Vellum is coming to the AI Engineering World's Fair in SF. Come visit our booth and get a live demo!

Cursor AI is god tier

I’d Pay $2,000 Out of My Own Pocket to Keep Using Cursor - The tab + context is next level.

4 min
Written by
Reviewed by

I would gladly pay about $2,000 a month out of my own pocket to keep using Cursor. These tools are so affordable compared to the value they provide.

Many might argue that Cursor AI is overhyped, but I definitely think it deserves the hype.

Why? Because Cursor is god tier.

The delta between Cursor and Copilot is approximately as much as the delta between Copilot and using smoke signals to communicate our PRs to each other.

I’ve been using Cursor for two weeks now, and I’m not even thinking about switching back to Copilot anytime soon. Copilot seems ancient now, and it's surprising how quickly I've adapted to Cursor.

I have a few reasons why. Let’s go through them.

Tab is All You Need

The tab functionality is just next level. Whoever thought of pressing tab repeatedly to accept a series of diffs one after another is a genius. I find myself typing a couple of characters, then pressing tab 3-5 times, and I’m done with whatever file I was changing.

Cursor can rewrite and jump you to the next suggested edit in a completely different part of the code. It's conceivable to make an edit in one spot, then just hit tab eight times to watch it edit the code for you in 8 different spots of your file.

Our entire team is beginning to adopt Cursor. My colleague Vargas finds it remarkable that Cursor sometimes suggests ideas before he even thinks of them, and his usual reaction is, 'Yeah, that’s better than what I was thinking.' He’s also noticed that he’s not correcting Cursor as much as he had to with Copilot. While autocomplete impressed him initially, he found himself making corrections frequently over time, making that feature feel outdated in comparison now.

Copilot has been around for a while, and it deserves praise for the work the team has put into its development. They have consistently followed excellent AI development practices, as outlined in their documentation.

But although Copilot maintains context across files these days, it doesn’t propose changes across files quite as well as Cursor. So between the context and tabbing, Cursor just lets me and my team glide through changes.

Coding is Much More Pleasant

Folks who haven’t tried Cursor argue that it will ruin coding. If you give it a chance, I think you’d find the opposite.

Something that's probably highly underrated is how much more pleasant it makes writing code. It makes me want to write more code, my thinking is clearer, and I make less mistakes. It feels like driving a car with really, really nice handling.

And more importantly, we hit flow state more easily with it.

We all know that one of the biggest flow state killers is seeing something tedious and then deciding to do a quick scroll on Slack instead. Because Cursor does that tedium for us, we get distracted less.

And we code a lot—like more than 10 hours per day, a lot. So I wondered what the impact is for beginners. Imagine you’re just starting to code— is it as useful to you as someone who’s more experienced and can revert an AI change fast?

More People Can Learn to Code Faster

With AI in general, and tools like Copilot and Cursor, we’re entering a new world order where everyone can code. Whatever you need to do, you can easily help yourself with a quick AI prompt in your browser.

Our marketing lead at Vellum now creates her own AI workflows to generate any script she needs, whether that’s event handling, styling, or a Webhook setup—she’s able to move 10x faster without pinging the engineering team.

On a company level, one of our customers is experimenting with a new concept: everyone ships one experiment per day. That’s right. Everyone. This includes designers, ops managers, C-suite, and subject matter experts. Literally everyone.

Sounds like chaos, right? Engineers must hate the codebase pollution going on? Not necessarily.

It turns out that this has unlocked a few things for them:

  1. Engineers focus on more meaningful work, while designers work on the pixel-perfect UI, and marketing adds social proof by themselves.
  2. The CEO button can actually be a CEO button now. No more ad-hoc requests from the CEO that you don’t agree with—they can just do it themselves and prove their case. Not bad for morale. Either they’re wrong about their idea and didn’t waste engineers’ time, or they proved they were right with less friction.
  3. Tighter feedback loops. Those requesting other misc. changes and going back and forth with engineers can often make them themselves, or at least start them.

But code is also getting more complex faster.

LLMs aren’t perfectly aware of linting rules, and not all are auto-fixable… yet.

It’s not that bad, but for people “writing code” who don’t quite know how to code, it can be a reasonable blocker enough to warrant certain linting standards. I’d expect this to change in the not-so-distant future, though.

Anyway, we’re pretty excited to see how that experiment turns out for them.

On the contrary...

Using Cursor can sometimes lead to less modular code — because we could either think of a good abstraction, or write the first example and hit tab, tab, tab.

That convenience might mean missing out on some thoughtful design.

Final Thoughts

I definitely believe Cursor deserves the hype, and it’s only going to get better.

For the skeptics, I suggest trying Cursor (or Copilot) for just a few days. Then, challenge yourself (spoiler alert: it’ll be hard) to code without it, and you’ll notice the difference.

For the big Copilot fans, I already like you because you’re coding at ten times the speed you were before. However, I think you should give Cursor a shot— the tab + context is next level.

ABOUT THE AUTHOR

ABOUT THE reviewer

Sidd Seethepalli
Co-founder and CTO

Sidd Seethepalli, CTO and co-founder at Vellum (YC W23) is very passionate about LLM product development, and is constantly pushing the boundaries of what’s possible with current models and techniques for more than 100 customers at Vellum who use LLMs in production. Before starting Vellum, Sidd completed his undergrad at the Massachusetts Institute of Technology, then spent 4 years working for well known tech companies like Quora and Dover.

David Vargas
Full Stack Founding Engineer

A Full-Stack Founding Engineer at Vellum, David Vargas is an MIT graduate (2017) with experience at a Series C startup and as an independent open-source engineer. He built tools for thought through his company, SamePage, and now focuses on shaping the next era of AI-driven tools for thought at Vellum.

lAST UPDATED
Oct 1, 2024
share post
Expert verified
Related Posts
Guides
October 21, 2025
15 min
AI transformation playbook
LLM basics
October 20, 2025
8 min
The Top Enterprise AI Automation Platforms (Guide)
LLM basics
October 10, 2025
7 min
The Best AI Workflow Builders for Automating Business Processes
LLM basics
October 7, 2025
8 min
The Complete Guide to No‑Code AI Workflow Automation Tools
All
October 6, 2025
6 min
OpenAI's Agent Builder Explained
Product Updates
October 1, 2025
7
Vellum Product Update | September
The Best AI Tips — Direct To Your Inbox

Latest AI news, tips, and techniques

Specific tips for Your AI use cases

No spam

Oops! Something went wrong while submitting the form.

Each issue is packed with valuable resources, tools, and insights that help us stay ahead in AI development. We've discovered strategies and frameworks that boosted our efficiency by 30%, making it a must-read for anyone in the field.

Marina Trajkovska
Head of Engineering

This is just a great newsletter. The content is so helpful, even when I’m busy I read them.

Jeremy Hicks
Solutions Architect

Experiment, Evaluate, Deploy, Repeat.

AI development doesn’t end once you've defined your system. Learn how Vellum helps you manage the entire AI development lifecycle.

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Build AI agents in minutes with Vellum
Build agents that take on the busywork and free up hundreds of hours. No coding needed, just start creating.

General CTA component, Use {{general-cta}}

Build AI agents in minutes with Vellum
Build agents that take on the busywork and free up hundreds of hours. No coding needed, just start creating.

General CTA component  [For enterprise], Use {{general-cta-enterprise}}

The best AI agent platform for enterprises
Production-grade rigor in one platform: prompt builder, agent sandbox, and built-in evals and monitoring so your whole org can go AI native.

[Dynamic] Ebook CTA component using the Ebook CMS filtered by name of ebook.
Use {{ebook-cta}} and add a Ebook reference in the article

Thank you!
Your submission has been received!
Oops! Something went wrong while submitting the form.
Button Text

LLM leaderboard CTA component. Use {{llm-cta}}

Check our LLM leaderboard
Compare all open-source and proprietary model across different tasks like coding, math, reasoning and others.

Case study CTA component (ROI)

40% cost reduction on AI investment
Learn how Drata’s team uses Vellum and moves fast with AI initiatives, without sacrificing accuracy and security.

Case study CTA component (cutting eng overhead) = {{coursemojo-cta}}

6+ months on engineering time saved
Learn how CourseMojo uses Vellum to enable their domain experts to collaborate on AI initiatives, reaching 10x of business growth without expanding the engineering team.

Case study CTA component (Time to value) = {{time-cta}}

100x faster time to deployment for AI agents
See how RelyHealth uses Vellum to deliver hundreds of custom healthcare agents with the speed customers expect and the reliability healthcare demands.

[Dynamic] Guide CTA component using Blog Post CMS, filtering on Guides’ names

100x faster time to deployment for AI agents
See how RelyHealth uses Vellum to deliver hundreds of custom healthcare agents with the speed customers expect and the reliability healthcare demands.
New CTA
Sorts the trigger and email categories

Dynamic template box for healthcare, Use {{healthcare}}

Start with some of these healthcare examples

Healthcare explanations of a patient-doctor match
Summarize why a patient was matched with a specific provider.
Prior authorization navigator
Automate the prior authorization process for medical claims.

Dynamic template box for insurance, Use {{insurance}}

Start with some of these insurance examples

Insurance claims automation agent
Collect and analyze claim information, assess risk and verify policy details.
AI agent for claims review
Review healthcare claims, detect anomalies and benchmark pricing.
Agent that summarizes lengthy reports (PDF -> Summary)
Summarize all kinds of PDFs into easily digestible summaries.

Dynamic template box for eCommerce, Use {{ecommerce}}

Start with some of these eCommerce examples

E-commerce shopping agent
Check order status, manage shopping carts and process returns.

Dynamic template box for Marketing, Use {{marketing}}

Start with some of these marketing examples

LinkedIn Content Planning Agent
Create a 30-day Linkedin content plan based on your goals and target audience.
Competitor research agent
Scrape relevant case studies from competitors and extract ICP details.

Dynamic template box for Sales, Use {{sales}}

Start with some of these sales examples

Research agent for sales demos
Company research based on Linkedin and public data as a prep for sales demo.

Dynamic template box for Legal, Use {{legal}}

Start with some of these legal examples

Legal document processing agent
Process long and complex legal documents and generate legal research memorandum.
PDF Data Extraction to CSV
Extract unstructured data (PDF) into a structured format (CSV).

Dynamic template box for Supply Chain/Logistics, Use {{supply}}

Start with some of these supply chain examples

Risk assessment agent for supply chain operations
Comprehensive risk assessment for suppliers based on various data inputs.

Dynamic template box for Edtech, Use {{edtech}}

Start with some of these edtech examples

Turn LinkedIn Posts into Articles and Push to Notion
Convert your best Linkedin posts into long form content.

Dynamic template box for Compliance, Use {{compliance}}

Start with some of these compliance examples

No items found.

Dynamic template box for Customer Support, Use {{customer}}

Start with some of these customer support examples

Trust Center RAG Chatbot
Read from a vector database, and instantly answer questions about your security policies.
Q&A RAG Chatbot with Cohere reranking

Template box, 2 random templates, Use {{templates}}

Start with some of these agents

AI agent for claims review
Review healthcare claims, detect anomalies and benchmark pricing.
Financial Statement Review Workflow
Extract and review financial statements and their corresponding footnotes from SEC 10-K filings.

Template box, 6 random templates, Use {{templates-plus}}

Build AI agents in minutes

LinkedIn Content Planning Agent
Create a 30-day Linkedin content plan based on your goals and target audience.
Research agent for sales demos
Company research based on Linkedin and public data as a prep for sales demo.
Retail pricing optimizer agent
Analyze product data and market conditions and recommend pricing strategies.
Clinical trial matchmaker
Match patients to relevant clinical trials based on EHR.
Legal document processing agent
Process long and complex legal documents and generate legal research memorandum.
Financial Statement Review Workflow
Extract and review financial statements and their corresponding footnotes from SEC 10-K filings.

Build AI agents in minutes for

{{industry_name}}

Clinical trial matchmaker
Match patients to relevant clinical trials based on EHR.
Prior authorization navigator
Automate the prior authorization process for medical claims.
Population health insights reporter
Combine healthcare sources and structure data for population health management.
Legal document processing agent
Process long and complex legal documents and generate legal research memorandum.
Legal contract review AI agent
Asses legal contracts and check for required classes, asses risk and generate report.
Legal RAG chatbot
Chatbot that provides answers based on user queries and legal documents.

Case study results overview (usually added at top of case study)

What we did:

1-click

This is some text inside of a div block.

28,000+

Separate vector databases managed per tenant.

100+

Real-world eval tests run before every release.