploy a Sprig survey to new users asking about specific pain points
- Sprig responses confirm and add detail to Pelin's findings
- You have both the broad pattern (Pelin) and specific feedback (Sprig)
Survey Fatigue Considerations
In-product surveys face a real challenge: users get tired of them.
Initial response rates might be 15-30%, but they decline over time as users learn to dismiss pop-ups. Heavy survey users report rates dropping to 5% or lower.
Pelin's approach sidesteps this entirely. Users don't see surveys, don't dismiss anything, and can't develop fatigue. The feedback analyzed is feedback they were already giving.
If you're in a situation where users already complain about too many surveys, Pelin's passive approach might be more sustainable.
Pricing Considerations
Sprig prices based on responses collected and features needed. Heavy survey usage can drive costs up.
Pelin prices based on connected data sources and team size. Analyzing more feedback doesn't necessarily cost more.
Consider your expected feedback volume when comparing.
The Bottom Line
Sprig and Pelin represent different philosophies about user feedback.
Sprig says: "Ask users what you want to know, at the right moment." It's powerful for targeted research, hypothesis validation, and capturing feedback at specific touchpoints. The tradeoff is survey management and potential user fatigue.
Pelin says: "Users are already telling you—just listen." It analyzes the feedback customers naturally share through support, sales, and community channels. The tradeoff is you can't ask specific questions.
If your biggest challenge is "we have too much feedback and can't find patterns"—Pelin. If your biggest challenge is "we need answers to specific questions"—Sprig. If you want both broad patterns AND targeted answers—consider using both.
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