Frequently asked questions
What qbrix is, when to use it, how fast it is, and how to connect it to your app.
qbrix continuously decides which option to show and shifts traffic toward winners in real time, so you stop spending half your traffic on options you already suspect are worse. A/B testing splits traffic evenly and waits until the test ends to pick a winner. qbrix learns as it runs, steering toward whatever performs best while the run is still live.
Use qbrix when the outcome resolves quickly (clicks, conversions, add-to-cart), when you run many continuous optimizations, or when you care about results during the run rather than just a clean verdict after. Stick with a classic A/B test when you need a rigorous causal estimate of every variant, or when outcomes land long after the decision (for example email), which breaks the fast-feedback loop adaptive methods rely on.
qbrix is a managed, API-first system for real-time decisioning and adaptive optimization. You call select to get a decision and feedback to report the outcome; qbrix handles the learning and traffic allocation, continuously steering toward whatever performs best. It is a dedicated decisioning system — not a feature inside a feature-flag or analytics platform, and not a library you operate yourself.
qbrix is delivered as a managed cloud service, and our Python and JavaScript/TypeScript SDKs are open source — so you operate no infrastructure.
Those are broad platforms — feature flags or experimentation suites — where adaptive optimization is one feature among many. qbrix is purpose-built for real-time, API-first decisioning: continuous optimization is the entire product, with far deeper adaptive capability than a single bolted-on feature.
Most users do not choose — qbrix's auto mode runs several policies in parallel and automatically routes traffic toward whichever performs best on your data. If you want manual control, qbrix also lets you select a specific policy by reward type and whether you use context features. See the policies reference →
qbrix serves decisions on a dedicated hot path that makes zero database calls, reading parameters from an in-memory cache. Selection replicas are stateless and scale horizontally to sustain high request volume. Learning runs off the hot path, so model updates never slow down decisions.
Yes. qbrix can use a feature vector describing the user or request to personalize each decision, rather than treating every visitor the same. You enable it by setting a context dimension on your experiment. Read about contexts →
Through a simple HTTP API with two core calls — select and feedback — plus open-source Python and JavaScript/TypeScript SDKs. Authentication is via API key, and you can be live in minutes. Start with the quickstart →
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