Warehouse-native experimentation is an architecture where experiment data (assignments, exposures, metric events, and analysis results) lives in the customer's own data warehouse rather than being copied into the vendor's infrastructure. The experiment platform reads from and writes to BigQuery, Snowflake, Redshift, or Databricks directly. The vendor never stores your raw user-level data.
This is a structural choice, not an integration feature. Most experimentation platforms are SaaS-first: they ingest your data, store it in their own systems, run analysis on their compute, and display results in their UI. A warehouse-native platform inverts this. The data stays where it already lives, under your governance policies, your access controls, and your retention rules. The platform runs the analysis inside your warehouse.
Why does warehouse-native matter?
The conventional SaaS model for experimentation creates three problems that compound as you scale.
Data duplication. To analyze an experiment, the platform needs your metric data. In a SaaS model, you export that data to the vendor: user events, session logs, revenue data, product metrics. Now you have two copies of your most sensitive behavioral data, governed by two different systems, two different retention policies, and two different security postures. Every new experiment metric you add means another data pipeline to maintain.
Governance gaps. Your data warehouse has access controls, audit logs, and compliance policies your organization has already built and verified. When experiment data lives in a vendor's infrastructure, those controls don't apply. You're trusting the vendor's security posture for data that often includes user-level behavioral records, A/B test assignments tied to user IDs, and metric values derived from business-critical events.
Analysis bottlenecks. When experiment data lives in the vendor's system but related business data lives in your warehouse, joining them requires export/import cycles. An analyst who wants to segment experiment results by a custom dimension that only exists in your warehouse needs to move data between systems. That friction is why, at many companies, the most interesting experiment analyses never get run.
Warehouse-native experimentation eliminates all three. One copy of the data. Your existing governance. No export/import friction. The analysis runs where the data already is.
How does Confidence implement warehouse-native?
Confidence is warehouse-native by primary architecture, not as an add-on mode. Here's what that means concretely.
Assignment and exposure logging writes directly to your warehouse. When a user is assigned to a variant, the assignment event goes to BigQuery, Snowflake, Redshift, or Databricks. Confidence doesn't store a copy.
Metrics are computed in your warehouse. You define metrics using your own data (tables, columns, SQL expressions). Confidence's analysis engine runs the statistical calculations (CUPED variance reduction, sequential testing, multiple testing corrections, guardrail checks) as queries inside your warehouse environment. The raw data never leaves your infrastructure.
Results are stored in your warehouse. The output of the analysis (point estimates, confidence intervals, p-values, decision recommendations) writes back to your warehouse. You can query experiment results with the same tools you use for everything else.
This architecture reflects a core belief stated in the Confidence manifesto: your data belongs to you. Assignment data, exposure logs, and behavioral events are the intellectual history of your product development. They should live alongside your user data and business metrics, not in a vendor's system.
What's the difference between warehouse-native and warehouse-connected?
Several experimentation platforms advertise warehouse integrations. The distinction matters.
Warehouse-connected means the platform can read from your warehouse (usually to import metric data) and sometimes write results back. The platform still has its own data store where it does the primary processing. Your warehouse is a data source, not the system of record.
Warehouse-native means the warehouse is the primary data store. There's no separate vendor database holding your experiment data. The analysis runs inside the warehouse. Nothing is copied out.
Confidence is warehouse-native. The warehouse isn't an integration point. It's the architecture.
Does warehouse-native mean you need a data team?
You need a data warehouse with your metric data in it. If your company already uses BigQuery, Snowflake, Redshift, or Databricks for analytics, you have what you need. Confidence connects to the existing tables.
You don't need a dedicated data engineering team to maintain export pipelines, because there are no export pipelines. You don't need to build a metrics layer in the vendor's system, because metrics are defined against your own tables. The setup work is connecting Confidence to your warehouse and pointing it at the right tables, which a data analyst or engineer can do in the same day.
For companies early in their data maturity, Confidence's managed service handles the statistical methodology. The warehouse-native architecture means you still own the data from day one, so you don't accumulate migration debt if your data infrastructure evolves later.