MDZ.AI: Bundling $200/Month of AI Tool Subscriptions Into a $49 Plan — $165K/Month in Two Years

MDZ.AI, launched by Su Jimmy in January 2024, is a marketing tool for e-commerce sellers that bundles 15+ AI models into a single subscription. It can generate product photos, sales copy, video, and graphics for Shopify and Amazon listings. It costs $49/month (about ¥7,350). Run by a team of three, the founder plus two employees, the business currently generates $165,000/month (about ¥24.75M), or an annualized $1.98M.

(Dollar figures are converted at an approximate rate of $1 = ¥150. Actual receipts vary with payment fees and exchange rates.)

Tracking the speed of the ramp-up

A quick sanity check here: dividing $165,000/month by the $49 price point gives roughly 3,367 users. That’s the scale of paid accounts a team of three is supporting, and conversely, since no higher-tier or annual plan is disclosed, that user count is the only yardstick available for sanity-checking the revenue figure. Starter Story is a publication that runs founder self-reports, and no third-party verification is involved here, worth flagging up front.

The starting point was “my own bill”

Jimmy has a software development background and was also an e-commerce seller himself. Just to put together a single product page, he’d bounce between tabs and subscriptions, ChatGPT Plus for copy, Midjourney for product images, yet another tool for video. The total came to over $200/month (about ¥30,000). That bill itself is the origin of the idea.

What matters is that he didn’t start building immediately. He talked directly with 50+ Shopify and Amazon sellers in online communities and confirmed the same pain was widely shared. A step was placed before development to avoid misreading his own inconvenience as “the market.”

The two decisions that set the trajectory

There’s no dramatic reversal in this case. What set the trajectory were two decisions made around the start of development.

The first: he didn’t build his own AI models. Rather than going down the path of proprietary models, he committed entirely to bundling existing APIs and put his resources into experience design instead. This choice shifts the competitive axis away from “model performance” and toward “everything handled under one contract.” That’s because customers are comparing the product against the $200/month bill they’re currently paying, not against other AI SaaS products.

The second: he set the price at $49, not $99. Jimmy reflects that this decision sped up adoption and generated word of mouth. Replacing a $200/month bill with $49 is less a discount than a reduction to a quarter of the cost, a difference of order of magnitude. Dropping the unit price to a level that requires almost no internal approval or comparison shopping explains the rapid ramp right after launch.

Jimmy also notes: “Don’t wait for perfect. We now have 15+ models, but only 5 were integrated at launch.” What mattered wasn’t the number of models bundled, but the idea of bundling itself.

Where the 500 signups came from, and what followed

The beta landing page went out on Reddit’s r/ecommerce and r/shopify, drawing 500 signups in 24 hours. Nor was this luck. Those subreddits were where the pre-launch interviews had happened. The product was placed right at the watering hole where he’d already gone to ask about demand.

Subsequent acquisition is described as three-pronged. First, SEO and content: comparison posts like “MDZ.AI vs ChatGPT Plus + Midjourney” drive 40% of organic traffic. This aligns perfectly with the pricing strategy described above. The comparison isn’t against a competing SaaS, but against “the combined cost of multiple tools.” Second, community: answering questions for two hours a day in Facebook groups and Slack channels brought in 30% of early customers. A single before/after post is said to have generated 200 signups on its own. Third, a referral program that grants one free month per referral.

For the first 50 customers, Jimmy personally ran onboarding over Zoom, listening directly for feature priorities.

What missed, and the limits of this case

Failures are also disclosed. Three weeks were spent building a feature nobody had asked for, while customers actually needed a bulk export feature that could have been built in two days. That’s the biggest mistake Jimmy cites himself. Even someone who started correctly, with 50 interviews, can easily see priorities drift once things are underway.

The risks aren’t small either. A structure built on bundling existing APIs directly absorbs upstream price hikes, spec changes, and terms-of-service changes. If an integration partner decides to become the bundler itself, the product’s reason to exist thins out. The $49 price point also lowers resistance to cancellation, so the undisclosed churn rate could substantially affect the underlying reality. On top of that, acquisition that depends on two hours of the founder’s time a day in community channels is bound to the founder’s personal bandwidth, exactly the part that would break first if the team of three tried to scale.

Care is also needed with the granularity of the numbers. What’s disclosed is limited to monthly revenue, unit price, early signup and customer counts, and rough traffic-source ratios. Monthly progression, churn rate, customer acquisition cost, and profit are all undisclosed. What shape the line took between “founded January 2024, 100 paying customers two weeks later” and “roughly 3,367 paying-equivalent users now” (a straight line, or one with a plateau along the way) can’t be determined from this article.

What’s transferable, and what isn’t

What’s easy to transfer: choosing a problem from your own bill, confirming it with 50 people in the same industry before you start building, placing the product exactly where you asked about demand, and structuring price and content around “current combined cost” rather than “competing product” as the comparison point. None of this requires capital or an existing audience.

What’s hard to imitate: the dual identity of being both an e-commerce seller and a developer at once, and the timing of early 2024, “the moment when AI tools were proliferating and the value of bundling them was at its highest.” The value of consolidation drops as the market matures. Bringing the same idea in today would mean finding a different, currently-fragmented category to bundle.

Related reading

• Carrd — a solo SaaS that reached $2M/year through a low price point and high volume
• Plausible — a case that reached $1M ARR through comparison-driven content