Empowering Development with AI: A Product Engineer’s Take on Cursor & Lovable
How a Product Engineer Harnesses Cursor & Lovable to Slash Development Time and Supercharge SaaS Innovation
Recently, I caught up with my friend who’s building a new SaaS platform.
Yes, a brand new product he just sent to the world, and you can take a look here:
He’s telling his tale to the world down here 👇🏻
He shared firsthand experiences using two emerging AI tools: Cursor and Lovable. He’s aim was to accelerate frontend/back‐end development.
Below, I’ve distilled his insights into four key areas: ease of use and learning curve, cost versus value, flexibility and integrations, and actions to consider when selecting an AI coding assistant.
⚡️ Ease of Use & Initial Impressions
Cursor’s Rapid Onboarding (Early 2025)
Ángel first experimented with Cursor’s free tier earlier this year, hooking it up to a React‐based project that involved stitching together APIs, writing tests, and building UI screens.
He described the experience as “impressively fast.” Whereas Copilot’s free version felt limited, Cursor’s prompt‐driven workflow allowed him to say, “Generate a component for X, connect API Y, wire Z → done.” In his words:
It was pam‐pam‐pam—give me this, give me that, connect here, connect there.
Tasks that would normally take hours were completed in minutes.
Lovable’s Framework Focus
In contrast, when he shifted to Lovable (initially using a free trial, then paying €20/month), he noticed a steeper learning curve tied to Lovable’s opinionated stack.
Lovable excels within a specific framework (including Supabase integration), but only if your project aligns with its conventions. For someone comfortable with VS Code and “conventional” React/Node toolchains, Lovable’s constraints felt limiting at first.
Key Takeaway
If you want lightning‐fast scaffolding with minimal setup, especially for diverse JavaScript/React projects, Cursor’s interface feels more intuitive out of the box. Lovable shines in projects that adopt its prescribed framework fully, but unlocking its full potential means sticking closely to its conventions.
💸 Cost vs. Value
Free‐Tier Tradeoffs
During Ángel’s initial trial, Cursor’s free tier quickly hit its token limit. That forced him back to vanilla VS Code (where he admits he “feels slower”). Meanwhile, Lovable’s free trial also limited usage, leading him to invest in its €20/month subscription.
Paid Tiers & ROI
Once he switched to Cursor’s $16/month license, his efficiency gains justified the cost. Cursor could hook into any GitHub project instantly, and the time saved translated directly into pushing SaaS features faster.
For Lovable, the €20/month fee was viable only if he fully embraced its stack; otherwise, he felt the value proposition dipped once he outgrew its framework.
Key Takeaway
Free tiers are terrific for testing, but token caps often become bottlenecks. For ongoing, production‐scale work, expect to invest in a paid license. Evaluate your project’s complexity and framework alignment:
If you need a generalist AI assistant, Cursor’s price point seems more accessible.
If you’re happy with opinionated stacks and tight Supabase integration, Lovable may still make sense.
🤸🏼 Flexibility & Integrations
Cursor’s Versatility
Cursor stood out because it connects to virtually any GitHub project. If the SaaS platform evolves (e.g., switching from React to Vue, or introducing new microservices), Cursor continues working seamlessly. He emphasized:
I can plug it into any repo and it just…works.
Lovable’s Supabase Edge
A standout Lovable feature is its deep Supabase integration. When Ángel asked Lovable to modify the database schema, it not only generated migration scripts but also updated table policies (e.g., row‐level security) automatically.
That tight coupling of AI‐driven schema changes and policy updates can shave off hours of manual database work, especially for teams leveraging Supabase for auth and real‐time features.
Framework Lock‐In Considerations
However, Lovable’s Supabase magic only applies if you’re fully committed to its “Lovable‐approved” stack. Once you step outside that framework (e.g., introducing alternative storage layers or custom API layers), the AI suggestions become less reliable.
Cursor, by contrast, never asks what “flavor” of framework you’re using—it adapts to your existing codebase.
Key Takeaway
If your product roadmap heavily relies on Supabase (e.g., real-time dashboards, instant migrations), Lovable’s built-in workflows may justify its narrower scope. But for a multi-tech, polyglot SaaS, Cursor’s “any repo” flexibility wins out.
🛠️ Actions & Recommendations
Prototype with Free Tiers First
Quickly spin up a small proof-of-concept (POC) using both Cursor and Lovable under their free plans. Identify how many tokens or AI suggestions you consume in a typical “feature build” (e.g., adding a new React page with API calls). This will roughly estimate your monthly costs.
Align Tool Choice to Your Tech Stack
If your SaaS is entrenched in Supabase and you’re comfortable letting an AI layer dictate database migrations and policies, Lovable’s focus could be a huge time-saver.
If you expect to experiment with different stacks, perhaps mixing React, Next.js, serverless functions, or non-JS languages, Cursor’s agnostic approach is more future-proof.
Budget for Paid Plans Early
Plan your product budget to include AI-tool subscriptions (e.g., ~$16/month for Cursor or €20/month for Lovable). Factor in how many developers will use the tool, since “per-seat” pricing can scale quickly.
Evaluate Long-Term Workflow Impact
Beyond initial development, consider support/maintenance workflows. Simple schema changes via Lovable are exciting, but what happens when a colleague adds a custom database trigger? Does Lovable correctly interpret it, or do you need a manual override? Cursor’s lower automation removes some friction but might require more manual follow-up.
Establish Guardrails & Code Reviews
Regardless of AI assistant, enforce code reviews. AI-generated code
canwill introduce subtle bugs, especially around security rules, rate limiting, or performance optimizations. Human oversight ensures you catch issues early.
✨ Takeaways
From my conversation with
, both Cursor and Lovable accelerated development, but in markedly different ways.Cursor impressed him with raw speed, framework-agnostic adaptability, and a cleaner transition from free to paid use.
Lovable, on the other hand, delivered powerful database integrations (particularly with Supabase) but at the expense of flexibility.
Ultimately, he decided to standardize on Cursor for the upcoming SaaS launch, valuing its ability to “just plug into any GitHub repo” over Lovable’s narrower but deeper feature set.
Key Takeaways for Optimistic Engineers:
AI coding assistants can be genuine force multipliers, ready to generate components, wire APIs, and even refactor schemas in real time.
Beware of token limits on free tiers. Early POC work may mask the true costs of heavy AI usage.
Choose a tool that aligns with your long-term tech strategy. Framework lock-in can save time in the short run but may hinder flexibility down the road.
Always couple AI suggestions with human code reviews to maintain code quality and security.
Thank you for your support and feedback. I really appreciate it!
You’re the best! 🖖🏼
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 | A guest post by
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It’s clear that AI isn’t just a fancy add-on but a real game-changer that enhances productivity by handling repetitive and difficult tasks. It allows developers to focus on higher-level design and problem-solving. These tools help reduce context switching and load, allowing engineers to focus on solving meaningful problems. Thanks for sharing such an insightful and inspiring perspective!