The analytics landscape has shifted dramatically. Google Analytics 4 brought a learning curve and complexity that frustrated many teams. Privacy regulations continue tightening. Cookie rejection rates climb higher each year—with studies showing 50-70% of European users now reject tracking when given a clear option. Meanwhile, a new category of privacy-first analytics tools has emerged, offering a fundamentally different approach to understanding website performance.
This comparison examines Databuddy—an open-source, cookieless analytics platform—against traditional analytics tools like Google Analytics 4, exploring the tradeoffs in privacy, performance, accuracy, and functionality.
Philosophy: Data Collection Approaches
Traditional Analytics (GA4)
Google Analytics 4 operates on a "collect everything, model what you can't" philosophy:
- Uses first-party cookies to create persistent user identifiers
- Tracks users across sessions and devices (when possible)
- Employs machine learning to model data from users who decline consent
- Shares data with Google for product improvement and ads optimization
- Requires consent management and compliance infrastructure
Databuddy (Cookieless)
Databuddy operates on "collect only what you need, respect privacy by design":
- No cookies, local storage, or persistent identifiers
- Session-level tracking only—no cross-session user profiles
- All data is anonymous by definition, not by processing
- Full data ownership—no sharing with third parties
- No consent required in most jurisdictions—nothing personal to consent to
Data Accuracy Comparison
Counterintuitively, less tracking often means more accurate aggregate data.
The Consent Problem
| Metric | GA4 (with consent) | Databuddy |
|---|---|---|
| Visitors captured | 30-70% (varies by region) | ~100% |
| Consent acceptance rate | 25-80% (region-dependent) | N/A |
| Ad blocker bypass | Blocked by most (129M+ Chrome users alone) | Typically allowed |
| Data modeling required | Yes (for gaps) | No |
| Data confidence level | Modeled/estimated | Actual observed |
Regional Consent Variations
Cookie consent acceptance rates vary dramatically by region:
- Germany and France: Fewer than 25% of users accept cookies when given a clear choice
- United States: Over 80% acceptance rate (often with opt-out rather than opt-in models)
- Poland: Up to 64% acceptance rate
- Overall EU trend: When a proper "Reject All" button is equally visible, 50-70% of users now reject tracking
What the Numbers Mean
When GA4 reports 10,000 visitors, that typically represents:
- 10,000 visitors who accepted cookies
- Plus an unknown number who declined (modeled via Consent Mode)
- Plus an unknown number using ad blockers (invisible—approximately 31.5% of users globally)
- Minus bots that weren't filtered
When Databuddy reports 10,000 visitors, that represents:
- 10,000 actual visitor sessions observed
- No modeling, no estimation, minimal gaps
Real-World Impact
Organizations switching from GA4 to privacy-first analytics typically report:
- 20-50% more pageviews recorded (previously unconsented or blocked traffic)
- More accurate traffic sources (no sampling or modeling)
- Clearer conversion attribution (complete funnel visibility within sessions)
Note: The exact increase depends heavily on your audience demographics, geographic distribution, and how strictly your previous consent implementation was configured.
Feature Comparison
Core Analytics Features
| Feature | GA4 | Databuddy |
|---|---|---|
| Pageview tracking | Yes | Yes |
| Custom event tracking | Yes | Yes |
| Conversion tracking | Yes | Yes |
| Funnel analysis | Yes | Yes (session-level) |
| Traffic source attribution | Yes (multi-touch available) | Yes (first-touch/session-based) |
| Geographic reporting | Yes | Yes |
| Device/browser breakdown | Yes | Yes |
| Real-time dashboard | Yes | Yes |
| Core Web Vitals | Limited | Yes (LCP, FID/INP, CLS) |
| API access | Yes | Yes |
| Feature flags | No (requires separate tool) | Yes (built-in) |
Advanced Features
| Feature | GA4 | Databuddy |
|---|---|---|
| User-level tracking | Yes (with consent) | No (by design) |
| Cross-device tracking | Yes (logged-in users) | No |
| Cohort retention analysis | Yes | Session-level only |
| Audience segmentation | Yes (persistent) | Yes (session-based) |
| Predictive analytics | Yes (ML models) | No |
| Google Ads integration | Native | No |
| BigQuery export | Native | ClickHouse native |
| Custom dashboards | Looker Studio | Built-in + API |
| Error tracking | Limited | Yes (built-in) |
Performance Impact
Script size and loading behavior significantly impact user experience and Core Web Vitals scores.
Script Size Comparison
| Tool | Script Size | Additional Requests |
|---|---|---|
| Google Analytics 4 (gtag.js) | ~45-104 KB (depending on implementation) | Multiple (gtag, collect, etc.) |
| Google Tag Manager + GA4 | ~75-200 KB (varies by container) | Many (container + tags) |
| Databuddy | ~3 KB | Single request |
| Plausible (comparison) | <1 KB | Single request |
Note: Google Tag Manager has a container size limit of 200 KB, with warnings at 140 KB. Actual sizes vary based on implementation complexity.
Core Web Vitals Impact
Analytics scripts affect Largest Contentful Paint (LCP) and Total Blocking Time (TBT):
- GA4: Can add 50-200ms to LCP depending on implementation
- GTM + GA4: Often adds 100-300ms, plus blocking time for tag execution
- Databuddy: Minimal impact (<10ms typical) due to tiny script and async loading
Carbon Footprint
Privacy-first analytics tools claim significantly lower environmental impact based on:
- Smaller data transfer per pageview (3 KB vs 75+ KB)
- Fewer server requests
- Less data storage and processing required
According to Website Carbon Calculator methodology, this can translate to meaningful reductions in CO2 emissions at scale—estimated at several kilograms of CO2 per year for sites with 100,000+ monthly visitors.
Privacy and Compliance
GDPR Compliance
| Requirement | GA4 | Databuddy |
|---|---|---|
| Consent banner required | Yes | Generally no (no personal data collected) |
| Data Processing Agreement | Required | Not typically required |
| Privacy Impact Assessment | Recommended | Not typically required |
| Data subject access requests | Must handle | N/A (no personal data) |
| Right to erasure | Must implement | N/A (nothing to erase) |
| US data transfer concerns | Addressed by EU-US DPF (but uncertain long-term) | No (self-hosted option available) |
The Google Analytics Legal Situation
Multiple European Data Protection Authorities ruled against Google Analytics between 2022-2023:
- Austria (DSB): Ruled GA non-compliant with GDPR in January 2022
- France (CNIL): Ordered websites to stop using GA without adequate safeguards in February 2022
- Italy (Garante): Found GA transfers to US violated GDPR in June 2022
- Denmark (Datatilsynet): Stated lawful use requires supplementary measures beyond Google's settings (September 2022)
- Sweden (IMY): Ruled against GA use with fines in June 2023
- Norway (Datatilsynet): Final decision against GA in January 2025
Current Legal Status (2025)
The EU-US Data Privacy Framework (DPF), adopted in July 2023, has temporarily stabilized the legal situation. Google LLC is certified under the DPF, making data transfers legally permissible for now. However:
- Privacy advocates (including NOYB and Max Schrems) have announced plans to challenge the DPF
- The European Data Protection Board's 2025 report urged the Commission to re-evaluate the adequacy decision
- Norway's DPA issued new warnings in February 2025 about DPF stability
- Legal uncertainty persists for organizations prioritizing long-term compliance
Disclaimer: This is not legal advice. Organizations should consult legal experts for compliance guidance specific to their situation.
Cost Analysis
Google Analytics 4
- Base cost: Free (with data limits and sharing to Google)
- GA360: Starts at $50,000/year, can reach $150,000-200,000/year for enterprise features (usage-based pricing model)
- Hidden costs:
- Consent management platform: $5,000-50,000/year
- Compliance management: Staff time + legal review
- GTM expertise: Consulting or training costs
- BigQuery costs if using raw data export
- Data accuracy loss from consent gaps: varies
Databuddy
- Cloud (managed):
- Free tier available
- Pro: Based on traffic volume
- Self-hosted:
- Software: Free (open source under AGPL-3.0)
- Infrastructure: $20-200/month typical (VPS or cloud)
- Maintenance: Minimal (Docker-based deployment)
- Hidden costs:
- No consent platform needed: $0
- Reduced compliance burden: Staff time savings
- No data accuracy loss from consent gaps
Total Cost of Ownership Example
For a medium-traffic website (1M pageviews/month) based in the EU:
| Cost Category | GA4 + Compliance | Databuddy Self-Hosted |
|---|---|---|
| Analytics platform | $0 | $0 |
| Consent management | $3,000-10,000/year | $0 |
| Infrastructure | $0 | $600-1,800/year |
| Legal/compliance review | $2,000-5,000/year | $500/year (minimal) |
| Estimated Total | $5,000-15,000/year | $1,100-2,300/year |
Note: Costs vary significantly based on organization size, location, and specific requirements. The "data accuracy cost" is difficult to quantify and has been excluded from this comparison.
Use Case Fit
When to Choose Databuddy
- Content websites and blogs: Traffic metrics without user tracking complexity
- Marketing landing pages: Conversion tracking with full visitor capture
- E-commerce product pages: Understanding browse behavior (complement with transactional analytics)
- EU-focused businesses: Reduce GDPR compliance burden and legal uncertainty
- Privacy-conscious brands: Demonstrate commitment to user privacy
- Performance-critical sites: Minimize analytics impact on page speed
- Developer tools and SaaS: Tech-savvy users who frequently block GA4
- Teams wanting simplicity: Fewer tools, integrated feature flags and error tracking
When to Choose GA4
- Heavy Google Ads investment: Native conversion tracking and optimization
- Complex attribution needs: Multi-touch, cross-device journey analysis
- User lifetime value focus: Tracking individuals across sessions (with consent)
- Existing Google ecosystem: Deep integration with BigQuery, Looker Studio, Google Cloud
- Predictive audiences: ML-powered user segmentation for marketing
- Enterprise requirements: GA360 features, SLAs, and dedicated support
- US-focused audiences: Higher consent rates, different regulatory environment
Hybrid Approach
Many organizations use a combination:
- Databuddy for all visitors: Accurate traffic and content performance baseline
- GA4 for consented users: Deep user-level analysis where permitted
- Product analytics (PostHog, Mixpanel): Authenticated user behavior in-app
Migration Considerations
Moving from GA4 to Databuddy
- Run parallel: Keep GA4 active while deploying Databuddy (typically 2-4 weeks minimum)
- Compare metrics: Understand differences in reported numbers and why they occur
- Adjust dashboards: Rebuild key reports in Databuddy format
- Update goals: Redefine conversion tracking events
- Train stakeholders: Explain why numbers differ (and why Databuddy may be more complete for aggregate metrics)
- Archive historical: Export GA4 data before removing (you have limited time to access historical data after stopping)
What to Expect
- Higher visitor counts: 20-50% increase typical (previously unconsented or blocked traffic)
- Different bounce rates: May change due to different session definitions
- Simpler interface: Faster to find answers, less configuration needed
- Missing user-level data: Plan for this if you relied on cohort analysis or user journeys
- No predictive features: ML-based audiences and predictions not available
Technical Architecture
GA4 Architecture
Browser → gtag.js (45-104KB) → Google Servers → BigQuery/Reports
↓
cookies stored
user_id generated
cross-site tracking enabled (Consent Mode dependent)Databuddy Architecture
Browser → script.js (~3KB) → Your Server → ClickHouse → Dashboard
↓
no cookies
no storage
session hash only (ephemeral)Self-Hosted Stack
┌─────────────────────────────────────────────────────┐
│ Your Infrastructure │
├─────────────────────────────────────────────────────┤
│ ┌───────────┐ ┌───────────┐ ┌───────────┐ │
│ │ Databuddy │ │ PostgreSQL│ │ ClickHouse│ │
│ │ (App) │←→│ (Metadata)│ │ (Events) │ │
│ └───────────┘ └───────────┘ └───────────┘ │
│ ↑ │
│ │ HTTPS │
└────────┼───────────────────────────────────────────┘
│
┌────┴────┐
│ Visitors │
└─────────┘Note: Databuddy uses PostgreSQL (via Neon) for metadata and ClickHouse for high-volume event analytics. The stack is built with Next.js, TypeScript, and can be deployed via Docker.
Conclusion
The choice between Databuddy and Google Analytics 4 isn't just about features—it's about philosophy, compliance posture, and what you truly need from analytics.
Choose Databuddy when:
- Aggregate data accuracy matters more than individual user tracking
- GDPR compliance is a concern or ongoing burden
- Page performance is critical to your business
- You value simplicity and integrated tooling
- Privacy is part of your brand promise
- You want to avoid long-term legal uncertainty around data transfers
Choose GA4 when:
- Google Ads integration is essential to your marketing
- User-level journey analysis is required
- You need predictive audiences and ML features
- Enterprise support and SLAs are necessary
- Your audience is primarily US-based with high consent rates
For many organizations—particularly those with European audiences—the 20-50% data completeness gain from cookieless analytics may outweigh the loss of individual user tracking capabilities. When combined with consent-based product analytics for authenticated users, privacy-first tools like Databuddy can provide a complete, legally defensible analytics stack that respects both user privacy and business intelligence needs.
Last updated: January 2025. Statistics and legal information may change. Always consult legal counsel for compliance decisions specific to your organization.