LaunchDarkly has built an exceptional product. Its feature flag infrastructure is fast, reliable, and battle-tested by engineering teams at companies like IBM, Atlassian, and Intuit. If your primary need is safely rolling out new features to a percentage of users, managing kill switches, or running canary deployments, LaunchDarkly is a defensible choice.
But product and marketing teams searching for a "LaunchDarkly alternative" are usually not looking for a better feature flag tool. They are looking for something to do what LaunchDarkly was never really designed to do: run rigorous, statistically valid A/B experiments that answer the question "which version earns more money?"
That distinction matters more than most teams realize. And understanding it is the fastest way to choose the right tool before you have wasted six months running experiments that produce inconclusive results.
What LaunchDarkly Was Built to Do
LaunchDarkly is a feature management platform. Its core value proposition is giving engineering teams the ability to decouple feature deployment from feature release. You ship the code to production, but the feature stays off until you deliberately turn it on for a controlled segment of users.
This is enormously valuable for engineering teams. It eliminates the risky "big bang" release, reduces rollback complexity, and lets you test performance on a subset of production traffic before committing to a full rollout. LaunchDarkly has added experimentation features on top of this foundation, but the product was architectured around flags, not experiments.
- Feature flags with percentage rollouts and user targeting rules
- Experimentation layer built on top of flag infrastructure
- SDK-first workflow requiring engineering involvement for nearly every test
- Strong audit trail and flag lifecycle management
- Advanced user targeting based on custom attributes
- Pricing that scales with Monthly Active Users (MAUs)
Notice what is not in that list: a visual editor, a drag-and-drop experiment builder, an out-of-the-box statistical significance calculator, or a way for a marketing manager to run a headline test without filing a ticket. LaunchDarkly is a developer tool first. Experimentation is a secondary layer.
The Consequences of Using a Flag Tool for Experimentation
When you use a feature flag platform as your primary experimentation tool, you run into a set of compounding problems that gradually erode your ability to learn from your data.
Every test requires an engineer
In LaunchDarkly, running an A/B test on a landing page headline means an engineer wraps the headline in a flag variation, deploys the code, and waits. Your growth team cannot self-serve. Every hypothesis that needs testing becomes a backlog item. In a product that moves fast, this bottleneck compresses your experiment velocity from dozens of tests per quarter to a handful. The ROI math on that delta is brutal.
Statistical significance is your problem
LaunchDarkly's experimentation layer tracks metric events, but the statistical analysis is basic. Teams routinely peek at results before reaching significance, declare winners early, and ship decisions that were statistical noise. Purpose-built experimentation platforms calculate p-values continuously, warn you when you are peeking, and do not let you declare a winner before the math justifies it. That discipline is not a nice-to-have. It is the difference between an optimization program that compounds and one that produces random outcomes.
MAU-based pricing penalizes growth
LaunchDarkly charges based on Monthly Active Users. As your traffic grows, your bill grows proportionally. For companies running experiments at scale, this creates a perverse incentive: the more people you test on, the more you pay. Platforms built around experiment throughput, not user counts, eliminate this ceiling.
What to Look for in a Real A/B Testing Alternative
Before evaluating specific tools, it is worth being precise about what "better than LaunchDarkly for A/B testing" actually means in practice. The criteria that matter for revenue-focused experimentation are different from the criteria that matter for feature rollout management.
- Visual editor that lets non-engineers run tests without writing code
- Correct statistical methodology with Bayesian or frequentist significance testing, not just event counts
- Anti-flicker protection so visitors never see the control version before the variant loads
- Targeting rules for URL patterns, device type, referral source, and custom attributes
- Per-experiment traffic controls with weighted variant distribution
- Conversion goal tracking at the page level, not just at the SDK event level
- Pricing that does not scale linearly with visitor volume
Segmently vs LaunchDarkly: A Direct Comparison
Segmently was built for one thing: helping businesses find the version of their site that earns more money. Where LaunchDarkly starts from the engineering workflow and adds experimentation on top, Segmently starts from the hypothesis and builds backward toward implementation.
Setup time
Segmently installs with a single script tag. One line of HTML, and you have access to the full platform: visual editor, event tracking, assignment logic, anti-flicker protection, and analytics. The entire installation takes under ten minutes. LaunchDarkly requires SDK integration in your application code, which means touching the codebase, shipping a deployment, and verifying the integration before you can run a single experiment.
Who can run tests
With Segmently, a marketer can click on any element of their site inside the visual editor, change its text or styling, set a traffic split, define a conversion goal, and launch the experiment without touching a line of code. With LaunchDarkly, that same marketer needs an engineer to implement each variation. The productivity difference is not marginal. Teams using purpose-built A/B testing tools run three to five times more experiments per quarter than teams relying on flag-based approaches.
Statistical rigor
Segmently calculates statistical significance automatically and surfaces it in plain language. The dashboard tells you when a result is conclusive, what the confidence level is, and which variant is performing better on your defined conversion goal. There is no need to export data, paste it into a spreadsheet, or look up a chi-squared table. The platform handles the math so you can make the decision.
Anti-flicker protection
Flickering is when a visitor briefly sees the control version of a page before the variant loads. It happens when experiment logic runs asynchronously after the page has already rendered. It poisons test results (visitors who see both versions are a confound), damages user trust, and can trigger page speed penalties. Segmently injects anti-flicker CSS synchronously via document.write before any HTML is parsed, ensuring that bucketed visitors only ever see their assigned variant. This is a solved problem in purpose-built experimentation platforms. In flag-based tools, it is often an afterthought or left entirely to the developer to solve.
“Every experiment you cannot run is a revenue opportunity you will never recover. The bottleneck between your hypothesis and your data is the most expensive line item in your optimization budget.”
Segmently
When LaunchDarkly Is Still the Right Choice
This is not a dismissal of LaunchDarkly. There are clear scenarios where it is the better tool, and being honest about that distinction is more useful than a one-sided comparison.
- Your primary use case is feature rollout management, kill switches, and canary deployments
- Your engineering team runs experiments by wrapping code blocks in flags, and non-technical teams do not need self-serve access
- You are already deeply integrated with LaunchDarkly SDKs across a large codebase and migration costs outweigh experimentation velocity gains
- You need enterprise audit trails for feature state changes as part of a compliance requirement
- Your "experiments" are primarily infrastructure-level (database migrations, new API versions, infrastructure topology changes) rather than user-facing conversion tests
If any of those describe your situation, LaunchDarkly is a reasonable choice. The product is excellent at what it does. The question is whether what it does is what you actually need.
The Cost of the Wrong Tool
The most expensive A/B testing mistake is not running a bad experiment. It is running experiments so slowly, or with such poor statistical foundations, that you fail to accumulate the learnings your competitors are compounding. Experimentation velocity is a compounding advantage. Teams that run 50 experiments a year learn ten times more than teams that run five. The platform that enables the 51st experiment as easily as the first is the platform that creates durable competitive advantage.
If you are currently in LaunchDarkly and your growth or product team is filing tickets every time they want to test a headline, a button color, or a pricing layout, you are not running an optimization program. You are running an engineering program with occasional experiments attached. The distinction in output is measurable in revenue.
Making the Switch
Switching from LaunchDarkly to a purpose-built A/B testing platform does not require decommissioning your flags. Many teams run both: LaunchDarkly for infrastructure-level feature management, and Segmently for user-facing conversion experiments. The two tools solve genuinely different problems, and for teams that need both capabilities, running them in parallel is a reasonable architecture.
If your primary reason for using LaunchDarkly is A/B testing on marketing pages, landing pages, or product UI, the switch is clean. The Segmently snippet installs in minutes. Your first experiment can be live the same day. You do not need to migrate SDK integrations, retrain engineers, or wait for a sprint cycle.
The question is not whether a better tool exists for conversion experimentation. The question is how long you are willing to let the bottleneck compound before doing something about it.