This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
AB-Labz
Plan, analyze, and manage A/B tests in one workspace
AB-Labz is a lightweight experimentation workspace for analysts and product teams. Upload a dataset and get a complete A/B or multi-variant test analysis in minutes — no SDK, warehouse integration, or engineering support required. Add the hypothesis, design, conclusions, and decision, export the result to Markdown, and build a searchable experiment archive over time. Plan tests, forecast outcomes, and track program impact when you need more.
Hi Product Hunt! I’m Petr, a product analyst and the founder of AB-Labz.
AB-Labz started as a tool we built for ourselves. We wanted a faster way to analyze experiments, keep their context, and avoid rebuilding the same workflow across SQL exports, spreadsheets, notebooks, and project trackers. It was never meant to be a heavy corporate platform — just something practical that made our own work easier. We use it every day, and we hope it will be useful to you too.
AB-Labz is a lightweight experimentation workspace that starts with the dataset you already have.
You can register, upload a prepared dataset, and get a complete A/B or multi-variant test analysis in minutes — without installing an SDK, connecting a warehouse, changing your traffic allocation system, or involving an engineering team. Even if your company is not ready to adopt a new tool, you can use AB-Labz as your personal statistical layer alongside Jira, Confluence, notebooks, and whatever else your team already uses.
The statistical engine was developed together with two practicing statistics professors, so it does more than run a basic significance test. It checks the data, selects suitable methods, and automatically handles skewed, noisy, and otherwise messy real-world distributions.
Then you can add the hypothesis, design, conclusions, and final decision, keep everything as a structured experiment record, export it to Markdown, or share the result through a public link that anyone can open without an account.
Over time, those records become a searchable experiment archive. When you need more structure, AB-Labz also includes sample-size planning, forecasting, Bayesian monitoring, hypothesis management, Kanban, Gantt, calendar planning, and portfolio-level analytics that show how the entire experimentation program is performing and what it delivers to the business.
AB-Labz does not replace GrowthBook, Statsig, Optimizely, or another feature-flagging and traffic-splitting tool. It works alongside them — or with your internal assignment system.
There is a free Solo plan for individual analysts.
I’d really appreciate your feedback: what is currently the most painful part of your experimentation workflow?
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About AB-Labz on Product Hunt
“Plan, analyze, and manage A/B tests in one workspace”
AB-Labz was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #151 on the daily leaderboard. AB-Labz is a lightweight experimentation workspace for analysts and product teams. Upload a dataset and get a complete A/B or multi-variant test analysis in minutes — no SDK, warehouse integration, or engineering support required. Add the hypothesis, design, conclusions, and decision, export the result to Markdown, and build a searchable experiment archive over time. Plan tests, forecast outcomes, and track program impact when you need more.
AB-Labz was featured in Analytics (173.2k followers), A/B Testing (20.4k followers) and SaaS (43.6k followers) on Product Hunt. Together, these topics include over 69.5k products, making this a competitive space to launch in.
Who hunted AB-Labz?
AB-Labz was hunted by Petr Nanichkin. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how AB-Labz stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Petr Nanichkin
Hi Product Hunt! I’m Petr, a product analyst and the founder of AB-Labz.
AB-Labz started as a tool we built for ourselves. We wanted a faster way to analyze experiments, keep their context, and avoid rebuilding the same workflow across SQL exports, spreadsheets, notebooks, and project trackers. It was never meant to be a heavy corporate platform — just something practical that made our own work easier. We use it every day, and we hope it will be useful to you too.
AB-Labz is a lightweight experimentation workspace that starts with the dataset you already have.
You can register, upload a prepared dataset, and get a complete A/B or multi-variant test analysis in minutes — without installing an SDK, connecting a warehouse, changing your traffic allocation system, or involving an engineering team. Even if your company is not ready to adopt a new tool, you can use AB-Labz as your personal statistical layer alongside Jira, Confluence, notebooks, and whatever else your team already uses.
The statistical engine was developed together with two practicing statistics professors, so it does more than run a basic significance test. It checks the data, selects suitable methods, and automatically handles skewed, noisy, and otherwise messy real-world distributions.
Then you can add the hypothesis, design, conclusions, and final decision, keep everything as a structured experiment record, export it to Markdown, or share the result through a public link that anyone can open without an account.
Over time, those records become a searchable experiment archive. When you need more structure, AB-Labz also includes sample-size planning, forecasting, Bayesian monitoring, hypothesis management, Kanban, Gantt, calendar planning, and portfolio-level analytics that show how the entire experimentation program is performing and what it delivers to the business.
AB-Labz does not replace GrowthBook, Statsig, Optimizely, or another feature-flagging and traffic-splitting tool. It works alongside them — or with your internal assignment system.
There is a free Solo plan for individual analysts.
I’d really appreciate your feedback: what is currently the most painful part of your experimentation workflow?