A practical guide for individuals using Claude, GPT, Gemini, and DeepSeek, and what a bootstrapped Indian team learned building a workspace around them.
Claude for writing. GPT for brainstorming. Gemini for research. DeepSeek because someone recommended it for reasoning.
Four tabs. Four logins. Maybe four bills.
And another fresh conversation that needs an introduction to your work.
The models are better than ever. The way we use them can still be messy, expensive, and repetitive.
India is betting big on AI
In March 2024, the Union Cabinet approved the IndiaAI Mission with an outlay of ₹10,371.92 crore over five years. It covers compute, datasets, homegrown foundation models, startup financing, and skills.
Homegrown model efforts are growing too. Sarvam AI is one of the organisations selected to build indigenous foundation models, with stated financial and compute support of ₹246.72 crore. BharatGen is building multilingual models for Indian languages.
One clarification: ₹10,371.92 crore is an approved programme budget, not a claim that the full amount has already been spent. It also does not represent all public and private AI investment in India.
National investment builds the foundation. But individuals face a more immediate question:
How do I use these models without wasting money, losing my work, or sharing more information than the task requires?
That is the question our team at 123sudo has been working on with 9xchat.
Disclosure: I am a co-founder of 123sudo, the team that builds 9xchat.
Problem 1: You are paying for tools you barely use
A monthly subscription costs the same whether you use it every day or twice a month.
Try this: list every AI subscription you pay for. Next to each one, write how often you actually used it last month.
You may find overlap or a tool you no longer need.
How 9xchat approaches it: one workspace with cloud models through OpenRouter, local models through Ollama, and the option to bring your own supported API keys.
Its credit-based cloud workflow uses transparent credits, helping you understand where your usage goes.
That does not automatically make AI cheaper. Heavy use of advanced models still costs money. The benefit is being able to assess your spending rather than guessing.
Problem 2: You use an expensive model for every task
Not every task needs the most powerful model.
Reformatting a list does not require the same capability as reviewing a complex code change.
A practical routing habit
Model families contain different versions with different strengths and prices. Test the actual model on your task instead of assuming the brand name guarantees quality.
In 9xchat, you can work with multiple models without opening another app.
Using lower-cost models for straightforward tasks can reduce spending, provided the output still meets your needs.
Problem 3: You keep re-explaining the same project
This is the hidden effort that rarely appears in a pricing table.
You open a new conversation, paste in your project brief, find the earlier decisions, and explain what you have already tried.
I am a co-founder who does not code, and I am close to ₹1 lakh in estimated AI spending this year across the tools and agent skills I use.
That made me pay closer attention to how much work involved repeating context rather than making progress.
How 9xchat helps keep context connected
- Persistent memory across models helps carry important information forward.
- Separate memory scopes distinguish broader context from individual chat and agent context.
- A knowledge base lets you save project material and access it from chats.
Memory does not make context free. Information sent to a cloud model can still contribute to token usage.
The useful goal is less repeated setup and more relevant context, not a promise that every conversation will cost less.
A habit that helps regardless of tool: keep a short, current project brief covering what you are building, who it is for, what is decided, and what comes next.
Problem 4: You are not sure where your data goes
A locally stored chat and a locally processed AI request are not the same thing.
Here is the distinction worth understanding:
- Local storage: 9xchat describes chats, notes, and memory as stored on your device.
- Local models: Ollama lets you run supported models on your own machine.
- Cloud models: using a cloud model requires sending the request and relevant context to the services involved in processing it.
- Tools and agents: web access, publishing, and other external actions may transmit information even when part of your workflow runs locally.
A local model can help limit external processing, but review the complete workflow, not just the model selector.
For cloud requests, share only what is necessary and check the relevant provider and routing-service policies.
Privacy is a set of choices and boundaries, not a magic setting.
Problem 5: The answer is not the finished task
AI gives you a plan. You still have to organize the steps, prepare the draft, review it, and move it toward completion.
How 9xchat approaches the gap
- Skills provide a repeatable approach to a job, such as reviewing code for bugs and missing tests.
- Agentic workflows help carry out multi-step tasks, including publishing a post.
- A floating copilot window makes AI accessible alongside your browser, IDE, or terminal.
- Voice notes can turn spoken ideas into structured Markdown.
Publishing and scheduling are different capabilities: 9xchat can publish as part of an agentic workflow, but it does not currently schedule posts.
You still need to review facts, permissions, and consequential actions. The point is to reduce the manual handoffs between a useful answer and a completed task.
Your 10-minute AI cost reset
Whatever tool you use, try this:
- List your AI subscriptions and how often you used each one.
- Review overlapping plans before cancelling or pausing anything.
- Write one current project brief and save it somewhere reusable.
- Choose a lower-cost default model for straightforward tasks.
- Review data handling before working with sensitive information.
- Check usage after one week, alongside the quality and usefulness of the results.
Look at cost per useful outcome, not just cost per response.
A cheap answer that requires repeated corrections may not be the better deal.
Built in India, for everyday work
123sudo is a bootstrapped team from India. We have been building everyday internet utilities since 2014, with a simple philosophy:
Software should solve the problem, respect the user, and get out of the way.
Our product page reports that more than 500 people have used 9xchat across 82 countries, with availability listed for Windows, macOS, Linux, iOS, and Android.
We are not claiming that 9xchat currently supports Sarvam or BharatGen. Their work belongs to the model layer. Our focus is the workspace layer: helping individuals use AI in a more connected way.
India's AI future needs compute, research, and homegrown models. It also needs practical products that help a student, freelancer, creator, or founder turn those capabilities into useful work.
Try 9xchat with a 3-day free trial and welcome credits at 123sudo.com.
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