Amplitude Experiment vs PostHog vs Explore (2026): Analytics vs Revenue
Amplitude Experiment is an experimentation layer bolted onto Amplitude Analytics, which it requires as a prerequisite. PostHog is an open-source product analytics platform with SDK-based A/B testing and feature flags. Both are developer-first with no visual editor. Omniconvert Explore is the Shopify-native eCommerce CRO platform: product page, cart, and checkout experiments, measured in revenue per visitor.
- Amplitude Experiment is an experimentation layer bolted onto Amplitude Analytics, which it requires as a prerequisite, with a 4.5 out of 5 G2 rating on the parent platform. [G2, 2026]
- PostHog is the leading open-source product analytics platform with SDK-based A/B testing and feature flags, with a 4.4 out of 5 G2 rating. [G2, 2026]
- Both are developer-first: neither has a visual editor, neither has native Shopify integration, and every variant is a code change.
- Neither measures results in revenue per visitor on the product-to-checkout path, the surfaces where store revenue is actually decided.
- Omniconvert Explore runs product page, cart, and checkout experiments natively on Shopify and measures results in revenue per visitor: pick it when the CRO lead owns the number and the code path is not a dependency.
Teams comparing Amplitude Experiment vs PostHog are usually choosing between two developer-first analytics stacks that also do experimentation. Amplitude Experiment leads with an integrated analytics environment: experiments run beside product data, targeted through Amplitude cohorts, priced for teams already on the Amplitude stack. PostHog leads with open-source analytics that combines event tracking, session recordings, feature flags, and A/B testing in one self-hostable platform. Both fit engineering-owned programmes well, but neither is built for a Shopify marketing team to ship product page or checkout tests without developer work. This page covers what each does well, the gap they share for eCommerce, and when Omniconvert Explore is the right layer.
What is Amplitude Experiment, and what is it actually good at?
Amplitude Experiment is the experimentation layer on top of Amplitude Analytics. It reuses the events, cohorts, and metric definitions the analytics team already owns, and evaluates experiments through SDKs. It is built for product teams already invested in Amplitude who want testing connected directly to product data. [Amplitude, 2026]
Amplitude Experiment is an analysis layer first and an assignment layer second. Its parent platform holds a 4.5 out of 5 rating on G2 across 2,200 reviews. [G2, 2026] The strength is data continuity: an experiment reads the same events and cohorts that appear in the analytics dashboards, which removes the "why does the testing tool disagree with the funnel report" argument.
It supports advanced cohort targeting, server-side and client-side assignment, and sequential testing. Assignment happens through SDKs, not a visual editor, and the pricing model tracks Amplitude Analytics seat-based tiers.
Analytics-native experimentation runs experiments inside the same platform that already tracks product events and defines the team's metrics. Amplitude Experiment does this well for organisations already running Amplitude Analytics at scale. It is an add-on analysis layer, distinct from running a controlled revenue experiment on Shopify product, cart, and checkout pages through a marketer-accessible interface.
Where Amplitude Experiment is genuinely strong
- Analytics-native results: experiments read from the same events and cohorts as Amplitude Analytics dashboards.
- Advanced cohort targeting: reuse behavioural segments the product team already defines in Amplitude.
- Server-side and client-side assignment: flexible SDKs across web, mobile, and backend runtimes.
- Single vendor investment: teams already on Amplitude add testing without a separate tool purchase or data integration.
Where Amplitude Experiment hits its ceiling for an eCommerce store
- Amplitude Analytics required: the value proposition assumes the analytics stack is already deployed and paid for.
- No visual editor: variants are shipped through code and SDKs, not a WYSIWYG a marketer can use.
- No native Shopify integration: nothing wired to product pages, cart, or checkout out of the box.
- No multivariate testing: the platform runs A/B only, so factorial designs are not native.
- Product analytics framing: event counts and funnels, not revenue per visitor or order rate as the primary outcome.
What is PostHog, and what is it actually good at?
PostHog is the leading open-source product analytics platform. It combines event tracking, session recordings, feature flags, and A/B testing in one self-hostable tool. The free tier is generous, and it is popular with engineering and product teams that want full data ownership and no vendor lock-in. [PostHog, 2026]
PostHog is a developer-first analytics and experimentation platform. It holds a 4.4 out of 5 rating on G2 across 762 reviews. [G2, 2026] Its strength is breadth in one place: product analytics, session replay, feature flags, and A/B testing sit inside the same tool, and the whole stack can run on a team's own infrastructure.
Experiments run through SDKs and feature flags, evaluated in code. Pricing is usage-based with a generous free tier, which is a real advantage for early-stage product teams instrumenting a new application.
Open-source product analytics is a self-hostable event tracking and experimentation stack a team can run on its own infrastructure, with full data ownership and no vendor lock-in. PostHog does this well for engineering and product teams shipping a software product. It is a developer-owned analytics and feature-flag layer, distinct from running a controlled revenue experiment on Shopify product, cart, and checkout pages through a marketer-accessible interface.
Where PostHog is genuinely strong
- Open source and self-hostable: full data ownership, no vendor lock-in, deploy in your own cloud.
- All-in-one: product analytics, session recordings, feature flags, and A/B testing in a single platform.
- Generous free tier: usage-based pricing that scales from zero, popular with early-stage teams.
- Developer-friendly: strong SDKs across web, mobile, and server runtimes, with feature flags as a first-class primitive.
Where PostHog hits its ceiling for an eCommerce store
- No visual editor: every variant is configured in code, not through a WYSIWYG a marketer can use.
- No native Shopify integration: nothing wired to product pages, cart, or checkout out of the box.
- Developer resources required: a CRO lead cannot ship a checkout test independently.
- No multivariate testing: A/B experiments through feature flags, not factorial designs.
- Product analytics framing: event counts and funnel steps, not revenue per visitor or order rate as the primary outcome.
What Amplitude Experiment and PostHog cannot do for an eCommerce store
Amplitude Experiment and PostHog approach the problem from opposite ends, enterprise SaaS analytics and open-source developer analytics, but they share the same shape for a Shopify store. Both are developer-owned, both require code to ship a variant, and neither is built around the surfaces where eCommerce revenue is won or lost: product pages, cart, and checkout.
Amplitude Experiment is an add-on layer for existing Amplitude Analytics customers. It has no visual editor and requires developer implementation, and it is not designed for eCommerce checkout flow experiments. A marketer cannot ship a product page test through the Amplitude stack without engineering support, and the value proposition itself assumes the store is already paying for Amplitude Analytics, which most $1M to $50M ARR stores do not run.
PostHog is a developer-first product analytics and experimentation tool. It has no visual editor and every experiment requires code. It cannot run tests on Shopify checkout flows through a marketer-friendly interface, and outcomes are read as event counts and funnel steps, not as revenue per visitor or checkout conversion rate. Marketing has no way to ship the test independently.
The gap the two share is the eCommerce one. Both are strong product analytics platforms with experimentation attached, but neither is an eCommerce CRO platform. Neither treats the product-to-checkout path as the primary surface, and neither reports the result in revenue per visitor. For the wider context on how testing programmes actually move revenue, see Has personalization replaced A/B testing?
The deeper issue is that ownership of the test sits far from the person who owns the revenue number. A CRO lead has a hypothesis about the cart page and, in either tool, needs an engineer to write assignment code, define an event, and later stitch results back to order rate. Omniconvert Explore collapses that loop: a visual editor for product page and checkout variants, native Shopify integration, on-site surveys and overlays in the same platform, and the outcome measured in revenue per visitor. The person who owns the store's growth number owns the test.
eCommerce conversion rate optimization (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. 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 neither tool can tell an eCommerce team
- Did the win move revenue and margin. Whether a variant raised revenue per visitor and order rate, and held its margin once discounts and returns are counted, not just moved an event count in the analytics stack.
- Which surface to test first. Which pages in the store 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 SDK glue work or a feature-flag ticket.
- Whether it holds for valuable customers. Whether the result holds for repeat, high-value customers, the Customer Value Optimization question, not just for first-session visitors.
Omniconvert benchmarks more than 7,000 eCommerce websites in its CROBenchmark Report 2026, across 248+ audit criteria. The findings show where stores actually lose orders: 99.6% fail to make guest checkout visible and prominent, and 85.1% never show the full order cost before the final step. [CROBenchmark Report 2026, Omniconvert]
Those are fixes a CRO lead can hypothesise, mock up, and want to test today. In Amplitude Experiment the same fix is an engineering ticket that assumes the Amplitude stack already tracks the surface. In PostHog it is a code change guarded by a feature flag, and the outcome returns as an event count rather than an order-rate lift. Explore runs the experiment on the real revenue surfaces and reports the outcome in revenue per visitor, without an SDK integration or a feature-flag ticket between the hypothesis and the result.
This is what the title means by analytics vs revenue. Rigorous product analytics and open-source instrumentation are both real capabilities, but they sit at the wrong end of the store for a marketer whose job is order rate. Explore optimizes for revenue per visitor directly, and because Customer Value Optimization ties each result back to repeat, high-value buyers, the lift it confirms is margin the store keeps rather than traffic it rents. Explore also reaches Shopify-specific levers most developer-first tools cannot touch, including price testing; see Explore 3.0: pricing testing on Shopify.
Amplitude Experiment vs PostHog vs Explore: the capability comparison
Side by side, the three tools sit at different points on the experiment lifecycle. Amplitude Experiment is the analysis layer for teams whose source of truth is Amplitude Analytics. PostHog is the developer-first analytics and feature-flag layer for product and engineering teams. Explore is the eCommerce CRO layer for the store team that owns product, cart, and checkout, and is judged on revenue per visitor. See A/B testing with Explore for how those experiments run natively on the Shopify funnel.
| Capability | Amplitude Experiment | PostHog | Omniconvert Explore |
|---|---|---|---|
| Primary function | Experimentation layer on Amplitude Analytics | Open-source product analytics and feature flags | eCommerce CRO on product, cart, and checkout |
| A/B testing | Yes SDK-based, requires Amplitude Analytics | Yes SDK and feature-flag based | Yes visual editor plus code |
| Multivariate testing | No | No | Yes |
| Server-side testing | Yes via SDKs | Yes via feature flags | Yes |
| Visual editor | No code and SDKs only | No code and SDKs only | Yes WYSIWYG for marketers |
| On-site surveys and overlays | No | Partial surveys yes, on-site personalization overlays no | Yes surveys and overlays built in |
| Shopify integration | Low no native connector | Low no native connector | Yes native |
| eCommerce focus | Low built for product analytics teams | Low built for product and engineering teams | High built for store revenue workflows |
| Pricing model | Seat-based, free tier available, requires Amplitude Analytics | Usage-based, free tier available | Session-based, built for store traffic, free trial |
| Best for | Product teams already on Amplitude who want experimentation beside analytics | Product and engineering teams wanting open-source, self-hostable analytics | Shopify and eCommerce teams optimizing for revenue |
AliveCor used 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]
Competitor ratings, pricing, and plan details reflect publicly listed figures as of 2026 and can change. Amplitude Experiment requires an Amplitude Analytics subscription; PostHog offers a self-hosted open-source deployment alongside its cloud plan. 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 Amplitude Experiment or PostHog?
Start with the surface where your revenue is decided. If your product team already lives inside Amplitude Analytics and wants experimentation there, Amplitude Experiment earns its place. If your engineering team wants open-source, self-hostable analytics with feature flags, PostHog earns its place. For a Shopify store, run your next product page and checkout tests in Explore, measured in revenue per visitor. The three are complementary, not competitive.
Amplitude Experiment and PostHog are both capable platforms within their categories. Amplitude is the enterprise standard for product analytics with an integrated experimentation layer. PostHog is the leading open-source alternative with feature flags and self-hosting.
The question for a store is narrower: can your team run a controlled experiment on the Shopify product, cart, and checkout, and read the result in revenue per visitor rather than an event count. 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.