Yes! Databuddy is a privacy-first web analytics product that has everything I need. The experience was very smooth, the interface is easy to read, and all the key metrics are visible at a glance. I honestly couldn’t find anything missing. After using it for a while, I just decided to stick with it.
Overview
100% Satisfaction.
At first, I kept thinking, “Google Analytics is free. Why would I pay for another analytics tool?” But I completely changed my mind. Once your product starts growing, having a tool like this and using it intentionally really helps you optimize for conversions and actually generate revenue.
The main difference compared to Google Analytics is Databuddy doesn’t need any cookie notice or consent banner because it doesn’t use cookies and doesn’t collect personal data, only anonymous data. That alone removes a lot of friction, both legally and from a user experience perspective.
Why privacy-first analytics matters (and where GDPR fits)
A big reason privacy-first analytics tools have grown in popularity is the regulatory environment.
GDPR (General Data Protection Regulation) is a privacy law that governs how personal data of people in the European Union (EU) and European Economic Area (EEA) is collected, processed, and stored. Crucially, it applies not only to organizations based in the EU, but to any organization that processes data of EU residents, regardless of where that organization is located.
One of GDPR’s core principles is data minimization. This principle requires organizations to collect and process only the personal data that is strictly necessary for a specific purpose, and to avoid retaining it longer than needed.
This has direct implications for how analytics systems are designed. GDPR pushes teams to think more carefully about whether they truly need personal data to understand user behavior, how long that data should be kept, whether it should be shared with third parties, and whether user consent is required.
As a result, analytics approaches that are designed to avoid collecting directly identifiable personal data make compliance significantly simpler. When meaningful insights can be derived without identifying individuals, both privacy risk and operational complexity are reduced.
This is the space where privacy-first analytics tools like Databuddy are positioned. Databuddy is built to provide strong conversion and product insights while being intentionally designed to avoid directly identifiable personal data.
First impressions (dashboard and usability)
My first impression was simple: it is very intuitive. The UI makes it easy to understand what happened and when it happened. I could answer basic questions right away without having to learn analytics first. Google Analytics usually takes time to set up and understand, while some privacy-first tools are so minimal that they lack important insights.
Databuddy strikes a comfortable balance. It feels “real-time” in the sense that it is easy to look at the product and get an immediate sense of current activity, but the information is still structured enough to support more serious questions like:
- Where do users drop off in a funnel?
- Which events correlate with conversion?
- Is something broken after a deployment?
Conversion tracking and funnels (the core value)
The feature I found most useful in Databuddy is feature flags and funnels.
Feature flags allow you to control feature rollouts and run A/B tests. You can easily enable or disable features instantly from your dashboard.
Databuddy provides many flag templates for example “Premium Users Only” so you can restrict new feature to premium tier and “Pricing Tier test” to optimize pricing for maximum conversion rate. As an indie hacker, it’s always very difficult to answer “how to set my pricing”. So it’s worth trying out.
I tried A/B test for my website for a couple days and it gave me valuable insights. Because it went different from what I’ve expected. What I set on my A/B test was this:
- 50% of users would see the discount banner when they land on the website
- They could copy the discount code
- They end up purchasing any of my products
And it didn’t take an hour to implement it (with Claude code) because they provide SDK package and well written official documentation!
Anyway 25 people out of 50 people got this discount banner and only 2 people copied the code. And none of them completed a purchase.
The result itself wasn’t great, but what mattered more was how quickly I could make a decision. Instead of debating pricing for weeks, I had a clear signal within days.
I thought the cheaper it is, the happier users would be. But it’s not true. My website users are mostly from X who saw the viral X post (at least recent days). They were curious, not ready to buy. That helped me to clarify the next step: “Targeting who sees an offer matters more than making it cheaper.”
Instead of showing a discount code to everyone on the homepage, a better next step is to show it only to users who demonstrate higher intent, for example, people who click on the Premium listing, or users who spend 30+ seconds on the homepage or pricing page.
If you are working alone and don’t know what the better decision is, try this feature flags + funnel. It’s just so easy to set up and see the result and get the feedback naturally.
Notable future direction (LLM mention analytics)
I also saw a post from the founder of Databuddy about a feature they plan to release: tracking how often LLMs mention your product.
This is a strong idea because visibility inside LLM responses is becoming as important as traditional SEO signals. Discovery is no longer driven only by Google rankings or backlinks. It increasingly happens through AI-powered search, chat, and recommendations.
In that context, knowing how frequently your product appears in LLM answers is a much closer proxy to future real traffic and brand discovery than page views alone.
Final thoughts
Google Analytics feels like it's built for analysis that ends in a report. Databuddy feels like it's built for analysis that ends in action.
If I had to summarize my experience in one sentence: Databuddy makes privacy-respecting conversion analytics feel normal and straightforward, rather than complicated and risky.
Because I’m on the hobby plan, I don’t have access to features like error tracking or retention. Even so, I’m extremely satisfied with Databuddy overall.
I actually think this setup makes a lot of sense: you can start with the hobby plan, try it out with almost no friction, and then upgrade later as your product grows or when you need more advanced features.