In my last blog I wrote about how my journey using and learning about AI tools in my software engineering role. I briefly also wrote about my team and I used AI tools to improve our development workflows and speed up our product’s development process. Since the time I wrote that blog, there have been leaps and bounds to the number of tools available for development and it has dramatically changed how I approach a problem in general.
I thought it would be good to write more about what I have seen continue my train of thought around Engineering with AI. I see there is a lot of value in me spending time to express my thoughts and experiences on this blog series. Trying to write an essay without AI tools has never been harder.
Keeping up
It’s exciting and challenging to keep up. I feel like I am gonna miss the AI train if I don’t keep up. Every few weeks/months there is big leap in LLM tools. Each model claims to be more powerful than the previous one with lots of graphs and tables explaining the tests and evaluation metrics. One of the major focuses in these evaluations are around how much faster or better the model is in code generation, image generation or video generation tasks.
Some times it feels like it’s little but other times it feels like AI can solve any given problem in the world. Apart from tools, there are new ways to interact with LLM tools, including MCP servers and Agentic AI. As of writing this blog I haven’t explored a whole lot of these tools.
Being spoilt for choice
AI tools for the software development industry all the way from hands-free development websites like v0.dev by Vercel, lovable.dev, bolt.ai to IDE and CLI applications like Cursor IDE, Windsurf IDE, Claude Code & Gemini CLI.
All the tools either have free versions with limits this is to increase the user base of the tool. Some users will understand the usefulness and novelty of the AI tool enough to make a decision to upgrade to the paid version while others try to understand the best way to use the free version of the AI tool in question.
Which brings me to the next question.
Is spending money on paid versions opportunity cost?
The FOMO (Feeling Of Missing Out) of not utilizing the latest and greatest models has me more often than not deciding which paid/Pro/Plus version I must purchase? I have seen times where I have purchased ChatGPT Plus, Claude Pro, Cursor and a HuggingFace subscriptions all at the same time.
The decision to upgrade to a paid version of an AI tool is difficult for various reasons. The pricing of most of these tools are around the 20$ per month mark which translates to roughly 2000 INR a month. I always trick myself to think that this is an opportunity cost I am paying. I say to myself I will learn something new or be able to solve some complex problem more easily than other people using the free models. In fact I use some of these paid AI tools more just because I have paid for it; and have explored some cools features before my peers can. A good example of opportunity cost for me is getting to use Claude Code earlier than my peers who use the free model.
Paying for AI models is a commitment and for most users in a country like India, it’s difficult to commit to a subscription. The logic is simple if you spend 20$ per month per tool on more than one tool, you will end up spending close to 240$ to 480$ a year or 21,000 INR to 42000 INR a year. People I work with are surprised to know that I pay for Plus / Pro variants of AI tools, some times even multiple paid tools. Their argument is that there are ample free models available which give comparable results to paid versions. Some of the bright minds that I work with also have figured out prompting strategies etc. to be able to make the most of the free models and in some cases give better results than the free version. I am not that smart.
I have switched between paying for ChatGPT Plus and Claude Pro multiple times. Depending on what I was doing I would cancel my subscription and move to the other. Notable examples of when I switched is when the “Ghibli Art” trend caught along and when “Claude Code” was released. I have always found that the Plus/Pro versions gave slightly better results than the free version; this might be a confirmation bias, but I tend to be more confident with the outputs I get from the paid version. I have been able to the use the latest/greatest models and see the benefits of higher token limits, context windows and a variety of output options. Models like o4-mini-high and Claude Opus 4 really helped me increase my trust and skill in AI assisted problem solving and development.
Trying out these paid tools helped me recognize the value of these tools in the software development space and also stay ahead of the curve. Since you don’t hit the limits frequently, you are encouraged to use the tools more effectively by starting more prompts and experimenting.
AI is becoming more accessible
I believe that AI is becoming more accessible to people. Free models are getting good and there are more entry-level paid plans for people.
Cheaper Plans
A very good recent example is Perplexity’s partnership with Airtel in India to give away Perplexity Pro’s 1 year subscription for free. As users utilized the free subscription they realized that it was the same as the free version with just a few more capabilities. Perplexity here might be trying to tap in to a market of users who will build dependencies.
ChatGPT also release a Rs. 399 per month plan so that students and individuals wanting to utilize AI more effectively can claim that slight edge over people who don’t use it. To OpenAI it is a game of numbers in India and monetizing a niche market of young developers and working class who might not be able to pay 20$ a month but can spare 9$.
Open Source Models
There is also a wave of free open source models which are getting good and becoming accessible for free through Ollama. Some recommended models to try is Mistral, Llama by Meta and Gemma by Google. These models perform well enough with basic compute and memory. I have tried running Mistral 7B and Llama3.1 8B while trying to build local chat bots for some side projects. I see these models are good for most general purpose workflows like email reformatting, basic bash scripting and content generation. The only limiting factor here is the need for GPU memory and fast storage.
Projects like Open Web UI are also making it easier for organizations to deploy the above open source models on a chat interface to introduce their employees to AI in high compliance environments like finance and healthcare.
My recommendation
Keeping up with AI has been tough. The benefits of using AI tools have never been better. Paying for an AI tool can benefit you in some ways while choosing to use the free versions help you understand best-practices and prompting strategies to make most of the free models. Open source models will help you start learning more about the use cases of AI, but you’re limited by GPU memory.
Get one paid plan if you can. Experiment deploying local models using tools like Ollama. Explore more powerful models for specific tasks using platforms like HuggingFace which allow you to pay for the compute/memory and GPU for only the resources you have used on an hourly basis. This will help you understand the benefits and challenges of using AI.
