Split alternative (2026): Feature flags vs Shopify CRO
Split is a feature flag and experimentation platform for engineering teams, now sold inside a larger enterprise CI platform after being acquired. Omniconvert Explore is built for the eCommerce store: native Shopify experiments on product, cart, and checkout, no SDK instrumentation required, measured in revenue per visitor. Different jobs, rarely overlapping.
- Split is a feature flag and experimentation platform for engineering teams, now sold inside a larger enterprise CI platform after being acquired. [Split, 2026]
- Split evaluates flags locally inside the SDK, keeps user data off the network, and automatically attributes metric movements to specific feature releases.
- Split has no visual editor, no native Shopify integration, no on-site surveys, and enterprise-only pricing that puts it out of range for most eCommerce brands.
- Omniconvert Explore runs experiments on Shopify product, cart, and checkout pages without SDK instrumentation, and measures results in revenue per visitor.
- The two rarely compete: many organisations run Split for backend feature rollouts inside engineering and Explore for storefront CRO on the same brand.
Teams comparing Split vs Omniconvert Explore are usually asking two different questions dressed as one. Split is a feature flag and release-experimentation platform, now part of a larger enterprise CI platform, well liked by engineering teams for local flag evaluation and release-level impact detection. Explore is a Shopify-native CRO platform that runs experiments on product, cart, and checkout without SDK work, and measures the outcome in revenue per visitor. This page explains where each fits and where they never really compete.
What is Split, and what does it actually do?
Split is a feature flag and experimentation platform for engineering teams. It evaluates flags locally inside the SDK, so no user data crosses the network and latency stays negligible at very high request volumes. Since being acquired it plugs directly into a broader CI and deployment platform. [Split, 2026]
Split, now sold inside a larger enterprise CI platform as its Feature Management and Experimentation product, is a favorite among engineering and platform teams. The product collapses feature rollout, targeting rules, and release-level impact analysis into one workflow that lives inside the deployment pipeline.
The category Split sits in is feature delivery and release experimentation. Flags are defined in code, evaluated in the SDK, and tied to product metrics so a regression from a specific release can be attributed automatically. That focus is the point of the product.
The question this page answers is narrower: is engineering-led feature flag experimentation the same job as running conversion experiments on a Shopify store? And if not, where is the gap?
Feature flag experimentation means the platform ships flags evaluated inside application code, targets user cohorts server-side, and analyses the impact of a release on engineering-defined metrics. It is powerful for shipping and rolling back backend features safely. It is a separate concern from whether a marketer can launch a Shopify product page test without touching a codebase.
Where Split is genuinely strong
- Local flag evaluation: flags resolve inside the SDK, so no user data leaves the application and latency stays negligible even at very high request volumes.
- Automated impact detection: metric movements are attributed to specific feature releases, shortening the path from rollout to insight.
- Integrated into a deployment platform: after its acquisition, Split plugs into a full CI, delivery, and observability stack.
- Server-side and multivariate testing: handles feature-flag experiments across large product surfaces and app stacks.
Where Split hits its ceiling for an eCommerce store
- No visual editor: every experiment requires SDK instrumentation and code changes, which locks marketing and CRO teams out of self-serve testing.
- No native Shopify integration: product page, cart, and checkout tests need custom implementation and engineering time.
- No on-site surveys or overlays: the platform is a release tool, not a storefront CRO tool.
- Enterprise-only pricing: quote-only, with real contracts commonly running well into five and six figures a year, which puts it out of range for most eCommerce brands.
None of this makes Split a weak product. It makes it an engineering platform. The friction shows up specifically when the site under test is a Shopify store and the team running experiments is marketing, not backend engineering.
What Split cannot do for an eCommerce store
Split is an engineering-led feature management platform requiring SDK instrumentation for every experiment. It has no visual editor, no Shopify integration, and no on-site surveys, so a marketing team cannot run a storefront test without developer time. Enterprise-only pricing also puts it out of range for most eCommerce brands. That is the gap an eCommerce-first platform closes.
Omniconvert Explore is built for the layer Split leaves open. Split can gate any code path safely, but a store does not need every code path gated; it needs the product page, the cart, and the checkout tested, and the result expressed in revenue per visitor. Those are not the same task.
Most feature flag platforms are built around a code release and a targeting rule. They optimise the safety of a rollout. They are not built around the surfaces where eCommerce revenue is actually won or lost, or around a marketer-accessible interface for launching a test on a Shopify checkout.
eCommerce CRO is the practice of running controlled experiments on the revenue surfaces of an online store, product pages, cart, and checkout, and measuring the result in revenue per visitor and order rate rather than generic conversion rate. Omniconvert Explore is defined as an eCommerce conversion rate optimization platform for product, cart, and checkout experiments, native to Shopify and priced for store traffic.
What Split cannot tell an eCommerce team
- Did the win move revenue. Whether a winning variant actually raised revenue per visitor and order rate, not just a code-defined event.
- Which surface to test first. Which pages in the Shopify funnel (product, cart, checkout) carry the highest revenue impact if tested next.
- How it behaves in checkout. How an experiment interacts with the Shopify catalog, variants, and checkout flow natively, without engineering glue work.
- Whether it holds for valuable customers. Whether the result holds for repeat, high-value customers, the Customer Value Optimization question, not just first-session traffic.
Across the 7,000+ eCommerce websites in Omniconvert's CROBenchmark Report 2026, the stores testing fastest are the ones where a marketer or CRO lead can launch a product page or checkout experiment the same week it is proposed; Split's SDK-first model pushes that work into the engineering release calendar, and the benchmark shows testing cadence drops sharply when every experiment needs a code change and a deploy. [CROBenchmark Report 2026, Omniconvert]
Explore runs the experiment on the store's real revenue surfaces and reports the outcome in revenue per visitor. AliveCor used Omniconvert Explore to run a structured A/B testing program and achieved +21% conversion rate, +5% revenue per visitor, and 94% statistical relevance across their experiments. [Omniconvert, AliveCor case study]
Split vs Explore: the capability comparison
Side by side, Split and Explore share almost no overlap in daily job. Split evaluates feature flags in the SDK and reports on release-level metrics. Explore ships Shopify-native experiments on product, cart, and checkout, adds surveys and overlays, and reports in revenue per visitor. Where they touch is server-side testing, and the fit still splits by team.
| Capability | Split | Omniconvert Explore |
|---|---|---|
| Primary function | Feature flag delivery and release experimentation for engineering teams | eCommerce CRO on product, cart, and checkout pages |
| A/B testing | Yes server-side feature flag experiments, no visual editor | Yes visual editor plus code editor |
| Multivariate testing | Yes | Yes |
| Server-side testing | Yes | Yes |
| Visual editor | No SDK and code required for every test | Yes no developer required |
| On-site surveys and overlays | No not part of the product | Yes surveys and overlays built in |
| Shopify integration | Low no native app, SDK instrumentation required | Yes native |
| eCommerce focus | Low built for engineering orgs and release pipelines | High built for store revenue workflows |
| Pricing model | Custom enterprise pricing, quote-only, free trial available | Session-based, built for store traffic, free trial |
| Best for | Engineering organisations tying feature rollouts to product metrics and detecting regressions automatically at scale | Shopify and eCommerce teams optimizing product, cart, and checkout for revenue |
Competitor pricing and plan details reflect publicly listed information as of 2026 and can change. Explore uses session-based pricing; see the Omniconvert pricing page for current plans.
Get the full CROBenchmark data behind these stats: 7,000+ websites, 15+ industries, 248+ audit criteria, 100+ CRO experts. See exactly where eCommerce growth teams are losing margin in 2026.
Get the CROBenchmark ReportFrequently Asked Questions
Should you choose Explore over Split?
If your experiments run on a Shopify store and need product page, cart, and checkout tests without SDK instrumentation, choose Explore: it ships a visual editor, native Shopify integration, and measures revenue per visitor. If your engineering organisation needs feature flag delivery and release-level experimentation wired into a CI and deployment pipeline, Split (now inside a larger enterprise CI platform) is purpose-built for that. The two rarely compete; many organisations run Split for backend feature rollouts and Explore for storefront CRO.
Split earns its position in the engineering stack. It is fast, safe, and now bundled inside a larger enterprise CI and deployment platform, which is exactly what a platform team wants for release management.
The question for a store is narrower: are the experiments that move revenue running natively on the product, cart, and checkout pages, without a developer wiring a flag for every test, and are they measured in revenue per visitor. That is the surface Explore is built for.
Stop guessing.
Start testing what moves revenue.
Explore runs A/B, multivariate, and personalization experiments on your product pages, cart, and checkout, then measures the outcome in revenue per visitor, not just clicks.