How GrowthBook's Warehouse-Native Approach Works

GrowthBook is warehouse-native, which means it connects directly to your existing data warehouse and computes experiment and analytics results from the data already stored there, rather than copying that data into a separate GrowthBook-managed store. This matters most if your product events and user attributes already live in a warehouse such as your analytics stack, and you want experimentation, feature flags, and product analytics to run against that same source of truth. GrowthBook offers both a cloud option and a self-hosted option, and per the site both keep the warehouse-native model.

What "warehouse-native" actually changes

In a conventional experimentation tool, you typically send events to the vendor, and the vendor stores and processes them on its own infrastructure. Warehouse-native flips that: the warehouse stays the system of record, and the platform queries it.

The practical consequences:

• No second copy of your data. Events and user attributes are not duplicated into a vendor-specific store, so there is less data movement to maintain.
• Consistency with existing reporting. Because metrics are computed from the warehouse, experiment results and your normal analytics draw from the same tables and definitions.
• Security and governance stay where they are. Access controls, retention, and permissions you already apply to the warehouse continue to apply.
• One platform across three jobs. The site positions experimentation, feature flags, and product analytics as a single platform, all operating against connected product data.

The two halves: assignment vs. analysis

A useful way to understand the architecture is to split it into what happens at runtime and what happens afterward.

Runtime: feature flags and assignment

Feature flags decide which variation a user sees. This has to be fast and available, so it does not wait on a warehouse query. The site cites scale signals such as "1T+ feature flag lookups per day" and Dropbox at "3 billion feature evaluations daily," which indicates the flag-evaluation path is built for high volume. Flag management also includes safety mechanics the site calls out: auto-rollbacks and ramp schedules with guardrails, aimed at letting teams and AI coding agents ship faster without unmanaged risk.

Analysis: metrics from the warehouse

Once users are assigned, the outcome measurement runs against warehouse data. GrowthBook connects to your product data so teams can understand behavior, track trends, and find opportunities, and the site mentions an AI Analyst for querying that data. This is the part that is genuinely warehouse-native: the metric computation reads from your tables instead of a vendor-side event stream.

What you need for it to work

Warehouse-native is not automatic. It assumes:

1. A data warehouse you control, with product event and user data already flowing into it.
2. A connection from GrowthBook to that warehouse, so it can read the tables needed for metrics.
3. Defined metrics and experiment assignments that map onto those tables.
4. A deployment choice: GrowthBook Cloud for "instant updates, low maintenance, and warehouse-native," or Self-Hosted for "same features, same product, 100% on your cloud or on prem."

If your events are not in a warehouse yet, the warehouse-native benefit is mostly prospective until that pipeline exists.

Cloud vs. self-hosted, on the same model

The site frames these as the same product with the same features, differing in who operates it. Choose cloud when you want to avoid running infrastructure; choose self-hosted when the product must stay entirely inside your own environment.

Where the approach pays off, and where it doesn't

It tends to fit teams that already treat the warehouse as the source of truth and want experiment results to match their existing analytics. The site's customer examples point in this direction: Wikimedia Foundation scaling experimentation for 15B+ monthly page views with +30% account creation, Breeze Airways reporting $1MM+ incremental monthly revenue, and Fyxer going from $1M to $35M ARR with paid conversion moving from 5% to 35%. These are outcome claims from GrowthBook's own customer stories, not independent benchmarks, so treat them as directional.

It fits less well if you have no warehouse, if your event data is fragmented across tools, or if you need a vendor to own the entire data pipeline for you.

Common points of confusion

• "Warehouse-native" does not mean everything is a warehouse query. Flag evaluation at runtime is a separate, low-latency path; the warehouse-native part is primarily the analysis and metrics layer.
• It is not the same as "no data leaves your systems." That depends on whether you use cloud or self-hosted. The site describes self-hosted as 100% on your cloud or on prem.
• Open source and warehouse-native are separate properties. The site describes GrowthBook as open source and fully transparent, which is a licensing and code-access point, distinct from where computation happens.

To evaluate it for your case, start from the two questions that determine fit: is your product data already in a warehouse you control, and do you want GrowthBook to operate the platform or run it yourself? The site's pricing page is the place to check current plan details, since pricing specifics are not covered in the material here.