{"slug": "vibe-coding-economically-which-framework-is-the-cheapest-rails-vs-django-vs", "title": "Vibe Coding Economically: Which Framework Is the Cheapest? (Rails vs Django vs Laravel)", "summary": "AppSignal researchers compared the token costs of using AI coding assistants with Rails, Django, and Laravel by building the same web app with identical prompts. Rails consumed 61,141 tokens and required zero iterations, while Django and Laravel used more tokens and needed multiple iterations. The experiment highlights that framework choice significantly impacts AI-assisted development costs.", "body_md": "AppSignal\n\n# Vibe Coding Economically: Which Framework Is the Cheapest? (Rails vs Django vs Laravel)\n\n[Tarun Singh](/authors/tarun-singh.html) on\n\nToken costs used to be something most developers ignored. They simply dismissed them as theoretical. Now, they’re showing up in your Cursor/Claude Code bill, in every pasted error, in that package the AI pulled in without asking, or in that clarification round you didn’t plan for.\n\nMost developers choose a framework based on what they've used before, what the job description asks for, or simply whatever was used on their last project. Very few developers give any consideration to the total cost of using an AI to develop the application, even though token spend is now becoming a real engineering expense for teams using tools like Cursor and Claude Code daily.\n\nThat got us wondering: which framework is actually the most economical for AI-assisted development? To find out, we gave [Rails](https://rubyonrails.org/), [Django](https://www.djangoproject.com/), and [Laravel](https://laravel.com/) the same task, used the same prompts, and tracked every token in an attempt to measure the real vibe coding cost of each.\n\nHere's what we discovered.\n\nWhat Did the Experiment Look Like?\n\nFor this experiment, the task was straightforward: build a simple web app where users could authenticate/register, create and manage posts, comment on posts, and expose the posts through a JSON API endpoint available at `/api/posts`\n\n.\n\nWe used [Cursor Pro](https://cursor.com/) and tested each framework using the same prompt:\n\nEvery framework iteration was executed without manual changes or intervention. Token usage was measured directly from Cursor’s dashboard, counting only input and output tokens. Cached reads were excluded because they heavily depend on project size and could distort the comparison.\n\nThe list of tracked items included:\n\n- Total tokens consumed\n- Number of prompt iterations to reach a working application\n- Number of dependencies/packages installed by the AI\n- Memory footprint associated with the application after installation\n\nRails\n\n**Tokens spent:** 61,141 tokens (input: 48,527, output: 12,614)\n\nThe principle of “convention over configuration” is particularly apparent when AI writes code for you. Rails has a clear, predictable structure. Models are located in `app/models`\n\n, controllers are found in `app/controllers`\n\n, routes are defined in one file, and migrations use standard name conventions.\n\nThere’s little architectural uncertainty and even less need for the AI to backtrack or ask follow-up questions. As a result, the application was built on the first try, with zero iterations.\n\nNow let’s take a look at the integrations and tooling that Cursor picked to use in our app. It added `bcrypt`\n\nto the Gemfile for encrypting passwords, `solid_queue`\n\nfor background processing, `bundler-audit`\n\nfor scanning gem security, and `brakeman`\n\nfor scanning static vulnerabilities. None of these were explicitly mentioned in the prompt. It’s just that Rails provides such extensive documentation for the complete ecosystem that the AI tends to automatically reach for default Rails solutions whenever it encounters an implicitly defined issue.\n\nThe request was for authentication, but what we got goes a bit further. We ended up with authentication plus the kind of security tools an experienced Rails developer would usually add on day two.\n\nAnd it works.\n\nRuby on Rails apps generally load these gems at startup, which is part of why the app takes roughly 80 MB of installed memory, about four times larger than the other two alternatives we’ll look at. But, that memory tax is the trade-off that comes with “convention over configuration”.\n\nOh, and one more thing worth flagging: if you’re a Rails developer or you already know the foundations, it’s a relatively easy language to debug. For instance, if the solid_queue breaks, you know exactly what it is and why it’s there. However, for someone who isn’t aware of this, every AI-generated code can quickly turn into archaeology (that is, digging through layers just to understand why something exists).\n\nDjango (Python)\n\n**Tokens spent:** 31,301 tokens (input: 20,808, output: 10,493)\n\nDjango was the best choice based on all criteria, and by a wide margin. It used less than half the tokens that Rails used during development, produced a working application faster, and had the smallest memory footprint (compared to Laravel), but the biggest surprise was the dependency count.\n\nThe only dependencies included in the pip freeze after the development were those that are built into Django: `asgiref`\n\n, `sqlparse`\n\n, and `typing_extensions`\n\n. None of them were written by our AI agent, as all three are Django internals.\n\nAI models have been trained on hundreds of thousands of lines of Python code, which is more than most other languages. You can see it in the quality of their output and how rarely they question themselves.\n\nIn addition to fewer clarification rounds (and, therefore, tokens), Django's architecture also reinforces efficiency. Models, views, URLs, and templates all live in predictable locations. This structure gives the AI clear guardrails without forcing it to create a new architecture.\n\nThe API endpoint exposed one consistent pattern across all three frameworks. The generated code worked fine in development:\n\nHowever, in production, this introduces an [N+1 problem](https://blog.appsignal.com/2025/07/09/how-to-avoid-nplus1-queries-in-django-python.html). This isn’t a big deal when you have 10 posts or so, but it does become a production incident when you hit 10K or more posts.\n\nLaravel (PHP)\n\n**Tokens spent:** 104,989 tokens (input: ~89,138, output: ~15,851 - combining multiple sessions)\n\nThe overall cost of building an application using Laravel compared to Rails or Django was significantly higher. That headline number needs a bit of context, though, rather than being left as a simple data point.\n\nThe initial build of the application was completed successfully from a structural point of view, but it still needed some debugging before the app could function properly. That debugging session alone accounted for approximately ~44,000 tokens, which makes up most of the difference in token usage between Laravel, Rails, and Django.\n\nThe structure of the error wasn't complex. It was a typical issue AI occasionally runs into with Laravel's layered patterns, especially when attempting to coordinate Eloquent, a service provider, and route model binding at the same time.\n\nThe way dependencies were handled was interesting. The Laravel package `laravel/breeze`\n\nwas added for authentication scaffolding, which was a reasonable choice but also an opinionated one that the AI made on its own. Another package, `laravel/pao`\n\n, was added as well, which may be an existing package with a hallucinated or incorrect name. It's a good reminder that proper audits should be done before anything reaches production.\n\nThe total use of RAM on both frameworks at run time was **~18MB**. Although the number of tokens used by Laravel during the build was **3.4x** more than the other two, the application isn't heavier at runtime.\n\nIt’s also important to note that PHP hosting is the cheapest of the three stacks by a significant margin. If you're already factoring in infrastructure costs alongside token costs, that matters, since spending more during the build phase can sometimes mean cheaper ongoing hosting down the line.\n\nSide-by-Side Results\n\nHere’s the complete token cost comparison across all three frameworks with real numbers:\n\nMetric | Rails | Django | Laravel |\n|---|---|---|---|\nTotal Tokens | 61,141 | 31,301 | 104,989 |\nPrompt Iterations | 0 | 0 | 1 |\nDependencies Installed | 3-4 | 0 | 2 |\nMemory Footprint | ~80 MB | ~20 MB | ~18 MB |\nTime to a Working App | 9 minutes | 3 minutes | 7 minutes |\n\n**Django** comes out as a clear winner on every metric related to costs: fewer tokens, zero prompt iterations, no dependency sprawl, and lighter memory, similar to Laravel.\n\nWhat really stood out was the memory difference, as most developers assume Rails and Django are roughly comparable in this regard. However, in practice, Rails consumes around 80 MB of memory (4 times more than Django, which consumes approximately 20 MB) to provide the same feature set.\n\nLaravel's token cost is the other counterintuitive finding. PHP is generally known as a pragmatic, low-overhead choice, and it lived up to that reputation when it came to memory and hosting. However, that was not the case in terms of cost.\n\nThe Hidden Cost: What Happens After “It Works”\n\nTokens tell just one side of the story. They represent only the build cost, not the total cost (in some cases, they are not even the largest expense). It’s equally important to consider what it takes to maintain these applications once they’ve been shipped.\n\n**Debugging tax:** All three apps built by Cursor produced[N+1 queries](https://www.appsignal.com/learning-center/what-is-an-n-plus-1-query)in their API endpoint. When it comes to Django, fixing it only requires one line if you know`select_related`\n\n. If you don't, you're back in Cursor, spending tokens to diagnose something the AI has quietly introduced.**Production issues**: The AI produces code that works correctly when there are 10 rows of data, but fails when there are 10K of them. Some examples include missing indexes, improperly configured caching, and unprompted packages/libraries with their own failure modes. None of these will be apparent until you deploy your application.**Non-developer problem**: If you have no framework background and let the AI decide, it will likely reach for Python, where it’s most fluent. But fluency doesn’t help you when the ORM starts misfiring and you don’t know what an ORM is to begin with (it’s an Object-Relational Mapper, in case the acronym wasn’t clear).**Multi-agent costs**: The numbers discussed in this blog are single-agent sessions. Parallel front-end and back-end agents scale token costs non-linearly. One team reportedly burned €1,000 in just six hours.\n\nThe Real Answer\n\nBased on the raw numbers alone, **Django** was the cheapest framework for AI coding in our experiment. It used half the tokens that Rails used and a third of Laravel's. It also boasted the fastest build speed, with no unexpected dependencies produced.\n\nBut when all is said and done, the least expensive framework is actually the one where it's easy to fix things when they break. If you're using Active Record in a Rails app, you’ll likely recover from mistakes the AI made faster than you would if you were to debug unknown Eloquent behavior.\n\nAnd regardless of how many tokens each framework used to scaffold the app, make sure you’ve set up monitoring before you ship, as AI code does not provide you with a mental model of where things are likely to fail.\n\nThe good news is [AppSignal](https://appsignal.com/users/sign_up) has the monitoring you need for [Rails](https://www.appsignal.com/ruby), [Django (Python)](https://www.appsignal.com/python), and [Laravel (PHP)](https://www.appsignal.com/opentelemetry/php-monitoring) applications.\n\n## Wondering what you can do next?\n\n[Try out AppSignal with a 30-day free trial](https://appsignal.com/users/sign_up).[Reach out to our support team with any feedback or questions](mailto:support@appsignal.com).\n\n- Share this article on social media\n\n## Tarun Singh\n\nTarun Singh is a software engineer and technical writer with 5+ years of experience creating developer-focused content on backend systems, APIs, and modern web development. He has published 800+ technical articles across major platforms and frequently writes deep-dive tutorials on developer tools, testing, AI, agentic tools, cloud, and infrastructure. Tarun is passionate about open source, developer education, and building reliable software systems.\n\n[All articles by Tarun Singh](/authors/tarun-singh.html)\n\nBecome our next author!\n\n[Find out more](/write-for-us.html)\n\n## AppSignal monitors your apps\n\nAppSignal provides insights for Ruby, Rails, Elixir, Phoenix, Node.js, Express and many other frameworks and libraries. We are located in beautiful Amsterdam. We love [stroopwafels](https://www.appsignal.com/waffles). If you do too, [let us know](mailto:contact@appsignal.com). We might send you some!\n\n[Discover AppSignal](https://www.appsignal.com)", "url": "https://wpnews.pro/news/vibe-coding-economically-which-framework-is-the-cheapest-rails-vs-django-vs", "canonical_source": "https://blog.appsignal.com/2026/06/17/vibe-coding-economically-which-framework-is-the-cheapest-rails-vs-django-vs-laravel.html", "published_at": "2026-06-17 00:00:00+00:00", "updated_at": "2026-07-08 17:17:55.856157+00:00", "lang": "en", "topics": ["developer-tools", "artificial-intelligence"], "entities": ["AppSignal", "Rails", "Django", "Laravel", "Cursor"], "alternates": {"html": "https://wpnews.pro/news/vibe-coding-economically-which-framework-is-the-cheapest-rails-vs-django-vs", "markdown": "https://wpnews.pro/news/vibe-coding-economically-which-framework-is-the-cheapest-rails-vs-django-vs.md", "text": "https://wpnews.pro/news/vibe-coding-economically-which-framework-is-the-cheapest-rails-vs-django-vs.txt", "jsonld": "https://wpnews.pro/news/vibe-coding-economically-which-framework-is-the-cheapest-rails-vs-django-vs.jsonld"}}