Vibe coding has picked up steam at a remarkable pace. It’s not hard to understand the appeal. Using AI to code a piece of software using just natural language prompts, without ever having to learn a single line of code, is pretty incredible.
However, AI coding is still in its infancy as a technology. Not only are there significant risks to vibe coding, but the companies that build these tools are still figuring out how to monetize their platforms. According to TechCrunch, the latest generation of “reasoning” AI models has dramatically improved coding capabilities at the expense of much higher inference costs. A report by CB Insights finds that these rising costs are resulting in many platforms moving to less predictable, more costly usage-based models.
For anyone wanting to build an app with the help of AI, it’s critical to understand pricing models, how much usage it will take to get your desired result, and whether other app development techniques might be a better bet financially.
AI coding can be charged by plan or by usage, and most AI vibe coding tools today are using a hybrid approach. But that’s not where it ends. Hidden costs can push the final price of your app even higher.
This guide will help you understand the pricing models that the most common platforms are using as of Fall 2025.
What is vibe coding?
Read about AI developmentBase plan pricing
Almost every major coding AI offers monthly plans, typically charging a flat fee for a certain level of usage. Base pricing might include up to X number of AI prompts, or a pool of credits, or simply “unlimited” standard usage with fair use limits. Some platforms may have hidden limits, like gating what models you can use.
Usage-based pricing
On top of a subscription, most vibe coding platforms have some sort of usage-based pricing.
LLM-based tools typically charge based on the number of tokens processed. You pay based on the number of “tokens” processed. A token is just a chunk of text (roughly 3–4 characters in English, or about ¾ of a word). Every time you prompt the model or get a response, those characters are broken down into tokens. With token pricing, it’s hard to estimate cost upfront because you don’t know how many retries, errors, or regenerations you’ll need.
Code is token-dense. A single line of code might translate into more tokens than a line of natural language. This means debugging, regenerating code, or asking for large files can rack up usage quickly. If Lovable uses OpenAI’s GPT-4o, a 1,000-word code generation request might burn through several thousand tokens.
Instead of billing you directly per token, most platforms sell you credits. Each action, like generating code, debugging, or running a test, costs a certain number of credits. This makes billing more predictable, but the downside is that it can be opaque. With credits, you may not know how much “real work” each credit buys.
Lovable.dev, for example, has a free tier with 5 credits per day and a Pro plan that includes 100 credits monthly. That sounds reasonable until you realize how quickly those credits evaporate. One reviewer on Reddit reports that “100 credits per month... run out very quickly with few interactions, making it unfeasible for those who want to develop something more robust.”
Effort-based pricing
Newer, reasoning-based AI models have made vibe coding costs even more unpredictable in recent months, with some AI coding platforms now considering how much “thinking” the model does in their costs.
Traditional models like GPT-3.5 mainly scale cost by input and output length. Newer reasoning models, like OpenAI’s o1 series, Anthropic’s Claude 3.5 Sonnet, or models used by Bolt/Lovable, may run multiple “hidden” reasoning steps before returning code. Platforms sometimes expose this as “effort” or “intensity” settings. You can request a “fast” answer (cheaper, less reasoning) or a “deep” answer (slower, more credits/tokens).
For example, if you ask AI to “write me a login form in React” that is a simple, cheap request. However, if you’re asking “design a full user authentication flow with OAuth + 2FA” the model has to plan, cross-check, and generate longer code. Even if the output is similar length, the hidden reasoning makes it more expensive.
This is why some platforms are introducing tiered pricing based on effort. Light tasks cost fewer credits, while heavy reasoning costs more credits.
Reasoning-focused models are fundamentally more expensive to run. This is because:
- They do more internal computation, like chains of thought, self-checks, or tool use.
- They often use larger context windows, processing entire codebases at once.
- They can invoke multiple “sub-queries”, all of which are billed to you.
Even if the output tokens are small, the platform charges more because the compute load is higher. For example, an o1-mini completion might cost 3–4x more than GPT-3.5, even on the same request. o1-preview or Claude Opus can be 10x+. Platforms like Lovable/Bolt abstract this away into credits per request, but behind the scenes, they’re paying higher per-request costs.
In theory, smarter models can save money by producing usable code in fewer iterations. However, this depends heavily on the user’s skill. Platforms sometimes set flat monthly bundles, like Bolt’s credits or Lovable’s per-app fees, to smooth over these spikes, but enterprise customers especially still feel the shift in model costs.
Replit initially offered a flat subscription for its Ghostwriter AI, but it recently switched to a usage-based model. One Redditor complained that after the change, “every little change that would have previously cost $0.25 is now about $2”, and they managed to rack up $350 in a single day of AI-assisted coding. The culprit was Opus 4, a more powerful model that, while impressively accurate, is much more costly than the normal agent.
Debugging and iteration overhead
Even the best AI coder doesn’t get things perfect on the first try. You’ll often find yourself in a cycle of prompting, testing, and debugging. Each iteration consumes more credits or tokens.
On Lovable, users note that if the AI gets stuck in a refactoring loop or hits a persistent bug, you can blow through a lot of credits just trying different prompts or fixes. Those circles cost credits, but without them, you may not get working code. Replit Ghostwriter users experienced a similar headache after the pricing revamp. The system’s default agent started executing more steps automatically, but if it doesn’t follow your intent perfectly, you end up paying for those extra loops.
There’s also a broader productivity cost to “almost right” code. A 2025 Stack Overflow study found that 66% of developers list “AI solutions that are almost right, but not quite” as a top frustration, and 45% say debugging AI-generated code takes more time than expected.
Because AI often produces code that looks plausible, you can spend ages finding the subtle bugs or adjustments needed. This is a “productivity tax” paid in hours, not dollars, but it directly translates to engineering cost. If you have to schedule extra QA, more test cycles, or involve senior engineers to untangle AI-written code, that’s a real expense on the project.
Hosting, deployment, and other backend fees
Once you’ve finished an AI-developed prototype, you still have to run that application. Here’s where another set of costs kicks in: hosting, deployment, and any backend services the app uses. Many AI coding platforms blur the line between coding and hosting – for example, Replit lets you deploy apps straight from their environment, and Lovable can deploy a generated app with one click on their servers. It’s convenient, but not always free.
Hosting costs can escalate as your prototype turns into a production app with real users. On Replit, free users can host lightweight public apps, but for anything serious, you’ll need at least a Core plan ($20/mo), which includes $25 of hosting credits and always-on capability. After you use up the included credits, it’s pay-as-you-go for usage, essentially cloud billing for your app’s CPU, RAM, and network.
You can pay for a custom domain on Lovable hosting, or you can push it to your GitHub, where you can host it yourself. So you might deploy to Vercel, Netlify, AWS, or another host. All those come with costs once you exceed free quotas. A hidden gotcha is that an AI-generated app might not be very resource-optimized. It could be doing something in a non-optimal way that’s fine for one user, but expensive at scale.
Lovable and others can get expensive as they provide another paywall for each additional app. “I pay $50/m for Lovable and have a handful of apps,” said developer Evan Furniss. “Each app, though, has a Supabase backend. Supabase is $25/m then $10/m per additional app for their smallest resource package. This means that per app I want to ‘play with’ in Lovable, I either need to shut down old Supabase projects, shuffle projects around to other organizations, or bite the bullet and pay $10.”
As your prototype grows, you’ll need to plan for the infrastructure costs: custom domains, servers or serverless functions, databases, content delivery (CDN), and so on.
Third-party APIs and integrations
Third-party APIs and services used in your app will also add to your costs. Many AI-generated projects include integrations: maybe your app uses Google Maps API, Twilio for SMS, Stripe for payments, or OpenAI’s API for some extra AI feature. During prototyping, you may test them a few times, often on free trial keys. However, in production, each of those has its own pricing.
For example, an AI-coded chatbot might be calling an NLP API that bills per 1,000 messages. Or your AI-generated backend might be running on a managed database that charges by storage and throughput. When budgeting, it’s easy to focus on the AI platform’s cost and forget these “satellite” costs that orbit around your app.
Engineering time to finish AI-generated code
AI coding tools are often marketed as if they’ll replace a chunk of your developers’ workload. In reality, they often shift the work rather than remove it. You skip some grunt work of writing boilerplate, but then you add work in reviewing, cleaning, and completing the code.
To get production-ready, users with professional needs, like businesses and startups, will need to hire developers to finish their apps. Adding more advanced functionality, like user authentication or connecting APIs, can be difficult to implement with AI coding tools. While many of these platforms are adding no-code-style components to help with these additions, they haven’t managed to overcome all of the barriers to deployment. For business leaders, this means budgeting for engineering involvement after the AI generates the basic code.
It’s also not as simple as a quick review or deployment. AI-generated code can be more challenging and time-consuming to review than human-written code. Over half of developers surveyed by Stack Overflow said they might be faster writing from scratch than wrestling with AI output for complex issues. Every hour a developer spends understanding and rewriting AI code is an hour of human engineering time, which is a real cost for businesses. If you have a team of developers at an average loaded cost of say $100/hour, even 10 extra hours spent cleaning up a feature is $1,000 of hidden cost attributable to the AI’s imperfection.
Scaling costs moving from prototype to production
Many of these hidden costs may seem minor when you’re just fiddling with a prototype or MVP. During the prototype phase, you might stay within free credit limits, ignore security since it’s not public, and be the only user, so hosting is free-tier. The real test comes when you decide to turn that prototype into a production application. This is when the costs tend to escalate.
You’ll likely need to move to higher-grade Business or Enterprise plans if you have a serious app in production for your business or startup. Business apps need more assurance for reliability and support, more data, so more infrastructure needs, and more users or developers working on it, requiring additional seats and additional credits for prompting. Compliance needs may also require scaling to higher plans if you have sensitive data and have to ensure the safety and security of your software.
What is no-code? Learn more about how to develop apps without engineers
Read the guideHow can I get the benefit of AI while still controlling costs?
Businesses, entrepreneurs, and professional developers need to budget accurately for their projects. When vibe coding, constantly changing plans and unpredictable costs that can’t be easily calculated make this challenging.
When using a vibe coding tool, you’ll need to monitor usage closely so you won’t be blindsided by a huge bill, allocate time for your engineers to test and harden AI-generated code, and choose pricing plans or self-hosted options that make sense for your scale. You also need to keep an eye on long-term maintainability: prototypes are great, but plan how you’ll maintain or transition that code in the long run.
It’s also wise to know what other technologies you can use when vibe coding isn’t the right approach. No-code platforms can give you more control and predictability. Using a platform like Glide, you can even use AI development in a more stable way. With Glide Agent, you can use AI to design your app using natural language prompts, but your output is assembled from reliably designed pre-built components with hosting, security, and advanced features already in place.
No-code is a more mature technology with more stable pricing and more predictable costs. Vibe coding give you speed, iteration, and creative freedom. Having both technologies in your tech stack is a strategic move. Find the best uses for vibe coding, like landing pages and prototyping, and rely on Glide for critical business tools where you need a secure and well-supported platform.
