Superwall vs RevenueCat vs Adapty: a developer's comparison

If you're building a subscription app and looking at paywall tooling, you've probably landed on three names: Superwall, RevenueCat (specifically their Paywalls feature), and Adapty. All three let you create and manage paywalls remotely, run A/B tests, and iterate on monetization without app updates. But they differ meaningfully in architecture, developer experience, and trade-offs.

This is a comparison written from a developer's perspective — focused on how these tools actually work under the hood, not what their marketing pages say.

Architecture overview

All three are dashboard-first tools. You design paywalls in a web interface, configure targeting through form fields, and manage experiments by clicking buttons. The difference is in how they render paywalls and what else they bundle.

Rendering approach

Superwall renders paywalls using web views. You design the paywall in their dashboard editor (or provide custom HTML/CSS), and the SDK displays it in a WKWebView on iOS or a WebView on Android. This gives you maximum design flexibility — anything you can do in HTML, you can do in a Superwall paywall. The trade-off is performance and feel. Web views have a noticeable loading delay, don't match platform interaction patterns, and can look out of place in a native app. Users who are sensitive to polish will notice.

RevenueCat Paywalls renders natively using SwiftUI on iOS and Jetpack Compose on Android. You choose from a set of predefined templates and configure them with copy, images, and colors. The result looks and feels native because it is native — no web layer involved. The limitation is flexibility: you're constrained to the templates RevenueCat provides. If you need a layout that doesn't match a template, you're either building it yourself or waiting for them to add one.

Adapty takes a similar approach to RevenueCat with native rendering from templates, but offers a more visual builder in their dashboard. They provide more template options and more customization within those templates. The rendering quality is good, but you're still working within the bounds of what their template system supports.

Integration complexity

Superwall is the easiest to integrate if you just want to show paywalls — a few lines of code to configure the SDK and register placements. But it doesn't handle subscription management, so you still need StoreKit or a service like RevenueCat for purchase handling, receipt validation, and entitlement management. This means two SDKs, two dashboards, and two sets of analytics to reconcile.

RevenueCat has the advantage of being a full subscription infrastructure platform. If you're already using RevenueCat for purchases (and many apps are), adding their Paywalls feature is incremental. Entitlements, purchases, and paywalls are all in one system. The downside is that their paywall offering is newer and less mature than their subscription management, which has been battle-tested for years.

Adapty tries to be a complete solution — paywalls, A/B testing, analytics, and some subscription management features. The integration isn't as streamlined as RevenueCat's, and the documentation can be inconsistent. But it covers more ground than Superwall without requiring a separate subscription management SDK.

A/B testing

All three platforms offer built-in A/B testing, but the implementations vary in depth.

Superwall has the most mature experimentation feature. You can test different paywall designs, copy variations, and even whether to show a paywall at all (holdout groups). Their analytics dashboard shows conversion rates, revenue per user, and statistical significance. The experiment setup is straightforward through the dashboard.

RevenueCat supports experiments as part of their Offerings system. You define multiple offerings and split traffic between them. It's tightly integrated with their subscription data, which means you get accurate revenue attribution. The setup is less intuitive than Superwall's — the concept of "offerings" vs. "paywalls" vs. "experiments" can be confusing, especially for teams new to RevenueCat's data model.

Adapty offers A/B testing with a visual interface for comparing variants. Their statistical engine is adequate, and they provide win probability estimates. The analytics are decent but not as deep as what you'd get from a dedicated experimentation platform. One nice feature: they support running experiments on specific audience segments.

The shared limitation across all three is that experiment configuration lives in the dashboard. You can't define an experiment as code, version control it, or create it through a CI/CD pipeline. If someone misconfigures an experiment in the dashboard, there's no audit trail or rollback mechanism beyond what the vendor provides.

Pricing

Pricing structures differ significantly and can be the deciding factor for many teams:

• Superwall charges based on "paywall impressions" — each time a paywall is displayed counts toward your quota. Free tier is generous for testing, but costs scale linearly with your user base. At scale, this can be expensive relative to the other options.
• RevenueCat offers Paywalls as part of their existing subscription management pricing. If you're already paying for RevenueCat, Paywalls is included at certain tiers. If you're not, you're paying for the full subscription infrastructure platform just to get paywalls. Their pricing is based on monthly tracked revenue (MTR), which aligns incentives but gets expensive as your revenue grows.
• Adapty has a usage-based model tied to monthly active users. The free tier is limited but workable for early-stage apps. Paid plans are generally cheaper than Superwall at high volumes but more expensive than RevenueCat if you're already on RevenueCat's platform.

The developer experience gap

Here's what none of these platforms do well: treat developers as first-class users.

All three require you to use a web dashboard as the primary interface for creating and managing paywalls. There's no way to define a paywall as a JSON file in your repo, no way to create an experiment through a CLI, no way to pipe paywall analytics into your existing data warehouse through a first-party API that covers the full feature set.

This matters because monetization is increasingly an engineering concern, not just a product management concern. When your growth team wants to run 10 experiments per month, and each experiment involves creating paywall variants, configuring audiences, setting up tracking, and analyzing results — doing all of that through a GUI becomes the bottleneck.

And with the rise of AI-assisted development workflows, the lack of an API-first interface means these tools can't be operated by AI agents. You can't tell Claude to "create a paywall experiment that tests three different pricing tiers for US users" if the only way to create that experiment is by clicking through a web dashboard.

Summary and recommendations

Choose Superwall if: design flexibility is your top priority, you're already using a separate subscription management solution, and you don't mind web view rendering. Best for teams with a designer who wants full control over the paywall look and feel.

Choose RevenueCat Paywalls if: you're already using RevenueCat for subscription management and want to add remote paywalls without adopting another vendor. The native rendering is a real advantage, and the integrated analytics simplify attribution.

Choose Adapty if: you want a middle ground between Superwall's flexibility and RevenueCat's integration, and you prefer native rendering. Good for teams that want paywall management and basic analytics in one tool without committing to RevenueCat's full platform.

Consider an API-first alternative if: your team works in code, you want version-controlled paywall configuration, you plan to run experiments at scale, or you want AI agents to be part of your monetization workflow. The dashboard-first model works for simple use cases, but it doesn't scale with engineering teams that treat infrastructure as code.