Shopify is generally the better choice for straightforward catalogs where speed to launch and low ongoing maintenance matter more than deep customization, while a custom build makes sense once your commerce logic marketplace payouts, complex B2B pricing, unusual subscription rules genuinely doesn’t fit a standard platform. Most businesses should start with the platform question rather than assuming custom is inherently better, since a custom build carries real ongoing engineering cost that a platform-based store doesn’t.

Sales tax and VAT obligations depend on where you have legal nexus, what you sell, and where your customers are located, and most eCommerce platforms handle the calculation through a dedicated tax service integration (like Avalara or TaxJar) rather than custom logic. Because tax obligations are genuinely complex and vary by jurisdiction, confirming your specific requirements with qualified tax counsel before launch is worth doing rather than relying on general guidance alone.

Headless commerce separates the customer-facing storefront from the backend commerce engine that manages catalog, orders, and inventory, allowing the frontend to be built independently and often power multiple channels (web, app) from the same backend data. A traditional platform bundles frontend and backend together, which is simpler to manage but constrains how much the storefront experience can be customized beyond the platform’s theme system.

Cost depends primarily on which platform approach you choose a Shopify or BigCommerce build with standard functionality costs meaningfully less than a fully custom storefront or multi-vendor marketplace, since platform-based builds configure existing commerce functionality rather than building it from scratch.

 Multi-vendor marketplaces typically take longer than a single-seller store because vendor onboarding, commission management, multi-party payouts, and split order fulfillment all need to be designed and built as core functionality rather than added later.

 Page speed matters because slow-loading product and checkout pages directly increase cart abandonment, with the effect strongest at exactly the moment a shopper has already decided to buy and hits friction. Google’s Core Web Vitals measuring load speed, interaction responsiveness, and visual stability track the specific moments in a shopping session where that friction most affects whether someone completes a purchase.

 Yes, migration between any of these approaches is possible, though the complexity depends on your catalog size and how much order and customer history needs to transfer accurately. Data migration is typically the most underestimated part of a replatform, so we scope it as a dedicated workstream with its own validation testing rather than treating it as a quick export-and-import step.

 Peak-traffic handling combines auto-scaling infrastructure that expands compute capacity as traffic rises, caching strategy that keeps product pages fast without serving stale inventory data, and load testing against realistic peak-traffic simulations before the event itself. The load testing step specifically is what catches bottlenecks before they become a live outage, and it’s the step most commonly skipped when a project is under launch-date pressure.

 A native mobile shopping app is a dedicated iOS or Android application installed from an app store, offering the deepest device integration and offline capability, while a progressive web app is a website that behaves app-like installable, capable of push notifications without a separate app-store build. A PWA generally makes sense for businesses wanting app-like mobile performance without native app development and maintenance cost, while a native app earns its cost mainly for businesses with a large base of frequent, repeat mobile shoppers.

 Yes, integration with existing business systems ERP, inventory management, CRM, and POS for omnichannel retailers is scoped as a defined part of the project rather than assumed to happen automatically, since these integrations vary significantly by which specific systems you’re already running. We recommend identifying these integration requirements during the discovery phase rather than after development has started, since retrofitting integration points into an already-built platform is more expensive than planning for them upfront.

 It depends on your order volume and catalog size collaborative-filtering recommendation engines need meaningful order history to work well, so a newer or smaller store often gets more practical value from content-based recommendations or simply well-organized merchandising than from a full AI recommendation system. This is worth evaluating honestly against your actual data volume before investing in it, since a recommendation engine built on insufficient data tends to underperform simpler, rules-based merchandising.

 Ongoing maintenance includes security patching, platform and dependency updates, performance monitoring, and incident response for issues that surface under real customer traffic, distinct from feature development work. Left unmaintained, an eCommerce platform accumulates both security risk from unpatched dependencies and performance drift as traffic and catalog size grow beyond what the original configuration was tuned for  see our maintenance and support services page for what a maintenance engagement typically covers.