Pricing Page Teardown: Lovable

In these teardowns, we use the valueIQ Pricing Intelligence agent to look at the pricing pages of well-known companies to see what we can learn.

We have chosen Lovable as our first example. We do this out of respect. The Pricing Intelligence agent was built in part with Lovable (we also use Vellum as AI middleware), and there are a lot of things to learn about credit-based pricing best practices from Lovable.

You can get the Advanced Report on Lovable's pricing page by going to the Pricing Intelligence Agent, signing up, and searching for Lovable. The free plan gives you 200 credits per month, so you can download the Advanced Report for Lovable (Advanced Reports cost 120 Credits, Standard Reports cost 30 Credits).

Lovable uses credit-based pricing. It has a good description of its credits on its FAQ (this is a best practice for anyone with a credit-based pricing model).

Results of the analysis

So what did the pricing page analysis find? Here are a few of the key sections from the Advanced report.

Part 1: Snapshot of Current State of Pricing

Lovable now has a modern, public pricing model that is economically sound but harder to understand than key competitors. The urgency is to keep the architecture and make it radically simpler to choose, forecast, and control.

Current model (workspace-first, credits + usage)

• Tiers (Observed Data): Free, Pro, Business, Enterprise.
• Billing unit (Observed Data): Per workspace per month, with unlimited collaborators; segments primarily use one workspace per team.
• Primary metric (Observed Data): Monthly workspace credits consumed by AI-assisted building in the editor.
• Runtime monetization (Observed Data): Separate Lovable Cloud and Lovable AI dollar balances per workspace for hosting and runtime AI usage, seeded with free monthly allowances and billed pay-as-you-go when exceeded.
• Entry paid anchors (Observed Data, all per workspace per month):
  • Pro 100 – $25 for 100 monthly credits (plus daily credits).
  • Business 100 – $50 for 100 monthly credits (plus daily credits, governance features).
  • Enterprise – custom, no public ranges.

How this maps to segments

• Indie makers & solo developers: Free + Pro 100; highly sensitive to simplicity and bill predictability.
• Small teams & startup squads: Pro tiers; value shared credits and unlimited collaborators.
• SMB / mid-market departments: Business tiers; pay primarily for SSO, roles, data controls, and support.
• Enterprise & regulated organizations: Business/Enterprise; runtime usage and governance dominate economics.

Competitive context

Direct AI app builders cluster at $20–30/month entry for "serious" usage; Lovable's Pro 100 at $25/month is right in that band. Base44, Bolt.new, and Softr all use some form of credits/tokens + hosting; Cursor anchors per-seat WTP at $20+/user/month.

Key strengths today

• Strong value alignment: credits scale with AI building; Cloud/AI balances scale with app usage.
• Unlimited collaborators per workspace feels very fair for teams.
• Natural expansion: more apps and more traffic → higher credits and Cloud/AI spend.

Key weaknesses today

• Buyers must reason across three constructs (tier, credits, Cloud/AI) and often cannot answer "What will this cost me per month?" without deep reading.
• No concrete public guidance on Enterprise price bands (custom only).
• No scenario-based examples of "X credits ≈ Y apps / Y workload," raising bill-shock risk for indie and small teams.

Pricing SWOT

Most people are familiar with a SWOT analysis (Strengths, Weaknesses, Opportunities, Threats). The Pricing Intelligence agent generates a Pricing SWOT that narrows the focus to pricing issues.

Strengths + Opportunities

• Leverage workspace‑level subscriptions to win in converging credits/token models
• Use strong measurability to justify higher enterprise ARPU
• Exploit governance‑rich enterprise tiers to attract cross‑functional teams

Weaknesses + Threats

• Mitigate dual‑meter complexity before competitors simplify narratives
• Add scenario‑based usage guidance to protect margins in volatile AI‑infra markets
• Increase transparency in enterprise price bands to avoid margin‑eroding discounting

Mansard COMPASS Analysis

COMPASS (Choice of Optimal Metrics for Pricing Agentic Systems & Solutions) is a framework developed by Michael Mansard from Zuora and Steven Forth from valueIQ.ai to help companies navigate AI pricing.

Lovable scores reasonably well on the COMPASS framework. The greatest weakness is with Attribution. This is common with most SaaS and AI applications, and it is something we are working on an agent to help solve.

Predictability and Acceptability (to buyer) are also on the weak side. This is a common challenge with credit-based pricing models.

Other vibe coding companies to explore

To get more perspective, you may also want to run the pricing pages of other vibe coding apps and compare them:

• Base44 Pricing
• Cursor Pricing
• Bolt Pricing
• v0 Pricing
• Supernova Pricing
• Glide Pricing
• Rosebud AI Pricing

Go to the valueIQ Pricing Intelligence to get deep insights into the pricing of the different vibe coding apps.