Statsig Developer Tier
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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
Robust feature rollouts and experimentation
What do you like best about the product?
I primarily use Statsig for Feature rollouts and A/B experimentations on a daily basis. The tool has an intutive UI, is easy to integrate within the existing systems and analyze the results.
Automatic stat analysis makes it seamless to understand and analyze the experiments and results even for non data scientists. I especially like it for the ease of it, ability to dive deeper into each experiment, user results and have the ability to be able to debug user connections, analyze user journeys and impact of each feature/rollout on the metrics all through one tool without the need for constant engineering involvement.
I especially appreciate the "Pulse" and "Holdout" features offering visibility into how experiments impact core business metrics over time.
Also, great customer support team which helps in resolving issues in a prompt manner.
Automatic stat analysis makes it seamless to understand and analyze the experiments and results even for non data scientists. I especially like it for the ease of it, ability to dive deeper into each experiment, user results and have the ability to be able to debug user connections, analyze user journeys and impact of each feature/rollout on the metrics all through one tool without the need for constant engineering involvement.
I especially appreciate the "Pulse" and "Holdout" features offering visibility into how experiments impact core business metrics over time.
Also, great customer support team which helps in resolving issues in a prompt manner.
What do you dislike about the product?
The onboarding experience could be smoother for teams not familiar with experimentation platforms. The terminology and setup flows require upfront learning and hands on experience which if improved could be a huge upgrade.
What problems is the product solving and how is that benefiting you?
Statsig helps us move quicker and has decreased dependencies on engineering and analytics teams. It has empowered the product teams to be able to release new features, analyze performance have the ability to dive deeper into user flows and analyze the journeys/dropoffs etc better based on real data, and improved decision-making across engineering and product teams. We can release new features, rollout further, A/B test, understand the impact of each, roll back if metrics drop — all without waiting for a deployment.
I use statsig to validate our newly developed features through a/b's.
What do you like best about the product?
User interface and data visualization is the best
What do you dislike about the product?
I thought Statsig allocates all the eligible users to the a/b's. Turns out, Statsig can allocate nearly 80% of eligible users. It would be more helpful to highlight this fact somewhere on UI in order for product managers to communicate the true impact.
What problems is the product solving and how is that benefiting you?
Statsig helps us to validate our decisions by evidence. It helps us to be more data-driven with our product roadmap. Also it creates an easy, safer roll out process for newly developed features.
Statsig is fundamental for our machine learning lifecycle
What do you like best about the product?
Configuration is easy and the Statsig team responds rapidly to our questions
What do you dislike about the product?
Support for multi armed bandits is very limited at the moment
What problems is the product solving and how is that benefiting you?
Dynamically configuring our services and experimenting with changes in our ml models
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