Statsig Developer Tier
StatsigReviews from AWS customer
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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
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
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