Best Statsig alternatives for warehouse-native experimentation

As data pipelines consolidate into data warehouses like Snowflake, BigQuery, Databricks and Athena, product experimentation is shifting away from black-box SaaS platforms toward warehouse-native solutions. These platforms let teams run A/B tests and analyze metrics directly in their warehouse where the source of truth lives removing the need to duplicate data or manage sync pipelines.

This blog shows the best Statsig alternatives with a focus on data-native architectures, SQL transparency, metric model management, and integration patterns.

1. Mitzu

• Positioning: Large datasets, high-volume of data
• Audience: Data engineers, analytics engineers, product data teams

Overview

Mitzu is the leading warehouse-native post-experimentation & product analytics tool designed for companies with large volume of data. It doesn’t move or replicate data; all transformations and analysis happen via SQL executed inside your warehouse.

Unlike 3rd party tools, Mitzu builds and manages journeys, cohorts, funnels through automated SQL generation. This reduces the need to rewrite or revalidate logic when experiment parameters or schemas evolve.

Key features

• Churn measurement, cohorts, retention, funnels
• Automated SQL generation for test/control splits, filtering, attribution
• Declarative metric definition (custom window functions, joins)
• Supports schema changes without disruption.
• Predictable costs tied to team size
• No reverse ETL required consumes warehouse tables directly
• Embeddable dashboards (Notion, Craft, Miro, internal tools)

How does Mitzu compare to Statsig?

Mitzu is ideal for teams who already have their data in their data warehouse, and have high volume datasets. Therefor it is ideal for Statsig enterprise clients.

2. Eppo

• Positioning: Doubtable after the acquisition by Datadog
• Audience: Regulated industries, ML experimentation

Overview

Eppo is a warehouse-native A/B testing platform built for environments where experimentation must align with compliance, reproducibility, and statistical rigor. Eppo runs all experiment calculations directly in your warehouse no hosted data layer, and no data movement outside your cloud perimeter.

It supports complex statistical methods, power analysis, CUPED, model evaluation, and personalization logic all expressed in SQL or through a metrics layer.

Key features

• Fully warehouse-native architecture (Snowflake, BigQuery, Redshift)
• Strong emphasis on auditability, data lineage, and metrics versioning
• Supports advanced experimentation types: personalization, contextual bandits, etc.
• Integration with metrics layers and ML platforms
• Built for teams that demand high statistical and infrastructure control

How does Eppo compare to Statsig?

While Statsig offers fast time-to-value with a hosted interface and SDK-based tracking, Eppo is for teams that already have mature event pipelines and want experimentation fully contained within their data platform.

3. Amplitude Experimentation

• Positioning: Product analytics + experimentation
• Audience: Product teams, PMs, analysts

Overview

Amplitude Experimentation is an extension of Amplitude’s product analytics platform, offering experimentation capabilities connected to Amplitude's data layer. While not purely warehouse-native, it allows warehouse syncing and supports both event-driven and metric-based experimentation.

Experiments can be set up via a UI, with automatic exposure tracking and real-time results. It’s optimized for speed and accessibility, not for full SQL ownership or reproducibility.

Key features

• Connected to warehouse via event sync (not native execution)
• Tight integration with Amplitude’s analytics platform
• UI-driven setup of experiments and feature flags
• Real-time results based on tracked user behavior
• Limited visibility into raw SQL or data lineage

How does Amplitude compare to Statsig?

Amplitude is better for product-led teams that prioritize ease of use and integration with behavioral analytics. Statsig offers more flexibility in how and where you run experiments, while Amplitude simplifies the end-to-end flow at the cost of transparency and data control.

4. Kameleoon

• Positioning: Enterprise experimentation + personalization
• Audience: Product, growth, experimentation engineers

What is Kameleoon?

Kameleoon is a hybrid experimentation platform that supports both client-side personalization and server-side A/B testing. It integrates with data warehouses to pull results for downstream reporting but doesn't run experiments natively inside the warehouse.

It supports advanced statistical techniques like CUPED, sequential testing, and allows real-time segmentation for personalization ideal for companies operating at large scale or requiring dynamic targeting.

Key features

• Supports both client-side and server-side experimentation
• Warehouse-integrated: fetches results, doesn’t compute them
• Real-time personalization and targeting
• Statistical methods: CUPED, sequential tests, multi-armed bandits
• Enterprise-grade security and compliance

How does Kameleoon compare to Statsig?

Kameleoon is better suited for teams running personalization at scale and needing real-time audience targeting. Kameleoon focuses on personalization and marketing-oriented use cases.

5. GrowthBook

• Positioning: Open-source, warehouse-integrated experimentation
• Audience: Dev-first teams, startups, infra-heavy orgs

Overview

GrowthBook is an open-source A/B testing framework that integrates with your data warehouse. It supports both client-side and server-side flagging, and includes robust support for running analyses in your own analytics stack (e.g. dbt, SQL, Jupyter).

Notable Features

• Developer-first: GitOps-friendly, infra-agnostic
• Supports custom statistical models
• Optional self-hosted backend for experiment tracking
• Can integrate with Airflow, CI/CD, or Looker
• Transparent logic, no vendor lock-in
• Modular: use only SDKs, or full platform

How does GrowthBook compare to Statsig?

GrowthBook is best if you already manage your own data platform stack and want to integrate experimentation directly into existing tools.

Closing Thoughts

The warehouse-native experimentation space is maturing fast. If your team owns the data stack , orchestration, observability and prefers building pipelines in SQL or code, you’ll want a platform that aligns with those workflows.

Choosing the right platform depends on priorities around data control, speed, scalability, and team autonomy.

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