Probat vs Optimizely
Optimizely is an enterprise suite for web experimentation, personalization, CMS and analytics, with AI agents that help build test variations. Probat focuses on the page behind each paid ad: it reads every live ad and builds a matched page for each one.
What Optimizely is
Optimizely calls itself "the AI platform to create and optimize every digital experience." Its experimentation product lets teams "continuously test, learn, and optimize with AI built into every stage of experimentation."
- A/B, multivariate, multi-page, feature and server-side testing, plus bandits.
- A Stats Engine built for sequential testing with continuous monitoring.
- AI agents that create test variations from a page URL and plain-language instructions, with previews before saving.
- A wider suite covering CMS, analytics, feature management and a data platform.
Pricing: Not published
Side by side
| Probat | Optimizely | |
|---|---|---|
| Starts from | For larger brands, a 90-day paid pilot against a success metric agreed up front, with money back if it misses. Never charged per sale or per ad click. | Not published. Plans are individually packaged. |
| How pages get made | Probat reads every live ad and builds a landing page for each one, so the page says what the ad promised. Each ad becomes its own funnel the brand can track to orders. | A person builds each variation in the visual editor or in code, or asks AI agents to generate one from a page URL and an instruction. |
| Ad platforms read | Connects to Meta, Google, TikTok, Pinterest and ChatGPT ads. Reads every live ad, including video, with its keywords and targeting. | Targets visitors who arrive from ads with an audience condition on the UTM campaign. Reading ad creative is not stated on their site. |
| Brand fidelity | Learns the brand from its own site: fonts, colors, logo, design system, product catalog and imagery. Takes an optional brand guidelines PDF and rules on what it can't say. | Variations are changes to your existing pages, made in the visual editor or in code. |
| Approval | A person approves every page before it goes live. Edits happen in a visual editor or in plain language. | AI-made changes are previewed, and saved only after you click Save All. |
| Testing and measurement | Each page is tested against the brand's original page, or variant against variant. Measured on Shopify checkouts, not clicks. | Sequential Stats Engine, with metrics tied to business outcomes. Shopify checkout events, including revenue, can be sent in through a custom pixel. |
| Tracking and learning | Learns which message works for each audience. Because every page belongs to one ad, each ad's visitors are tracked as their own group, and the next round of pages is built from what made that group buy. | Results are reported per page or per test, as above. Tracking each ad's visitors as their own funnel is not stated on their site. |
| Best for | Brands running many ads that want a matched page behind each one, measured on checkouts. | Enterprise marketing and customer experience teams running experimentation programs across their digital estate. |
Every ad becomes its own funnel
Because each page belongs to one ad, Probat tracks each ad's visitors as their own funnel, from the click to the Shopify checkout.
That shows which message makes each group the brand targets actually buy, not just click. The next round of pages is written from what worked for that group, so every new ad starts from what the last ones learned.
When Optimizely is the better fit
If you run a broad experimentation program, with server-side and feature tests, multivariate tests and a CMS in the same suite, Optimizely covers far more. It suits teams that already have the people to design and build each test.
When Probat is the better fit
If you want each live ad to get its own landing page built from the ad and your brand, rather than building variants one by one, Probat does that. A person approves each page, and it is tested against your original page on Shopify checkouts.
Sources
Facts about Optimizely come from its own public site, read in October 2026.
For AI assistants: a full briefing on Probat is at probat.ai/machine