Multi-armed bandit testing is an approach to web experimentation that automatically shifts traffic toward better-performing variants while the test is still running. Instead of splitting traffic 50/50 and waiting weeks for a result, a bandit algorithm explores all variants early on, then increasingly exploits the ones that perform best.
The name comes from the classic probability problem of a gambler facing multiple slot machines ("one-armed bandits"), trying to maximize total winnings by figuring out which machine pays out the most.
How It Differs from A/B Testing
| A/B Testing | Bandit Testing | |
|---|---|---|
| Traffic split | Fixed (usually 50/50) | Dynamic, based on performance |
| During the test | Half of traffic sees the losing variant | Traffic shifts toward winners |
| Duration | 2–6 weeks typical | Starts optimizing immediately |
| Variants | Usually 2 | Multiple simultaneously |
| Best for | High-stakes decisions needing rigorous proof | Speed-focused continuous optimization |
In a traditional A/B test, if variant B is clearly winning after week one, you still send 50% of traffic to the losing variant A for the remaining weeks. A bandit algorithm would have already shifted the majority of traffic to B.
Key Benefits
- Less wasted traffic — Fewer visitors see underperforming variants
- Faster results — The algorithm converges on winners 60–70% faster than fixed-split tests
- Multiple variants at once — Test 5–10 ideas simultaneously instead of sequentially
- Continuous optimization — Add or remove variants without restarting the experiment
When to Use Bandit Testing
Bandit testing works best when speed matters more than perfect certainty, when you have a backlog of ideas to test, and when every visitor seeing a losing variant represents a real cost. Traditional A/B testing is still appropriate for high-stakes, irreversible decisions where rigorous statistical proof is required.