Introduction

To introduce myself briefly: I am a Data Scientist with over two years of professional experience and more than five years of programming in Python. My work primarily revolves around backend development and AI. While I had not previously managed complete projects independently, I had typically been assigned various tasks.

Eventually, I felt equipped with enough knowledge to create something on my own, and I successfully did so. Contrary to my expectations, the process wasn’t overwhelmingly challenging. In fact, my co-founder and I launched it in just one month, generating profits and attracting over 1,000 visits without any paid advertising.

So, why consider starting a startup if you haven't already?

There’s no single course or project that has taught me as much as this experience. Completing a course imparts specific skills, but working on a product from start to finish provides insight into the entire development cycle—from design to implementation and deployment. I refined my existing skills, acquired new ones, and gained a deeper understanding of how frontend and backend systems interact. This awareness also enabled me, as a Python developer, to write more effective APIs for my frontend colleagues.

Moreover, building a startup is an impressive addition to your resume. The time spent on your startup counts as legitimate work experience, unlike personal projects, and it enhances your portfolio—especially if it consists of a live, accessible website.

Lastly, and perhaps most importantly, there's the potential for financial gain. If your startup operates as a Software as a Service (SaaS), it can become a source of passive income. Additionally, with experience from creating multiple small SaaS applications, you can enhance your speed and professionalism, allowing for quicker development of new, profitable products.

In this article, I'll recount my experiences in building my startup and share actionable tips and insights.

Finding Ideas

You may already have a product idea, or perhaps you're still brainstorming. Idea generation is inherently creative, lacking a standardized approach. However, there is a method for validating your concepts:

1. Market Research: Assess the relevant markets. For instance, if you're interested in creating a website for book rentals, analyze market sizes, rental service statistics, and last year's book sales. A declining trend in these markets can be a red flag; for example, the rise of e-books has diminished the popularity of physical books.
2. Competitor Analysis: If numerous competitors exist, your solution must significantly outperform theirs—ideally by a factor of five to ten. A sparse competitive landscape can be a good sign; it suggests you can learn from existing products and gauge market fit. However, a complete absence of competitors can indicate a potential issue—either your idea is groundbreaking or it’s a "tarpit" idea that others have unsuccessfully attempted.
3. Customer Analysis: Determine your target audience, their willingness to pay, and the frequency of the problem your product addresses.

This is the perfect moment to introduce my startup, https://validator.yazero.io, which aids in the validation process through AI, web scraping, various APIs, and quantifiable feedback.

Another tip while brainstorming ideas is to leverage your unique background. For example, if you hold a degree in Physics, utilize that knowledge. If you know someone in the legal field, ask them about their challenges and how you might provide solutions.

For further reading on ideation, check out my previous article—a guide for tech professionals looking to start a startup.

Finding a Co-Founder

If there's a component of the startup you’re not keen on—be it marketing, frontend development, or sales—consider finding a co-founder. Not only will they bring expertise in their field, but they’ll also share the workload. Building a product should be enjoyable, so delegate tasks that don’t interest you.

Additionally, having a co-founder provides valuable validation. They can help prevent you from becoming overly attached to potentially flawed ideas, offer constructive feedback, and share your enthusiasm for building the venture. This collaboration can accelerate your startup's progress significantly.

For instance, if you lack frontend experience, learning a new framework can be daunting and time-consuming. Delegating this to a more experienced co-founder can lead to faster, higher-quality results, allowing you to focus on areas that excite you.

You can find potential co-founders on platforms like YCombinator’s co-founder matching service, where I found mine and have been pleased with that choice.

In terms of collaboration tools, we utilize Todoist for task management, GitHub for version control and automated deployment, and Figma for design brainstorming.

Frontend Development

Let’s delve into the technical aspects.

My primary advice for frontend development—if you opt to handle it yourself—is to stick with a framework you're familiar with. Even if it's considered outdated or unpopular, familiarity is crucial. The goal is to deliver a working solution to users; if it meets their needs, it’s successful.

A common pitfall for novice frontend developers is the tendency to chase trends and adopt new frameworks recommended by influencers, leading to a constant learning cycle with each project. Instead, focus on honing your skills with the framework you know, enabling faster development and more efficient troubleshooting.

For Python enthusiasts, I recommend exploring my lists of top frontend libraries in Python, which provide a thorough comparison of the most utilized libraries and their strengths and weaknesses. Familiarity with these tools can expedite your product development.

When it comes to deployment, you have two main options:

1. Utilizing Existing Platforms: Services like Vercel, Netlify, and AWS Amplify are user-friendly, particularly for popular frameworks like NextJS and React. However, most of these platforms don’t offer free deployment for commercial projects—Vercel, for instance, charges $20, while AWS Amplify has a free tier but includes a cold start for infrequently visited sites.
2. Self-Hosting Solutions: Providers like Hetzner, AWS, and Google Cloud offer more control, though they often lack free tiers. However, you can find affordable options; for example, Hetzner provides a private server with 2 vCPUs and 4GB of RAM for just €5, typically sufficient for hosting applications with fewer than 100 concurrent users. Self-hosting also enhances your skills in areas like DNS, SSL, and Docker, benefiting your resume.

Backend Development

This section will be more detailed, as I primarily focus on backend development within our startup.

A common question arises: why is a backend necessary? Why not perform all computations client-side?

The answer lies in enhanced security, observability, and control over processes, along with better logging capabilities and the ability to utilize greater computational resources. For instance, hosting a large machine learning model on a user’s device can be impractical; a dedicated server with GPUs, accessed via an API, is a more effective solution.

The primary tools I use for our backend include FastAPI with Pydantic, Gunicorn, Docker, Docker Compose, and Nginx. I favor Docker for its reliability across different operating systems and environments. It’s particularly useful when multiple application components need to run concurrently, such as a database alongside the backend.

Monitoring is crucial for backend systems. I employ two solutions—Prometheus and FastAPI-analytics—to provide a dashboard for tracking API performance, including request success rates, response times, and usage statistics.

Monitoring enables you to detect bugs or performance issues and assess whether your server requires upgrades in resources.

I typically deploy my solutions on VPS from Hetzner for cost-effectiveness.

There’s a common misconception that “Python is too slow or heavy for production.” However, in most cases, this is inaccurate.

Even if a request takes 5 milliseconds instead of 1, the difference is negligible for users. What truly matters is the speed of development; completing your project in days rather than weeks is a significant advantage.

Additionally, many libraries are optimized or utilize languages like Fortran, C, or Rust under the hood, so performance remains comparable.

Finally, in AI applications, particularly those involving large language models, response generation may take substantial time, which is independent of the programming language used. Therefore, if a request takes 20 seconds, the performance of a Python calculation that takes 0.1 seconds is irrelevant.

Summary

In this article, we explored key aspects of launching your own startup—its importance, how to identify promising ideas, strategies for frontend and backend development, and insights on deployment.

I welcome your feedback on whether this information was helpful and if I should consider writing a follow-up piece on topics such as AI in production, MLOps, marketing strategies, sales, and pricing models.

I can also provide a deeper dive into backend productionalization with practical examples and guides. Please share your thoughts in the comments!

Thank you for reading, and happy coding!

References

1. https://validator.yazero.io
2. https://yazero.io
3. https://shelamanov.medium.com/starting-a-startup-guide-for-techies-d220119c0b07
4. https://www.ycombinator.com/cofounder-matching
5. https://python.plainenglish.io/top-5-python-frontend-libraries-for-data-science-91261a65e366
6. https://readmedium.com/top-5-python-frontend-libraries-for-data-science-part-2-4d07a48d2fde
7. https://solara.dev/
8. https://github.com/trallnag/prometheus-fastapi-instrumentator
9. https://github.com/tom-draper/api-analytics