Statsig Developer Tier
StatsigReviews from AWS customer
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An efficient, practical, and modern tool
What do you like best about the product?
Its UI is friendly and clear. I’ve been working with the tool for over a year now, and the experience has been very positive. I work directly in an experimentation and analytics team, and as a Statsig administrator, I’ve been involved in the entire experiment setup process. The interface for defining targeting rules is simple and intuitive, and the results section is truly powerful.
One feature I’ve recently started using and found especially useful is assignment sources. If you have an external process outside of Statsig and simply want to use the platform to analyze results, this option offers an incredible engine for standardizing metrics. It allows you to quickly understand the behavior of key metrics.
Today, I can confidently say that Statsig has become our main tool for daily analytics. The migration process was smooth in terms of implementation, and we integrated more than 28 teams across the company—always supported by the close guidance of Statsig’s technical team.
One feature I’ve recently started using and found especially useful is assignment sources. If you have an external process outside of Statsig and simply want to use the platform to analyze results, this option offers an incredible engine for standardizing metrics. It allows you to quickly understand the behavior of key metrics.
Today, I can confidently say that Statsig has become our main tool for daily analytics. The migration process was smooth in terms of implementation, and we integrated more than 28 teams across the company—always supported by the close guidance of Statsig’s technical team.
What do you dislike about the product?
There are sections I haven’t had the chance to explore yet, such as Power Analysis or Autotune. However, I wouldn’t say they aren’t useful—on the contrary, I recognize that it’s more a matter of my own unfamiliarity. I’m sure they are highly valuable and useful tools.
Regarding my experience, perhaps the only drawback I’ve identified so far is the flexibility when integrating certain fields from databases. We’re currently working on adding segments to experiments—such as user type, city, or boolean fields indicating, for example, whether a user has an active subscription. While working directly within Statsig, adding these segments is straightforward. However, if you want to bring that information into a large-scale database report, it can become more complex to integrate these fields efficiently.
Regarding my experience, perhaps the only drawback I’ve identified so far is the flexibility when integrating certain fields from databases. We’re currently working on adding segments to experiments—such as user type, city, or boolean fields indicating, for example, whether a user has an active subscription. While working directly within Statsig, adding these segments is straightforward. However, if you want to bring that information into a large-scale database report, it can become more complex to integrate these fields efficiently.
What problems is the product solving and how is that benefiting you?
Without a doubt, it’s a tool that brings structure and order. From user management to the creation of experiments, feature gates, or dynamic configs, everything is designed to follow best practices and streamline operations.
We’ve really appreciated the environment it provides to clearly evaluate whether an experiment is successful and whether it should be scaled or rolled back. The way results are presented allows us to act quickly and confidently.
Having everything centralized in a single platform from the experiment configuration tab, through the assignment sources, to the metrics catalog enables us to make decisions directly within the tool.
We also want to highlight the flexibility to integrate our own models through Assignment Sources, which further expands our analytical capabilities.
Finally, all of this benefits us as a company by giving us greater visibility into what different teams are doing, making us more efficient and much clearer in our decision-making.
We’ve really appreciated the environment it provides to clearly evaluate whether an experiment is successful and whether it should be scaled or rolled back. The way results are presented allows us to act quickly and confidently.
Having everything centralized in a single platform from the experiment configuration tab, through the assignment sources, to the metrics catalog enables us to make decisions directly within the tool.
We also want to highlight the flexibility to integrate our own models through Assignment Sources, which further expands our analytical capabilities.
Finally, all of this benefits us as a company by giving us greater visibility into what different teams are doing, making us more efficient and much clearer in our decision-making.
Great experimentation platform - especially for streamlining analytics
What do you like best about the product?
It's very flexible on how to define metrics - we use our own warehouse, so we basically just feed it whatever internal business logic we already had, and Statsig helps us track metrics, check for imbalances, and cut by segments as we need. I use it as a product analyst every time we experiment, and if there is some arcane custom logic we have to run on an experiment one single time, it even outputs the tables to our warehouse so we can easily poke around in SQL.
Documentation is good, and the service staff is responsive; they have helped quickly when there are bugs to fix or stuff we didn't understand.
Documentation is good, and the service staff is responsive; they have helped quickly when there are bugs to fix or stuff we didn't understand.
What do you dislike about the product?
Not much, it can be a little hard to understand the underlying logic sometimes, but that's not the fault of the platform; rather that the users should be good at documenting definitions, because Statsig does allow you to write notes/descriptions where it matters.
The few times I've personally had troubles with Statsig, they've fixed them.
The few times I've personally had troubles with Statsig, they've fixed them.
What problems is the product solving and how is that benefiting you?
It solves the problem of metric creation and governance. Previously, we had a system where each analyst had their own SQL queries for getting metrics for an experiment, balance checks were done *sometimes*, and we'd often needed to end up rerunning them. Then we built a custom system that was git-controlled to standardize the SQL code, but adding experiments was cumbersome, and almost impossible to cut the data by the segments we wanted (at least automatically). Statsig encompasses all that, and then more by putting it all together in a nice package, where each team can more easily understand what the other team is doing (it's a single place for Engineering, Analytics and Product), not only that, but by saving the results of each experiment, we can more easily look back at things, like attributing changes in our metrics to a specific experiment (instead of slowly going through readouts).
AB Testing Made Easy, But Setup Needs Clarity
What do you like best about the product?
I find Statsig's AB testing features highly valuable, particularly the experiments tab that simplifies setting up tests. The Sidecar functionality is incredibly helpful for my team, as it enables us to conduct AB tests without extensive coding knowledge, making testing a more integral part of our workflow for data-driven decision-making. Gathering accurate data and user-friendliness are key aspects that influence my consideration of purchasing Statsig. Overall, these features make Statsig an essential tool for tracking and evaluating the performance of my website pages effectively.
What do you dislike about the product?
I find Statsig challenging to set up initially due to the substantial amount of code involved and my team’s lack of coding expertise. The setting up process was confusing because I wasn't aware of the Sidecar option from the start. It was only after I contacted the support team that I learned about it, and its installation significantly simplified our process. I wish it had been more apparent from the website that Sidecar could be installed on Google Chrome without having to reach out to support. Furthermore, I believe the help articles could be improved with more readability and visual aids to enhance understanding for users like myself, who value user-friendliness.
What problems is the product solving and how is that benefiting you?
I use Statsig for AB testing, which provides me with a method to test the performance of various elements on our website, such as landing pages and calls to action, enabling us to track and gather crucial data for informed decision-making.
Statsig is the best-in-class experimentation and feature flagging platform
What do you like best about the product?
Statsig does everything correctly. I've evaluated a dozen experimentation platforms. Their billing model makes sense, the two offerings (Cloud vs WHN) makes sense, the flexibility with treating different units as first-class citizens, the ability to really perform nuanced experiments with multiple goal metrics. Their SDKs are easy to implement, and performant too. Integrating Statsig into our organization was seamless with the Snowflake import/export functionality. Support is always prompt with a response, and they seemingly have the only (?) support bot I've seen so far that is actually helpful most of the time. Prior to Statsig, we only ran a handful experiments a year. We now run 4x-5x as many experiments.
What do you dislike about the product?
There are truly no real downsides to Statsig. It's very clear that it's a platform built by people that care about UX, analysis, and engineering.
What problems is the product solving and how is that benefiting you?
Statsig enables us to experiment at a much higher velocity than before, with a billing and implementation model that simply makes sense.
Versatile Experimentation Tool with Robust Features
What do you like best about the product?
I've been using Statsig for almost two years, primarily for A/B testing and experimentation across multiple product teams. I appreciate that it offers a comprehensive all-in-one platform for running tests directly within the tool, avoiding the need for external analysis. The core A/B testing experience is robust with advanced statistical techniques, such as Bayesian testing and variance reduction, or Frequentist approach, allowing flexibility depending on our statistical approach. Another standout feature is the product analytics, which helps us visualize data and identify any major issues, complementing our experiments effectively. I find the ability to select statistical engines particularly valuable, along with their newer capabilities like feature flagging and potential for testing algorithms or large language models. Statsig’s setup is intuitive and supports a smooth workflow. Statsig allows me to keep the evaluation in the platform without export it to other custom analysis engine (like a notebook). Overall, I find Statsig to be a well-rounded product, worthy of a solid nine out of ten rating for recommendation.
What do you dislike about the product?
We had a challenge integrating in React Native environment, but to be fair - that is a shortcoming on our side that we had for our apps initially. As we switched to native apps the integration with SDK was very smooth. I would also always suggest a deeper technical chat with platform experts, as testing the platform integration with A/A test is very crucial to uncover hidden issues.
What problems is the product solving and how is that benefiting you?
I use Statsig to streamline experimentation across multiple teams, enabling us to implement ideas quickly and measure impact accurately. It provides robust statistical tools and product analytics, helping us tie product development to business objectives and quantify crucial metrics.
Greatly speeds up experimentation setup & analysis
What do you like best about the product?
I use statsig to monitor product experiments almost every day.
- Ease of setting up new experiments
- Ease & speed of analysing experimental results
- Has all the features we need to cover 90% of the experiment setups we wish to run
- Speed of bug reporting, understanding & resolution
- Amazing documentation and further support when asking for it
- Ease of setting up new experiments
- Ease & speed of analysing experimental results
- Has all the features we need to cover 90% of the experiment setups we wish to run
- Speed of bug reporting, understanding & resolution
- Amazing documentation and further support when asking for it
What do you dislike about the product?
It can be complex to understand at first. There's a big learning curve upfront, especially for engineers, but once that's done, it becomes a superpower.
What problems is the product solving and how is that benefiting you?
- Experiment setup speed
- Results monitoring & finalisation (with statistical adjusments e.g. early stopping)
- Metric calculations and statistical testing
- Experiment catalogue and record keeping
- Federation of running experiments
- Holdout groups
- Results monitoring & finalisation (with statistical adjusments e.g. early stopping)
- Metric calculations and statistical testing
- Experiment catalogue and record keeping
- Federation of running experiments
- Holdout groups
Reliable Experimentation Platform with Excellent Support and Resources
What do you like best about the product?
One of the biggest things I appreciate about this platform is how easy it was to get started. Across our company, three different tech-stack teams (PHP, JS, mobile) have integrated it into their workflows—and each time, it’s been a smooth process. The documentation is clear, and we’ve never hit any frustrating blockers during setup.
The tool itself is intuitive for both technical and non-technical users. Our stakeholders feel confident navigating the UI, creating experiments and interpreting the results by themselves, which frees up a lot of my time, as I am an analyst in the team. The documentation, blog articles, recorded sessions, and the Statsig University are all a great help through the whole process. They not only explain how to use the platform, but also deepen understanding of experimentation best practices in general and can be interesting for absolutely everyone involved in the product development.
The support team deserves a special mention. Every time we’ve reached out with a question or issue, we’ve heard back the same day. Having a dedicated Slack channel has honestly been a game changer, as it makes the support feel more like a partnership than a ticket queue!
The tool itself is intuitive for both technical and non-technical users. Our stakeholders feel confident navigating the UI, creating experiments and interpreting the results by themselves, which frees up a lot of my time, as I am an analyst in the team. The documentation, blog articles, recorded sessions, and the Statsig University are all a great help through the whole process. They not only explain how to use the platform, but also deepen understanding of experimentation best practices in general and can be interesting for absolutely everyone involved in the product development.
The support team deserves a special mention. Every time we’ve reached out with a question or issue, we’ve heard back the same day. Having a dedicated Slack channel has honestly been a game changer, as it makes the support feel more like a partnership than a ticket queue!
What do you dislike about the product?
The main area where we’ve had issues is the Google Analytics (GA) integration. Right now, when events are forwarded from Statsig to GA, only the event_name gets sent, but any additional event parameters are lost. So for something like a purchase event, we lose all item details, which limits our ability to analyze user behavior fully outside of Statsig.
Also, the GA integration broke twice this year, and we didn’t know until we manually spotted issues and reached out to support. To their credit, both issues were fixed quickly, but it would be great to have some kind of monitoring or alerting in place to catch this earlier.
Also, the GA integration broke twice this year, and we didn’t know until we manually spotted issues and reached out to support. To their credit, both issues were fixed quickly, but it would be great to have some kind of monitoring or alerting in place to catch this earlier.
What problems is the product solving and how is that benefiting you?
We mainly use Statsig for running experiments, and it’s become an important step in validating ideas. It helps us assess our hypotheses and make the right decisions for our product based on real data and not just the gut feeling. We are still on a way to become more and more data driven, but we can already see a shift for our teams, as Statsig has really helped us to develop our product in the right direction.
In addition to experimentation, we also use Statsig as a feature flagging tool. It’s been surprisingly easy to set up and manage. Being able to quickly turn features on or off for specific user segments has made our releases much more controlled and less risky.
In addition to experimentation, we also use Statsig as a feature flagging tool. It’s been surprisingly easy to set up and manage. Being able to quickly turn features on or off for specific user segments has made our releases much more controlled and less risky.
Statsig makes my job so much easier
What do you like best about the product?
It's super easy to use (setting up experiments, deep diving into results) and has a wide range of useful features (user and metric breakouts, holdouts, dashboards)
What do you dislike about the product?
It's perhaps a little bit too stats-y for non-technical people to understand - it would be nice to have more in-situ information about what results mean, so that my stakeholders can self serve more easily
What problems is the product solving and how is that benefiting you?
Running experimentation at scale - Statsig allows me to run lots of experiments at once without drowning in setup & analysis
Great and easy to use product
What do you like best about the product?
The ease of use and the beautiful UI that makes it such a great experience
What do you dislike about the product?
I don’t think there are a lot. Mostly the running time of queries, but that’s our data lake..
What problems is the product solving and how is that benefiting you?
A/b testing and understanding what’s the impact on users
Statsig makes complex experimentation setup and analysis easy
What do you like best about the product?
I like the user interface from Statsig, all the experimentation capabilities it supports.
It's easy to breakdown results based on specific customer demographics, or improve the estimates of impact based on additional regressors. It takes a lot of the setup away from the organisation to focus on delivering insights
It's easy to breakdown results based on specific customer demographics, or improve the estimates of impact based on additional regressors. It takes a lot of the setup away from the organisation to focus on delivering insights
What do you dislike about the product?
Statsig could improve on how we extract all the details of experiment performance in a more programmatic way, so that we can do meta analysis offline
What problems is the product solving and how is that benefiting you?
Statsig enable more self serve capabilities for experiment analysis. We've had big success with getting non technical stakeholders to setup and analyse their own experiments, and only focusing on providing guidelines and best practices
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