Statsig Developer Tier
StatsigReviews from AWS customer
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Intuitive experimentation tool with a learning curve.
What do you like best about the product?
Statsig makes it really easy to get experiments up and running without a ton of overhead. I use the Power Analysis Calculator often — it’s straightforward and helps me scope tests quickly.
The metrics catalog is especially useful for creating a single source of truth and reduces the need for reconciling results between sources later on. The customer support team is also quite knowledgeable and responsive to questions.
The metrics catalog is especially useful for creating a single source of truth and reduces the need for reconciling results between sources later on. The customer support team is also quite knowledgeable and responsive to questions.
What do you dislike about the product?
There’s a bit of a learning curve when it comes to setup, especially if you're not continuing from a previous test. I’ve run into moments where it wasn’t obvious how certain events were being ingested or why the targeting logic wasn’t working as expected. Some of that could be improved with more in-product guidance or clearer documentation. I also wish debugging flows were a little more transparent — it can take some back-and-forth to figure out why a user didn’t qualify or why results aren’t showing up as expected.
What problems is the product solving and how is that benefiting you?
Statsig has helped take a lot of the friction out of running experiments — both technically and culturally. We used to spend a lot of time debating setup decisions and second-guessing results. Now, it’s easier to get experiments out the door, track impact in a consistent way, and focus more on what we’re learning rather than how the test was run. It’s also helped shift experimentation from something owned by a few to something more collaborative across teams, which makes the whole process more sustainable.
I use statsig at work for experimentation - both setup and basic analysis, as well as feature flags.
What do you like best about the product?
- Easy to setup
- Experiment/feature flag setup is straightforward
- UI is intuitive with respect to experiment setup and results
- Experiment/feature flag setup is straightforward
- UI is intuitive with respect to experiment setup and results
What do you dislike about the product?
There are some bugs with the client SDK, and we've had issues with documentation/debugging that required very close support with engineers from statsig.
What problems is the product solving and how is that benefiting you?
Doing experimentation at scale with analytics is cumbersome. At Notion, we previously had an in-house experimentation platform, but while scaling up statsig is easier and has more robust features/UI to serve our purposes.
Easy to use
What do you like best about the product?
It is very easy to use, and have integration with slack, so when pal use status updates I can got notifications.
It also very easy to setup experiment rules, and overrides, the graph for compare different groups is easy to understand
It also very easy to setup experiment rules, and overrides, the graph for compare different groups is easy to understand
What do you dislike about the product?
Top of my mind is the first time setup is not very straight forward, need some one to write down a very specific steps, but once we had that steps, it is very easy to analyze the data
What problems is the product solving and how is that benefiting you?
It listed all the metric I can use for my experiment, so when our Data Sciences set it up, I can easily use it.
And if I curious about something else, I can see the query to understand it better, or create my own query against the experiment, it helps for analysis
And if I curious about something else, I can see the query to understand it better, or create my own query against the experiment, it helps for analysis
Attentive Review
What do you like best about the product?
It really made A/B testing at Attentive better. It standardized our processes, made our metrics more rigorous, and centralized A/B testing as a whole across the company.
What do you dislike about the product?
Its a bit complicated to use. At times it can be overwhelming.
What problems is the product solving and how is that benefiting you?
We use statsig to A/B test new features
A fully customized end-to-end A/B testing platform
What do you like best about the product?
This is my first time using a third-party A/B testing tool. I like that Statsig has a lot of advanced functions, such as stratification, CURE, Sequential testing, etc. These features enable complex tests and also guarantee the qualify of the tests. I'm learning from Statsig about what the best practices other companies have in testing to improve my tests. The UI is easy to use and understand. The customer support is very helpful, they provide customized solutions to challenges we met.
What do you dislike about the product?
It would be great if custom query feature could be more flexible, allowing more options when customizing, automatically include new dates of allocation for scheduled queries.
PM and analytics don't always know what setting are made for the test. It would be great if Stasig can make the implementation more visible to product so that non-eng stakeholders can get a better sense about how the test is set up. Like what's the condition applied, what feature gate is used, etc. So cross functional team can help evaluate whether the test is set up in the right way as designed.
PM and analytics don't always know what setting are made for the test. It would be great if Stasig can make the implementation more visible to product so that non-eng stakeholders can get a better sense about how the test is set up. Like what's the condition applied, what feature gate is used, etc. So cross functional team can help evaluate whether the test is set up in the right way as designed.
What problems is the product solving and how is that benefiting you?
Statsig helps evaluate whether a new product change has a positive impact on users
Statsig helps me move fast
What do you like best about the product?
I love how Statsig handles all of the statistics and math for us. This really helps us take the guesswork out on whether something is statistically significant. I also like how easy it is to evaluate an experiment on different axes.
What do you dislike about the product?
I think Statsig could do a better job at alerting when something is amiss. For example, I mistakenly had too many exposures and someone had to manually notice and message our team.
What problems is the product solving and how is that benefiting you?
I work on the marketing site and we have lots of "launches". I love how I can schedule changes to features and experiments.
Many features for growth engineering teams to run effective tests
What do you like best about the product?
Lots of features for both engineers and data scientists to make educated decisions on A/B experiments, such as filtering out outliers, custom assignment sources, winsorization, etc. Analyzing experiments from within the web interface is easy and there are many tools available such as the Explore queries where you can cut by custom dimensions, adjusting the CI percentage, adding and removing metrics. Additionally, customer support is very helpful and friendly whenever we have questions or encounter issues.
What do you dislike about the product?
Some of the "edge cases" are not clear to the user - Segments are not supposed to be more than 10K rows, but the system doesn't stop you from going over that limit - it simply becomes unstable once you do. Other things - when calling Statsig API from offline jobs (such as cron) there is a default 500 batch limit and a 60-second flush interval, so if your job has < 500 exposures and finishes in < 60 seconds, those exposures get lost unless you explicitly call Statsig.flush() before the pod exits.
What problems is the product solving and how is that benefiting you?
How new features perform on our platform, what kinds of user cohorts utilize vs. shy away from certain features. Also helps with gradual rollout / immediate rollback of risky changes.
Great for FF and even more
What do you like best about the product?
The feature flag dashboard is very flexible and easy to use.
Managing different environments is accessible and quite flexible.
SDK makes integration a breeze.
Managing different environments is accessible and quite flexible.
SDK makes integration a breeze.
What do you dislike about the product?
Organizing FF as their numbers grow is not as easy as I expected. Could be better
What problems is the product solving and how is that benefiting you?
Mostly feature gating, but also product analytics.
Great product, easy to use and insightful
What do you like best about the product?
Very easy to use, set up experiment and features gate is quite straightforward. Dynamic config is also quite feasible for daily operations
What do you dislike about the product?
It is a little hard to understand its limitation on throughput, e.g. whether we should put it into db or statsig dynamic config. If the dashboard / insight can be more powerful we can save lots to use any tools such as mixpanel
What problems is the product solving and how is that benefiting you?
Feature rollout and A/B testing.
It is much easier to do it in Statsig than building in house solution
It is much easier to do it in Statsig than building in house solution
A powerful and developer-friendly experimentation platform with room to grow
What do you like best about the product?
Statsig makes it easy to run experiments and manage feature flags with minimal setup. The SDKs are straightforward, the dynamic configs are flexible, and the event logging is well-structured. I especially appreciate how experimentation logic can stay on the backend and remain decoupled from UI, which fits perfectly with our architecture. The ability to evaluate flags and variants in real time using console rules is also incredibly powerful. Lastly, the team is responsive and open to feedback, which makes a real difference.
Very smooth. The SDK was easy to integrate into our backend service, and initial setup of feature gates and dynamic configs was quick. We were up and running with our first experiment in a matter of hours.
We use Statsig consistently for experimentation logic, feature rollout control, and real-time configuration updates. It’s now an essential part of our development and deployment workflow.
Straightforward. Backend integration (in our case, .NET) was well-supported. The SDK offers a clean API, and the event logging + variant evaluation flow was simple to embed into our existing services.
Very smooth. The SDK was easy to integrate into our backend service, and initial setup of feature gates and dynamic configs was quick. We were up and running with our first experiment in a matter of hours.
We use Statsig consistently for experimentation logic, feature rollout control, and real-time configuration updates. It’s now an essential part of our development and deployment workflow.
Straightforward. Backend integration (in our case, .NET) was well-supported. The SDK offers a clean API, and the event logging + variant evaluation flow was simple to embed into our existing services.
What do you dislike about the product?
The documentation could go a bit deeper in some areas — especially around advanced configuration and production-ready patterns. We also noticed that the console UI can feel a bit clunky at times when dealing with large numbers of configs or gates. Some limitations around segment targeting and rule flexibility required us to build custom logic on top.
What problems is the product solving and how is that benefiting you?
Statsig helps us decouple experimentation logic from frontend clients and manage feature rollouts safely and efficiently. It solves the complexity of running A/B tests by providing real-time evaluation, clear variant assignment, and automatic metric tracking — all without reinventing the wheel internally.
By using Statsig, we can confidently experiment with new features, validate assumptions with data, and gradually roll out changes with minimal risk. It’s also helping promote a culture of experimentation across teams by making the tooling accessible and reliable.
By using Statsig, we can confidently experiment with new features, validate assumptions with data, and gradually roll out changes with minimal risk. It’s also helping promote a culture of experimentation across teams by making the tooling accessible and reliable.
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