Statsig Launches Warehouse Native, Bringing Powerful Experimentation to Product Teams on the Modern Data Stack

Empowers businesses to experiment faster and streamline software releases while ensuring data security.

Statsig, a leading provider of experimentation and product management solutions, announced the launch of Statsig Warehouse Native: a powerful, modular experimentation engine that runs natively within an organization’s data warehouse. This offering allows product and data teams to analyze experiments in minutes versus days, and streamline the entire workflow of software releases, A/B testing, and product analytics. With this release, Statsig is the first experimentation platform to provide both warehouse-native and cloud-hosted implementation options.

The highest-performing product teams run multiple experiments every day. Despite knowing this, most organizations use outdated A/B testing methods that are inefficient, error-prone, and turn data teams into bottlenecks for analysis. Statsig helps businesses innovate faster with an integrated suite of tools. Teams can run hundreds of experiments in parallel and get near real-time visibility into how features are impacting business metrics. With this knowledge, organizations can rapidly unlock data-driven decision-making at scale and be confident they are shipping the most impactful features to users. Additionally, product teams can stop overpaying for multiple point solutions and inefficient data connections by moving to a fully integrated platform.

With Statsig Warehouse Native, teams can choose to use Statsig solely for experiment analysis in their warehouse, leverage SDKs for end-to-end targeting and feature rollouts, or even integrate offline experiments as part of a hybrid solution.

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“Through feedback from product and data science leaders, we gained deep insights into how we could further enhance our platform and tools”

Capabilities of the platform include:

• Experiment analysis that runs inside the data warehouse from existing source-of-truth datasets.
• A robust stats engine equipped with CUPED, sequential analysis, as well as holdouts and layers (universes) for more sophisticated experimentation.
• Complete visibility into analysis with no-code tools to segment and drill down into results.
• Integrated SDKs supporting feature flags, experimentation, remote configuration, and other tools for managing software releases.
• Product insights, and dashboards for comprehensive data-driven decision-making.
• Enterprise-grade change management features including role-based access control, approval workflows, and auditing capabilities.

“Through feedback from product and data science leaders, we gained deep insights into how we could further enhance our platform and tools,” said Vijaye Raji, Statsig Founder and CEO. “We discovered that data science teams value quick iteration, governance, and the ability to avoid duplicating core business metrics in multiple systems.”

Early adopters have been using the new tools with positive results. “Statsig saves our team over 12 hours of analyst time per experiment by seamlessly integrating with our existing Snowflake data,” said Mark Phuong, Sr. Data Scientist, Black Crow AI. “We’re operating like a large experimentation organization at an enterprise tech company, effectively organizing, tracking, and analyzing multiple experiments in parallel. The platform has accelerated our data literacy and maturity, providing intuitive visualizations that enable even non-technical users to make informed business decisions.”

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