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growthbook/growthbook

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7,351 Stars·672 Forks·TypeScript·other·15 Aufrufewww.growthbook.io↗

Growthbook

GrowthBook is a feature flagging and experimentation platform that utilizes a warehouse-native approach to data analysis. It serves as a system for managing feature rollouts and conducting A/B tests by executing SQL queries directly against existing data warehouses to calculate experiment results.

The platform is distinguished by its integration of a Model Context Protocol server, which allows AI coding assistants and IDEs to manage flags and query analytics using natural language. It also provides specialized capabilities for AI model optimization, enabling the testing of prompts and models against warehouse metrics for cost, latency, and satisfaction.

The system covers a broad range of operational capabilities, including progressive feature rollouts with emergency kill switches, advanced statistical analysis using Bayesian and frequentist frameworks, and warehouse-native analytics for defining custom business metrics via SQL. It also supports governance through experiment guardrails, role-based access control, and standardized metric libraries.

GrowthBook supports self-hosted installations, including air-gapped and on-premises deployments to meet strict data residency and compliance requirements.

Features

  • A/B Testing - Provides a comprehensive framework for conducting multivariate experiments and measuring statistical significance using warehouse data.
  • Warehouse-Native Analytics - Calculates metrics and experiment results by executing SQL queries directly within the user's existing data warehouse.
  • Feature Flagging Platforms - Provides a complete platform for managing feature rollouts and conducting A/B tests with warehouse-native analytics.
  • MCP Servers - Implements a Model Context Protocol server that allows IDEs to manage flags and query results using natural language.

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Model Context Protocol Servers - Implements a Model Context Protocol server to expose platform capabilities to AI coding assistants.
  • Warehouse-Native Model Benchmarking - Enables testing of LLM prompts and models against warehouse metrics for cost, latency, and satisfaction.
  • Statistical Inference Frameworks - Implements Bayesian, frequentist, and sequential frameworks to ensure statistically rigorous experiment results.
  • Attribute-Based Targeting - Delivers features based on flexible units such as user IDs, locations, postal codes, or URL paths.
  • Analytics Dashboards - Provides visual dashboards and SQL-based metric definitions for analyzing user behavior directly in the warehouse.
  • SQL-Based Analytics Engines - Calculates experiment results by executing SQL queries directly against data warehouses like Snowflake and BigQuery.
  • Cloud Data Warehouse Connectivity - Provides secure connectivity to external data warehouses to enable warehouse-native analysis and experimentation.
  • SQL Metric Definitions - Creates business KPIs using SQL to track conversion, latency, retention, and revenue directly from a warehouse.
  • API Client SDKs - Provides language-specific libraries for server, client, mobile, and edge environments to execute flags and experiments.
  • Feature Flag Management - Provides utilities for programmatically controlling feature availability and configuration through flag toggles and targeting rules.
  • Runtime Feature Toggles - Enables instant visibility changes and rollbacks of features without redeploying application code.
  • Dynamic User Targeting - Delivers specific feature experiences to defined user segments based on attributes or performance lift.
  • Automated Rollout Managers - Manages application updates through incremental rollout procedures and integrated emergency kill switches.
  • Feature Flags - Provides a system for managing and toggling feature flag configurations with support for gradual rollouts.
  • Evaluation Logic - Implements targeting rules to determine the correct feature value for specific users based on their attributes.
  • Feature Orchestration - Controls application behavior and feature availability in real-time through segmented user targeting for stability validation.
  • Variance Reduction Techniques - Uses CUPED and post-stratification to reduce noise in experiment data for faster statistical significance.
  • Statistical Analysis Libraries - Implements Bayesian and frequentist statistical models with variance reduction to calculate experiment impact.
  • Runtime Parameter Tuning - Tunes application behavior or UI layouts without redeploying code by returning booleans, strings, numbers, or JSON.
  • Rule Evaluators - Processes complex feature targeting rules on-device using cached JSON payloads to eliminate network latency.
  • Evaluation Engines - Embeds lightweight logic in applications to evaluate feature flags locally, reducing network latency.
  • AI-Powered Data Assistants - Provides a natural language interface for generating queries and discovering insights from warehouse data.
  • Experimentation Workflows - Scales testing processes through shared metrics, templates, approval workflows, and statistical guardrails.
  • Quantile Regression - Analyzes different percentiles of a distribution to determine how changes affect high-value and low-value users differently.
  • Collaborative Dashboards - Offers shared dashboards that combine SQL queries and explorations for collaborative team analysis.
  • Experiment Decision Frameworks - Generates automated recommendations on whether to ship a feature by applying a decision framework to experiment results.
  • Visual Data Explorers - Transforms warehouse data into interactive charts and pivot tables for visual exploration of user behavior.
  • Conversion Funnel Analytics - Evaluates single changes across multiple stages of the user journey from conversion to long-term retention.
  • Automated Dimension Slicing - Generates automated drill-down analyses for a single metric across multiple dimensions.
  • Natural Language to SQL - Translates natural language prompts into executable SQL queries to generate hypotheses and data explorations.
  • Event Data Ingestion - Collects user interactions through SDKs or APIs and stores them in a warehouse for analysis.
  • Event Schema Mapping - Generates SQL queries tailored to specific third-party event tracking software or custom data formats.
  • Third-Party Data Connectors - Imports user behavior data from third-party tracking tools to analyze experiment results.
  • Deterministic Hashing - Uses deterministic hashing of unique user attributes to ensure consistent assignment to experiment variations.
  • Identity Merging - Joins different identity types to enable consistent analysis across disparate data tables.
  • Dynamic Lookback Window Calculators - Calculates impact over extended periods using dynamic lookback windows for users encountering features at different times.
  • Multi-Dimensional Analysis - Breaks down experiment performance across user attributes like geography or platform using multi-dimensional analysis.
  • Retention Reporting - Tracks how many users return within a specific time window after experiment exposure using retention reporting.
  • Reusable Logic Definition - Defines reusable SQL-based metrics to ensure consistent measurement across all team experiments.
  • Standardized Metric Libraries - Designates a trusted set of standardized metrics via the interface to ensure consistency across teams.
  • Real-Time Runtime Updates - Pushes configuration changes to running applications instantly via Server-Sent Events to avoid service restarts.
  • Environment Configuration Management - Defines different flag values and targeting rules for development, staging, and production environments.
  • Automated Flag Wrapping - Automatically wraps features in flags using an MCP server to accelerate the experiment setup process.
  • Feature Flag Diagnostics - Inspects flags during development using a dedicated mode compatible with browser extensions.
  • Feature Payload Synchronization - Fetches, caches, and decrypts feature payloads from a remote host using custom HTTP headers.
  • AI-Assisted Migrations - Uses an AI-powered assistant to transform codebase and configurations when migrating from other platform providers.
  • Metrics-Aware Rollbacks - Automatically reverses feature rollouts when real-time stability metrics breach defined guardrail thresholds.
  • Dynamic Attribute Management - Updates user-specific properties to dynamically change feature assignments and targeting eligibility.
  • Hierarchical Group Targeting - Supports complex targeting logic through nested user groups that automatically propagate definition updates.
  • Targeting Logic Simulation - Simulates user segments and steps through targeting logic to verify which users receive specific feature variants.
  • Edge Rollout Management - Executes feature evaluation and redirects on CDN platforms to eliminate visual flicker and reduce latency.
  • Canary Deployment Controllers - Increases the percentage of users exposed to a new feature gradually to mitigate risk and detect bugs.
  • Automated Guardrail Rollbacks - Gradually ramps up feature traffic to specific segments while monitoring guardrail metrics for automatic rollbacks.
  • Feature Kill Switches - Provides emergency shutoffs to instantly disable features or trigger degradation during operational incidents.
  • Self-Hosted Deployment Platforms - Supports installation on private, on-premises, or air-gapped networks to provide full control over data residency.
  • Self-Hosted Infrastructure - Supports self-hosted, air-gapped, and on-premises deployments to meet strict data residency and compliance needs.
  • Version Target Controls - Allows controlling feature rollouts by matching app version strings against semantic versioning rules.
  • Dynamic Traffic Allocation - Shifts user traffic toward higher-performing variants in real-time to maximize conversion.
  • Edge-Side Feature Evaluation - Offloads targeting logic to proxy servers or CDN workers to prevent business rules from leaking to the client.
  • Server-Sent Events - Uses Server-Sent Events to push real-time feature configuration updates to clients without manual polling.
  • CUPED Variance Reduction - Applies regression adjustment using pre-experiment data to reduce noise and reach statistical significance faster.
  • Configuration Encryption - Encrypts sensitive targeting rules and attributes to prevent exposure to client-side environments.
  • Role-Based Access Control - Implements granular permissions and role-based access to control platform functionality and production approvals.
  • Attribute Pattern Matching - Supports regular expression matching on user attributes to handle varied data formats during targeting.
  • Cluster-Based Randomization - Assigns entire clusters, such as organizations, to the same experiment variant to prevent experience fragmentation.
  • Experiment Holdout Groups - Assigns users to project-level holdout groups to ensure consistent baseline measurement across all experiments.
  • Local-First Architectures - Processes targeting logic on-device using cached JSON payloads to eliminate network latency and enable offline operation.
  • Long-term Control Holdouts - Keeps a small control group from receiving any new features to measure cumulative impact over time.
  • Configuration Propagators - Synchronizes configuration changes to running applications using periodic polling or real-time streaming via Server-Sent Events.
  • Variation Persistence - Ensures users remain in the same experiment group even if targeting conditions or attributes change.
  • Administrative Change Auditing - Maintains versioned logs and approval workflows to audit all changes to feature flags and experiments.
  • Metric Dashboards - Aggregates metrics, dimension breakdowns, and external context into shareable visualization dashboards.
  • Metric and Performance Monitors - Provides tools to monitor high-frequency stability metrics during feature rollouts to prevent regressions.
  • Statistical Distribution Analysis - Calculates percentiles and statistical distributions to identify how changes affect extreme user experiences.
  • Experiment Guardrails - Defines approval flows and automated alerts to prevent failed tests from impacting users.
  • Prompt Configuration Testing - Provides tools for testing and validating specific LLM prompt configurations and parameters against performance and safety metrics.
  • Warehouse-Native Model Testing - Tests and rolls out LLM prompts and models against custom warehouse metrics for cost, latency, and satisfaction.
  • Traffic Routing - Gradually shifts traffic between different code paths to validate performance before full cutover.
  • General Analytics - Open-source A/B testing and feature flagging platform.
  • Feature Flags - Open-source platform for feature flagging and A/B testing.
  • Feature Management - Feature flags and A/B testing platform.

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    Häufig gestellte Fragen

    Was macht growthbook/growthbook?

    GrowthBook is a feature flagging and experimentation platform that utilizes a warehouse-native approach to data analysis. It serves as a system for managing feature rollouts and conducting A/B tests by executing SQL queries directly against existing data warehouses to calculate experiment results.

    Was sind die Hauptfunktionen von growthbook/growthbook?

    Die Hauptfunktionen von growthbook/growthbook sind: A/B Testing, Warehouse-Native Analytics, Feature Flagging Platforms, MCP Servers, Model Context Protocol Servers, Warehouse-Native Model Benchmarking, Statistical Inference Frameworks, Attribute-Based Targeting.

    Welche Open-Source-Alternativen gibt es zu growthbook/growthbook?

    Open-Source-Alternativen zu growthbook/growthbook sind unter anderem: unleash/unleash — Unleash is an open-source feature management platform designed to decouple feature releases from code deployments. It… flagsmith/flagsmith — Flagsmith is an open-source platform for managing feature flags and remote configuration across web, mobile, and… langchain-ai/deepagents — Deepagents is an LLM agent orchestration platform and stateful application server designed for deploying and managing… openpanel-dev/openpanel — OpenPanel is a self-hosted product analytics platform designed for tracking user behavior and visualizing product… dubinc/dub — This project is a comprehensive link management and marketing attribution platform designed for creating, tracking,… rybbit-io/rybbit — Rybbit is an open-source, self-hosted web analytics platform designed for comprehensive user behavior tracking and…

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