Concepts referenced in this article, defined.
Run rigorous A/B tests and personalize every visit on Shopify or any storefront โ no engineers required.
LaunchDarkly and CustomFit.ai are built for different audiences with different goals. LaunchDarkly is a feature flag management platform aimed mostly at software engineering teams who need to roll out code features to subsets of users safely. CustomFit.ai is a CRO platform for D2C marketing and growth teams who run A/B testing experiments, personalize ecommerce experiences, and improve conversion rate and revenue per visitor. If you're a marketer at a D2C brand, you want CustomFit.ai. If you're a software team rolling out backend features, you want LaunchDarkly. Very few organizations actually have to pick between them, because the two tools do different jobs.
| Feature | CustomFit.ai | LaunchDarkly |
|---|---|---|
| Target user | Marketers / growth teams | Software engineers |
| Shopify native integration | โ | Via SDK (requires developer) |
| No-code visual editor | โ | No |
| A/B testing (marketer-controlled) | โ | Requires engineering |
| Feature flag management | No | โ (core capability) |
| D2C/ecommerce metrics (AOV, RPV) | โ | Requires custom setup |
| AI-powered optimization | โ | No |
| 1000+ audience targeting attributes | โ | Moderate (context-based) |
| Personalization engine | โ | No |
| 14-day free trial | โ | โ |
| Starting price | $99/mo | ~$20/mo per seat (scales steeply) |
| Developer required | No | Yes |
| Purpose | CRO / Marketing optimization | Feature delivery / Engineering |
The most important thing to understand is who each tool is for.
LaunchDarkly is built for software development teams. Feature flags let engineers turn features on or off for specific users without deploying new code, which supports safe rollouts, canary releases, beta testing with specific user segments, and quick kill switches when something breaks. LaunchDarkly's "experimentation" module lets engineers measure the impact of code changes on metrics.
CustomFit.ai is built for marketing and growth teams. Its visual editor, no-code experiment creation, audience segmentation by behavior and demographics, and real-time revenue tracking let marketers run tests on their own, without filing an engineering ticket.
If your team is asking "how do we test this new product page layout without a developer?", that's CustomFit.ai. If your engineering team is asking "how do we safely roll out this new checkout flow to 10% of users before full deployment?", that's LaunchDarkly.
CustomFit.ai installs from the Shopify App Store in one click. There's no developer involvement, no SDK integration, and no code changes, and ecommerce events are tracked automatically. You can launch your first experiment the same day.
LaunchDarkly requires SDK integration into your application code. A developer has to wrap features in feature flag logic, connect the SDK to LaunchDarkly's service, and configure user contexts. For a non-technical marketer, LaunchDarkly isn't usable without engineering support.
CustomFit.ai is designed for marketing-led experimentation:
LaunchDarkly is designed for engineering-led rollouts:
LaunchDarkly's experimentation works well for measuring whether a new feature improves engagement or reduces error rates. It isn't built to answer "which headline on our product page gets more people to add to cart," which is a marketing question that needs a marketing tool.
CustomFit.ai's personalization engine uses 1000+ attributes, including device, location, traffic source, cart value, purchase history, and behavioral intent, to serve different experiences to different audiences automatically. This is the kind of personalization D2C brands rely on to separate first-time visitors from returning customers and give each group the most relevant experience.
LaunchDarkly's "targeting" is context-based: you can target flags based on user attributes your engineers pass into the SDK. That works for technical rollouts, but it doesn't give marketers self-service personalization without engineering involvement at every step.
CustomFit.ai tracks revenue per visitor, average order value, add-to-cart rate, and conversion rate by experiment variant, all connected natively to Shopify's order data. You see revenue impact in dollars, not just statistical lift.
LaunchDarkly's metrics are configurable and can track business metrics when properly instrumented, but that takes custom event tracking setup. For a D2C marketer who wants to see "how much more revenue did variant B generate versus variant A," LaunchDarkly needs significant engineering setup to produce that answer.
| Plan | CustomFit.ai | LaunchDarkly |
|---|---|---|
| Starter | $99/mo (50K MUV) | ~$20/mo per seat (minimum seats apply) |
| Scale | $249/mo (100K MUV) | Scales significantly with seats and usage |
| Enterprise | Custom | Custom (typically $50K+/year) |
| Free trial | 14 days, no credit card | โ |
LaunchDarkly's pricing scales with seats and usage, and enterprise contracts can run into tens of thousands of dollars per year. For marketing-led CRO, that's far more than you need. At $99 to $249 per month, CustomFit.ai gives D2C brands the marketing experimentation capabilities they need at a fraction of the cost.
LaunchDarkly is the right tool for software engineering teams that need feature flag management, safe progressive rollouts, and the ability to kill features instantly if they cause issues. If you're a D2C brand with a significant technology team building a custom storefront or native app, LaunchDarkly solves real engineering problems. It isn't a replacement for a marketing-facing CRO platform, though. It's a complement to one. D2C brands with engineering teams sometimes use both: LaunchDarkly for safe feature releases, CustomFit.ai for marketing experiments.
If you're using LaunchDarkly for marketing A/B tests (which you've probably found frustrating to manage without engineering), here's how to move to CustomFit.ai:
Is LaunchDarkly an A/B testing tool? LaunchDarkly has an experimentation module, but it's designed for engineering teams measuring the impact of code changes, not for marketers running no-code page tests. Creating and managing marketing experiments in LaunchDarkly takes engineering involvement at every step.
Can marketers use LaunchDarkly without a developer? Not effectively. LaunchDarkly's core workflow requires developers to instrument feature flags in code, and the experimentation configuration, metric setup, and result interpretation all assume engineering context. It isn't designed for self-service marketing experiments.
What's the difference between feature flags and A/B testing? Feature flags control which users see which code features, so they're mainly a deployment and rollout tool. A/B testing is a structured experiment that compares two or more variants of a user experience to determine which drives better outcomes. LaunchDarkly does feature flags. CustomFit.ai does A/B testing and personalization.
Does CustomFit.ai require developer setup? No. CustomFit.ai installs from the Shopify App Store in one click, the visual editor is no-code, and ecommerce events are tracked automatically. Marketers can run experiments without involving engineering.
How does statistical significance work in CustomFit.ai? CustomFit.ai calculates statistical significance automatically as your experiment collects data and shows the results in plain language, so no statistics knowledge is required. You set a confidence threshold (usually 95%) and the platform tells you when you've reached it.
Can I use CustomFit.ai and LaunchDarkly together? Yes. They serve different purposes and don't conflict. Engineering teams use LaunchDarkly for safe feature rollouts, while marketing teams use CustomFit.ai for revenue optimization experiments. Both can run at the same time on the same Shopify store.