Lovable is growing fast enough to make most SaaS playbooks obsolete. But what’s more interesting than the pace of growth is how the company is thinking about pricing and packaging in an AI-native world, and which long-held SaaS instincts they’re deliberately leaving behind.

Even as they’ve crossed $200M in revenue just over a year after launch, and continue to grow incredibly quickly, Lovable doesn’t treat pricing as a fixed system to be optimized at a later time. Instead, it’s treated as a living part of the product, and one that evolves alongside usage patterns, underlying costs, and customer expectations.

The key thing to note is that there’s no polished model or playbook to copy here. What emerges instead is a set of hard-earned lessons about monetizing AI products in an environment where value, cost, and competition all move faster than traditional software norms allow.

AI pricing and packaging must mature earlier

One of Lovable’s defining beliefs is that AI pricing hasn’t been solved yet—not by them, and not by anyone else.

Even at their current scale, the company views itself as operating on a kind of product-market-fit treadmill, where costs change, models improve, and new competitors emerge quickly. Customer expectations shift in weeks instead of the old timeline of years. In this environment, static pricing becomes a liability.

There’s a significant implication in all of this. AI companies can’t afford to wait years to build pricing sophistication the way many SaaS companies historically did. Because they’re tightly coupled to usage, cost, and value delivery from day one, pricing and packaging have to mature much earlier.

Treating freemium as growth infrastructure, not margin leakage

One of the most counterintuitive aspects of Lovable’s approach is how aggressively they invest in free usage. Instead of being just a gated preview, their freemium plan is designed to deliver real value, and users can continue building for free on a daily basis.

Internally, this isn’t framed as margin leakage to be minimized. It’s treated explicitly as a growth and acquisition investment that competes for the same budget and ROI as paid marketing channels, and the logic for this is straightforward. If you’re not spending that money on product-led access, you’re likely spending it elsewhere, and often less efficiently. In Lovable’s case, free users become advocates, driving word-of-mouth and organic adoption in ways that traditional acquisition channels rarely match.

Packaging decisions can create or constrain growth loops

Lovable’s pricing strategy is about both feature access and distribution. By default, apps built on Lovable include visible branding, turning each published app into a growth surface. Removing that branding is a paid unlock, aligning monetization with intent: exploratory users help spread the product, while serious builders pay to control presentation.

It’s a subtle reminder that while packaging choices can gate value, they more importantly shape how value moves through the ecosystem. For AI tools that produce public or shareable outputs, pricing decisions can either reinforce or weaken organic growth loops.

Making credits work for customers (and the business)

Rather than treating credits as a rigid constraint, Lovable has focused on softening the edges of the model to better reflect how people actually build.

Monthly credit expirations frustrated users who didn’t work in steady, predictable cycles. By introducing rollovers, the team was able to improve retention without hurting upgrades, ultimately challenging the assumption that unused value automatically suppresses expansion. Still, credit rollovers are intentionally limited. Lovable allows users to roll unused credits forward one month, effectively doubling their available balance, but it stops there. Users aren’t able to accumulate credits, which protects the company from runaway liability while still accommodating uneven building patterns.

Similar thinking shows up in one-time top-ups and daily free credits. AI usage is often bursty—people build intensely for a few days, then step away. Giving users lightweight ways to keep making progress without constantly changing plans reduces friction while reinforcing habit formation.

These mechanics matter because many AI companies today are pushed toward credit-based or pass-through pricing by high and volatile model costs. Credits provide a workable abstraction, but they’re not necessarily permanent. As underlying AI costs fall, new pricing approaches may become viable, whether that’s outcome-based pricing, internal “currencies,” or a return to simpler per-user models.

Unlocking collaboration by removing per-seat pricing

Lovable made a deliberate decision to remove per-user pricing from self-serve plans, treating users as an input metric rather than something to monetize directly. The result was a significant increase in collaboration, as teams could invite others freely without worrying about seat counts.

This decision does create friction in enterprise conversations, where predictability and per-seat pricing remain the norm. Lovable is actively experimenting with enterprise models to reconcile those expectations.

The broader lesson is clear: pricing models optimized for product-led growth and pricing models optimized for enterprise procurement rarely align perfectly, and AI companies may need to support both simultaneously.

Making users whole, even when it hurts revenue

Perhaps the clearest signal of Lovable’s philosophy is how it handles pricing changes for existing customers.

When a change removes value from a plan, the company doesn’t protect legacy revenue at all costs. The team lowers prices, migrates customers, and accepts the short-term revenue hits in service of long-term trust.

In one case, removing per-seat pricing resulted in a multi-million-dollar revenue impact. The team treated it as an investment, and growth accelerated afterward.

By changing pricing more frequently and communicating openly, Lovable has also reduced backlash. Pricing evolution becomes part of the expected experience, not a once-a-decade shock.

Experimenting on pricing like a product system

Underlying all of this is discipline. Lovable applies product rigor to pricing decisions: real experiments, real checkout flows, defined sample sizes, and clear success metrics.

Pricing changes are evaluated not just on conversion, but on downstream retention, expansion, and engagement. Pricing is no standalone lever for the Lovable team. It’s assessed as a key part of the entire growth system.

The real takeaway: Build the muscle, not the perfect model

It’s clear that AI pricing will continue to change. Costs will come down as time goes on, and new pricing primitives will emerge. Some models will snap back to familiar patterns, but others will look entirely new.

The companies that win won’t be the ones that guessed the perfect model early. That’s almost beside the point. The winners will be the companies that built the internal capability to change pricing quickly, responsibly, and in a way that keeps users whole as the ground shifts.

As seen today, Lovable’s pricing page is interesting, but that’s not the lesson to take away from all this. The lesson is that in the AI era, monetization must be treated as product infrastructure: something you design early, revisit often, and evolve in lockstep with how customers actually use your product.