Multi-Armed Bandits vs A/B Testing: When to Use Each

The Explore-Exploit Dilemma

Every time you run an A/B test, you face a fundamental tension: you want to explore different options to find the best one, but you also want to exploit what you already know works. Traditional A/B testing handles this by splitting traffic evenly for a fixed period, then switching to the winner. Multi-armed bandits take a smarter approach.

In a classic A/B test, if variant B is clearly winning after 1,000 visitors, you still send 50% of traffic to the losing variant A until the test concludes. That's lost revenue. Multi-armed bandits dynamically shift traffic toward the winning variant while still exploring alternatives.

How Multi-Armed Bandits Work

The name comes from the "one-armed bandit" (a slot machine). Imagine you're in a casino with multiple slot machines, each with a different (unknown) payout rate. How do you maximize your winnings?

Bandit algorithms solve this by maintaining a probability model for each variant's success rate. As data comes in, the model updates, and traffic allocation shifts accordingly. The three most popular algorithms are:

Thompson Sampling

Uses Bayesian probability to model each variant's conversion rate as a Beta distribution. Each time a visitor arrives, it samples from each distribution and sends the visitor to whichever variant produced the highest sample. This naturally balances exploration and exploitation.

Upper Confidence Bound (UCB1)

Selects the variant with the highest upper confidence bound—essentially choosing the variant that could be the best given the uncertainty. As more data is collected, uncertainty shrinks and the algorithm converges on the true winner.

Epsilon-Greedy

The simplest approach: with probability ε (say 10%), explore a random variant. Otherwise, exploit the current best. Simple but effective for many use cases.

When to Use A/B Testing

• You need clean statistical evidence — A/B tests produce clear p-values and confidence intervals that stakeholders understand
• You're making a permanent decision — like a site redesign where you want definitive proof before committing
• Regulatory requirements — some industries require formal hypothesis testing with predetermined sample sizes
• You have high traffic — with enough visitors, the "cost" of equal split testing is minimal

When to Use Bandits

• Time-sensitive promotions — during a flash sale, you can't afford weeks of equal-split testing
• Many variants to test — testing 10+ headline variations is impractical with A/B tests but natural for bandits
• Continuous optimization — when there's no "end" to the test and you want to perpetually optimize
• Low traffic — bandits minimize the cost of exploration when every visitor counts
• Non-stationary environments — if the best variant changes over time (seasonal effects, trending topics)

A Practical Example

Suppose you're testing 4 different CTA buttons. With A/B testing, each gets 25% of traffic for the entire test duration. After 2,000 visitors, you find the winner.

With Thompson Sampling, after just 200 visitors, the algorithm might already be sending 60% of traffic to the leading variant, 20% to the runner-up, and 10% each to the others. By visitor 2,000, it could be sending 95% to the winner while still occasionally checking the others.

The result: you get a similar conclusion but with significantly higher total conversions during the test period.

Using Bandits in Experiment Flow

Experiment Flow supports both approaches. When creating an experiment, simply toggle "Bandit Mode" to switch from fixed A/B testing to Thompson Sampling. The system automatically:

• Maintains Beta distributions for each variant
• Dynamically allocates traffic based on performance
• Provides real-time performance dashboards
• Still calculates statistical significance so you know when a winner is clear

// Enable bandit mode via API
fetch('/api/experiments', {
  method: 'POST',
  headers: { 'X-API-Key': 'your-api-key' },
  body: JSON.stringify({
    name: 'CTA Button Test',
    variants: ['Buy Now', 'Get Started', 'Try Free', 'Sign Up'],
    bandit_mode: true
  })
})

The Bottom Line

A/B testing and multi-armed bandits aren't competing approaches—they're complementary tools. Use A/B tests when you need rigorous evidence. Use bandits when you need to optimize continuously or can't afford the cost of equal-split testing. The best optimization programs use both.