Polar Analytics vs Cometly

If you are down to Polar Analytics and Cometly, the honest starting point is that these two were built for two different businesses. Both promise to tell you which of your ads actually made money, but they answer that for two different kinds of company. Polar Analytics is a business-intelligence layer for a Shopify or DTC ecommerce brand. Cometly is an ad-to-revenue attribution tool for B2B SaaS and subscription companies that need to tie paid spend to money in Stripe and deals in a CRM. The reader stuck between them is really deciding what they sell and how a sale closes.

Polar pulls Shopify, the ad platforms, Klaviyo and the rest into one place and lets someone comfortable with data build custom profit and marketing dashboards on first-party data, closer to a business-intelligence layer than a single profit screen. Cometly follows a click through a longer funnel, writes the full touch history onto the deal in HubSpot or Salesforce, and reports which campaign produced pipeline and closed-won revenue, with MRR and LTV attached. One centralizes a store's data so an operator can build the reports they want. The other connects ad spend to recurring revenue for a SaaS business. The prices point the same way: Polar opens at $750 a month on a GMV-linked scale, and Cometly quotes a usage-based fee it does not publish.

The one question that decides it: what you sell

Answer this before you compare a single feature: do you sell physical products to consumers through a Shopify or DTC store, or do you sell software or a subscription through a longer, sales-assisted funnel that closes in a CRM? If your problem is centralizing a store's orders, ad spend and email so a data person can build the reports your team needs, that is Polar's problem to solve. If your problem is knowing which ad led to a trial that became a paying customer weeks later, and getting that signal back to the ad platforms so they optimize toward revenue instead of raw form fills, that is what Cometly is for.

How you make money decides it. Polar assumes a Shopify storefront and reads that store's data, which is why a DTC brand with an analyst on staff gets so much out of it and a software founder gets so little. Cometly assumes a Stripe subscription or a CRM pipeline and a sale that takes weeks, which is why it writes pre- and post-form touch history onto each deal, the part a CRM's own tracking usually cannot see across a long journey. Put a B2B software closed-won question into Polar and it has no CRM pipeline to read. Put a Shopify store's blended-profit question into Cometly and it was never built to answer it. Knowing which of those two sentences describes you settles most of the choice.

Polar Analytics: a Shopify BI layer you build yourself

Polar is the attribution tool for the brand that has outgrown a fixed dashboard and wants to build its own. It pulls Shopify, Meta, Google, Klaviyo and the rest into one place and lets a data-literate operator assemble custom reports, with unlimited users and historical data on the Core plan. Its attribution runs off a first-party pixel fed server-side, and Polar is honest that the numbers are directional, which is the right way to read any tool in this category. For a growing Shopify store that wants reporting flexibility and a team that will use it, that goes further than a prebuilt profit screen. It rates 4.9 out of 5 across 117 Shopify App Store ratings, with setup and data centralization the most-praised parts, and we rank it in our ecommerce attribution ranking for exactly that buyer.

Its drawbacks are the bill and who has to drive it. Core scales with GMV, so the figure climbs with revenue and a smaller shop pays more than the data is worth. Buyers report it gets restrictive when you need deep or custom attribution, and a dedicated success manager only arrives at a $10,000 annual contract, with some reviewers below that flagging slow support and connector delays. Because the value is in the reports you build, a team without someone to build them buys a canvas nobody paints on. The full Polar Analytics review covers how it holds up in practice.

Cometly: ad spend tied to Stripe and CRM revenue

Cometly does a narrower and, for a subscription business, more useful thing: it ties ad spend to money that actually arrived. Its Comet Pixel and server-side events feed Stripe revenue and CRM deal stages into the attribution, so you see which ads brought in pipeline and closed-won revenue, with MRR and LTV attached, instead of trusting Meta to grade its own homework. It runs first, last, linear, U-shaped and data-driven models across a long journey, and it sends deal-stage-aware events back to Meta, Google, LinkedIn, TikTok, Microsoft, Reddit and Snapchat through a server-side conversion API on both plans, so bidding optimizes toward paying customers. Its G2 record is strong and consistent, 4.8 out of 5 across 36 reviews, with praise clustering on tracking accuracy and fast, human support, and a specialist wires your stack up during a done-for-you onboarding.

Where Cometly makes buying hard is the sales process. It publishes no price and offers no free trial: the Core and Enterprise plans are quoted on a sales call, usage-based on your monthly pageviews, and you commit, or at least pay for onboarding, before you see it run on your own traffic. Some buyers report the quote feels high. Make the live demo prove the tracking on your real numbers first, because that is the only up-front proof you get. Outside its solid but small G2 record the proof thins out, with Trustpilot at 3.6 and no clear Capterra listing. The full Cometly review walks through the setup and the sentiment in detail.

Two answers for two kinds of business

Each tool answers a different owner's question. Polar answers whether a Shopify store made money and lets an operator slice that any way they need, provided someone builds the reports. Cometly answers which ads produced recurring revenue for a SaaS team, and feeds that signal back to the ad platforms. Put a solo software founder into Polar and they pay $750 a month for a BI layer with no store for it to read and no analyst to build with. Put a scaling DTC brand's blended-profit question into Cometly and it cannot see it, because ad-to-revenue attribution built around a CRM pipeline was never meant to answer it.

So your operation breaks the tie. If you run a store, the SaaS-first attribution tool was never going to give you a store's profit picture, however you want to cut it. If you sell software or a subscription, the ecommerce BI layer is built around a storefront you do not have, and it does not tie a weeks-long sale to the deal in your CRM. Match the tool to how your revenue actually arrives and the rest of the comparison mostly answers itself.

Where the pricing diverges

The two do not price on the same axis, and that itself tells you who each is for. Polar's Core fee rises with your store's GMV, so the entry number climbs with your revenue, and hands-on support sits behind a five-figure annual contract. Cometly does not publish a price at all: its plans are usage-based on monthly pageviews and quoted on a call, so you cannot line its cost up against a competitor before you talk to sales. There is no crossover to calculate. If Polar's entry price sounds steep for your store's stage, it is built for a brand further along than you are. If you cannot get a straight number before a demo, that is the friction you accept with Cometly.

Size each bill to next year's business. Read Polar's Core price against the GMV bracket you expect to reach, since the figure scales with revenue. Ask Cometly for a written quote against your real monthly pageviews, because usage-based pricing climbs as your traffic grows.

A third path if you host the funnel yourself

There is a third answer worth naming, because neither of these tools builds or hosts the funnel it measures. Polar reads a Shopify store that lives elsewhere, and Cometly reads revenue out of a Stripe account and a CRM that sit elsewhere too. ElasticFunnels owns that middle instead. It is a funnel platform for teams running paid traffic that builds the pages, hosts the checkout, and keeps its ad-spend and conversion reporting on the same data layer as the pages, the CRM and the sale. Because a sale closes in a checkout it hosts, that conversion is a first-party event it records directly, so a click, an order, a rebill and a refund sit on one record with nothing to reconcile in a spreadsheet. It fires server-side postbacks to your networks on purchase, refund, chargeback and renewal straight from that checkout, and it split-tests page variants under one campaign URL so a test never resets the ad platform's learning. It starts with a 14-day free trial that takes no card, on plans from $97 a month, plus 0.5% of revenue processed through its hosted checkout.

One caveat keeps it on its own line. ElasticFunnels is newer than either tool here, and there is very little independent third-party review coverage to check its claims against, so until you run real volume through it you are largely taking the vendor's word for how its reporting and postbacks hold up. That is what you would expect of a platform this recent: Polar has a documented Shopify App Store record you can read before you buy, and Cometly a consistent G2 record. The gap is in outside proof, since the pages, checkout and reporting are usable from day one. Its reporting also covers only the funnels it hosts, so a DTC brand measuring paid media across a Shopify store it runs elsewhere still needs a BI layer like Polar, and a software team attributing a sales-led motion that closes in an outside CRM still needs a tool like Cometly. ElasticFunnels suits the seller who runs their own funnel and checkout end to end, next to either measurement tool.

Which one for you

You run a Shopify or DTC store doing real revenue and have a data owner: Polar Analytics. A first-party BI layer you build your own reports on is the right call for a store with sizeable GMV, as long as you can carry the entry price and have the person to turn the canvas into reports your team reads.

You sell B2B SaaS or a subscription and need ad spend tied to revenue: Cometly. Stripe and CRM revenue attribution, long-journey multi-touch models and server-side conversion feeds to seven ad platforms are the right fit, as long as you can commit without a public price or a free trial and you make the demo prove the tracking on your own numbers first.

You host your own funnel and checkout end to end: look at ElasticFunnels alongside them, with the newness and hosts-only-what-it-builds caveats above in mind, and keep Polar for a Shopify store's profit reporting or Cometly for a software sale that closes in your CRM.

Cometly is our default pick because more readers who reach this specific comparison can actually use it. It needs nobody to build reports, carries no GMV-linked floor or five-figure contract for real support, and it feeds the ad platforms server-side on every plan, which is the paid-traffic buyer's job. Polar's ground is different: for an ecommerce brand at scale with a data team that wants to build its own dashboards, Polar is plainly the better tool, which is why it holds a place in the ecommerce attribution ranking above. If you are actually weighing two measurement tools for an ecommerce store, our Triple Whale vs Polar Analytics comparison takes the next step, and if you are weighing two attribution tools for a software business, Northbeam vs Cometly is the one to read.