Feature Management
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Data Warehouse
Dynamic Configs
Metrics
Holdouts
Insights
Dashboards
Autotune
Documentation
Walkthrough Guides
SDKs and APIs
Open Source Code
Integration
Product Updates
How Statsig Works
Build vs. Buy
Experimentation Community
Peak Velocity
Customer Stories
A/B Testing Calculator
Feature Flags Liberated
Optimizing AI Model Temp
What is Product Observability?
Experiments with Generative AI
Experimentation Platforms
CUPED Explained
Feature Management
Experimentation
Data Warehouse
Dynamic Configs
Metrics
Holdouts
Insights
Dashboards
Autotune
Documentation
Walkthrough Guides
SDKs and APIs
Open Source Code
Integration
Product Updates
How Statsig Works
Build vs. Buy
Experimentation Community
Peak Velocity
Customer Stories
A/B Testing Calculator
Feature Flags Liberated
Optimizing AI Model Temp
What is Product Observability?
Experiments with Generative AI
Experimentation Platforms
CUPED Explained
PEAK VELOCITY: THE STATSIG BLOG
Featured Articles
Seattle AI Meetup: The wave is here
AI technology has been here for years, but the new wave of AI products and features is game-changing. We covered this, and other topics, at the Seattle AI Meetup.
How to create an experiment review culture (with templates!)
Building a culture of experimentation benefits greatly from things like reviewing experiments regularly and discussing the results.
Why people love Statsig’s customer support
Thanks to our support team, our customers can feel like Statsig is a part of their org and not just a software vendor. We want our customers to know that we're here for them.
What migrating experimentation platforms actually entails
Migrating experimentation platforms is a chance to cleanse tech debt, streamline workflows, define ownership, promote democratization of testing, educate teams, and more.
Understanding sample size
Calculating the right sample size means balancing the level of precision desired, the anticipated effect size, the statistical power of the experiment, and more.
Recency bias: The silent saboteur in statistics
The term 'recency bias' has been all over the statistics and data analysis world, stealthily skewing our interpretation of patterns and trends.
Early startup journey: Year two at Statsig
A lot has changed in the past year. New hires, new products, and a new office (or two!) GB Lee tells the tale alongside pictures and illustrations:
CUPED Explained
A deep dive into CUPED: Why it was invented, how it works, and how to use CUPED to run experiments faster and with less bias.
Statsig x Langchain: Unlocking experimentation in Langchain apps using Statsig
With the statsig-langchain package, developers can set up event logging and experiment assignment in their Langchain application within minutes, unlocking online experimentation in Langchain applications
Statsig @ Data Council 2023
As a data meeting, buzzwords were well-represented at Data Council 2023; Generative AI, the Semantic Layer, data observability—and did we say AI?
The benefits of using feature flags
The benefits of feature flagging are plentiful and all relate to the ability to control and gather metrics around individual features rather than an app build as a whole.
How to build an effective feature flagging and experimentation RFP
To ensure that your feature flagging and experimentation platform RFP meets your business needs, it's important to prepare before diving into writing the RFP.
AI Experimentation: How to optimize the performance of AI-powered features using Statsig (Webinar Recording)
Join the discussion: How to record important model inputs and outputs such as prompt, model choices, cost, and latency, and also measure performance and interpret results.
Experimenting on cached content
Sophisticated caching architecture ensures optimal performance when serving pages, assets, and API responses—and also caches responses for experimentation.
Online experimentation: The new paradigm for building AI applications
To embrace online testing of AI models, companies need the right set of tools to rapidly iterate on new ideas, learn from failures, and more on.
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