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 fundamentally different audiences with different goals. LaunchDarkly is a feature flag management platform primarily serving software engineering teams who need to safely roll out code features to subsets of users. CustomFit.ai is a CRO platform built for D2C marketing and growth teams who need to run A/B testing experiments, personalize ecommerce experiences, and increase conversion rate and revenue per visitor. If you're a marketer at a D2C brand, you need CustomFit.ai. If you're a software team rolling out backend features safely, you need LaunchDarkly. Very few organizations need to choose between them โ they serve different functions.
| 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 |
This is the most important thing to understand:
LaunchDarkly is built for software development teams. Feature flags let engineers turn features on or off for specific users without deploying new code โ enabling safe rollouts, canary releases, beta testing with specific user segments, and quick kill switches if 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. The visual editor, no-code experiment creation, audience segmentation by behavior and demographics, and real-time revenue tracking are designed so marketers can run tests independently โ no engineering ticket required.
If your team is asking "how do we test this new product page layout without a developer?" โ 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?" โ that's LaunchDarkly.
CustomFit.ai installs from the Shopify App Store in one click. No developer involvement, no SDK integration, no code changes. Ecommerce events are tracked automatically. First experiment: same day.
LaunchDarkly requires SDK integration into your application code. A developer needs to wrap features in feature flag logic, connect the SDK to LaunchDarkly's service, and configure user contexts. For a non-technical marketer, LaunchDarkly is inaccessible without engineering support.
CustomFit.ai โ designed for marketing-led experimentation:
LaunchDarkly โ designed for engineering-led rollouts:
LaunchDarkly's experimentation is powerful for measuring whether a new feature improves engagement or reduces error rates. It's not designed to answer "which headline on our product page gets more people to add to cart" โ that's a marketing question requiring a marketing tool.
CustomFit.ai's personalization engine uses 1000+ attributes โ device, location, traffic source, cart value, purchase history, behavioral intent โ to serve different experiences to different audiences automatically. This is the kind of personalization that D2C brands need to segment first-time visitors from returning customers and serve each the most relevant experience.
LaunchDarkly's "targeting" is context-based โ you can target flags based on user attributes your engineers pass into the SDK. This works for technical rollouts but 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 โ natively connected 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 require custom event tracking setup. For a D2C marketer wanting to see "how much more revenue did variant B generate versus variant A," LaunchDarkly requires 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, this is far more than you need. CustomFit.ai at $99โ$249/month delivers the marketing experimentation capabilities D2C brands 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 instantly kill features 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. But it's not a replacement for a marketing-facing CRO platform โ it's a complement to one. Sophisticated 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 requires 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. The experimentation configuration, metric setup, and result interpretation all assume engineering context. It's not 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 โ primarily a deployment and rollout tool. A/B testing is a structured experiment comparing 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. Ecommerce events are tracked automatically. Marketers can run experiments without involving engineering.
How does statistical significance work in CustomFit.ai? CustomFit.ai automatically calculates statistical significance as your experiment collects data, displaying results in plain language โ no statistics knowledge 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; marketing teams use CustomFit.ai for revenue optimization experiments. Both can run simultaneously on the same Shopify store.