But over the last few years, it has also raised the bar in terms of complexity, interpretation, and legal awareness, especially for small teams operating in the EU.
For many founders, the issue is no longer missing features — it’s the trade-off between complexity, data reliability, and compliance risk.
1. The GA4 learning curve now exceeds the needs of many small SaaS teams
GA4 was designed as a universal analytics platform: enterprise-ready, event-based, optimized for machine learning and attribution modeling.
For small and mid-size products, this often results in: manual event design, indirect answers to simple questions and data that requires explanation before it can be trusted.
Common pattern: Teams often use a small fraction of GA4’s capabilities, but still absorb the full operational complexity.
2. Privacy constraints now directly affect data completeness.
EU privacy regulations have changed the rules: consent banners are required, a meaningful share of users opt out, analytics data becomes incomplete by default.
This is not a GA4 bug. It’s the reality of a privacy-first web.
Practical consequence
Metrics should no longer be treated as exact truth — they are approximations, influenced by consent and browser behavior.
3. Server-side tracking helps, but introduces new costs and responsibilities
Server-side analytics can: reduce dependency on the browser, provide more control over data flow.
But it also brings: infrastructure overhead, maintenance complexity, increased responsibility for data handling.
For some teams, this is justified. For others, it’s disproportionate to the value they actually need.
4. GDPR compliance is not binary — it’s a spectrum
Most products operate somewhere between: “best effort compliance” and “we can’t afford full legal and technical certainty”.
What matters in practice: not every tool fits every risk profile, analytics choices are strategic decisions, not just technical ones.
Why alternative approaches are gaining attention.
Given these constraints, many teams are reconsidering how much data they truly need.
This has led to growing interest in: cookie-less analytics, strict data minimization, focusing on core product metrics instead of exhaustive tracking.
One example of this approach is CheckAnalytic.com : no cookies, no personal data, no consent banners, clear, unsampled metrics focused on essentials.
It’s not a full GA4 replacement — and it doesn’t try to be.
It’s a practical alternative for teams prioritizing simplicity, predictability, and lower compliance risk.
Questions the market still hasn’t answered.
Do all products really need deep behavioral tracking?
Where is the line between useful analytics and unnecessary data collection?
Can fewer, cleaner metrics lead to better decisions?
Different teams will answer differently.
That’s likely why analytics tooling is fragmenting rather than converging.
!!!Conclusion!!!
GA4 isn’t “bad”, and it isn’t “broken”.
It has simply evolved in a direction that doesn’t fit everyone equally well.
For founders and small teams, this creates an opportunity to: reassess what analytics should actually do, simplify their stack and choose tools aligned with their real constraints — technical, legal, and operational.