Building Your Next Side Project: Which AI Model Should You Choose?
Z
Zack Saadioui
8/10/2025
Building Your Next Side Project: Which AI Model Should You Choose?
So, you've got an idea for a side project. A cool app, a helpful tool, something that's been bouncing around in your head for a while. & here's the thing: you want to sprinkle some of that AI magic on it. Pretty cool, right? But then you hit a wall. A BIG one.
Which AI model do you actually use?
Honestly, it feels like a new, "game-changing" model drops every other week. One minute, everyone's talking about GPT-4o's incredible coding skills. The next, it's all about Claude 3.7's massive context window or Gemini 2.5's killer multimodal features. Then you've got Grok, DeepSeek, Llama, & a million others. It’s enough to make your head spin.
Here's the secret though: picking the "best" model isn't about chasing whatever is at the top of the leaderboards this week. It's about finding the right model for your project. The one that fits your goals, your budget, & your sanity.
I've been down this rabbit hole more times than I can count, so let me be your guide. We're going to break it all down, from the big players to the nitty-gritty details, so you can make a smart choice & actually get back to building.
Step 1: Before You Even THINK About a Model, Define Your Mission
I know, I know, you want to get to the fun part. But trust me on this. Before you start comparing benchmarks or API pricing, you need to get crystal clear on what you're actually building. It’s the most common mistake I see people make: they pick a hyped-up model & then try to shoehorn their project into it.
Ask yourself these questions, & be brutally honest:
What's the core problem I'm solving? Are you building a chatbot to answer customer questions? A tool to summarize long articles? An app that generates creative stories? Be specific. "Automating customer support" is different from "building a sassy, brand-voiced chatbot for an e-commerce store."
What's my "good enough"? Do you need PhD-level accuracy, like in a medical or legal app? Or is "pretty good & fast" totally fine for your use case? A high-stakes financial analysis tool has VASTLY different requirements than a fun text-based adventure game.
What are my non-negotiables? Think about things like speed (latency), cost, & privacy. If your app needs to feel real-time, a slow but super-smart model might be a deal-breaker. If you're bootstrapping this side project, cost is probably a huge factor.
Once you have a handle on this, you can start to filter the noise. You’ve defined your "job-to-be-done," & now you can look for the right "employee" (the AI model) to do it.
The Major Players on the Field in 2025
Alright, let's get to the main event. The AI model landscape is dominated by a few big names, each with its own personality & strengths. Think of them as different specialists you can hire.
OpenAI's GPT Series (GPT-4o, GPT-5)
The All-Rounder Superstar: OpenAI's models, especially the newer ones like GPT-4o & the anticipated GPT-5, are often seen as the gold standard for a reason. They're incredibly versatile, excelling at everything from complex reasoning & coding to creative writing. If you need a model that can do a bit of everything & do it well, this is a strong starting point.
Best For: General-purpose applications, coding assistants, complex problem-solving, & creative text generation. GPT-4o, in particular, leads many coding benchmarks.
Keep in Mind: They can be on the pricier side. While powerful, you're paying for that premium performance, & the costs can add up, especially for a side project.
Anthropic's Claude Series (Claude 3.5 Sonnet, Claude 3.7)
The Thoughtful Coder & Writer: Claude has carved out a reputation for being particularly strong in coding & handling long, complex documents. It has a massive context window (the amount of information it can remember in a single conversation), making it perfect for tasks that require analyzing large amounts of text. Developers often rave about its coding abilities, with some even preferring it to GPT for specific programming tasks.
Best For: Coding projects, summarizing & querying large documents (like legal contracts or research papers), & tasks requiring a more "thoughtful" or nuanced response.
Keep in Mind: While the Sonnet models are getting more cost-effective, the top-tier Claude models can be expensive.
Google's Gemini Series (Gemini 2.5 Pro)
The Multimodal & Cost-Effective Workhorse: Google is a behemoth in AI research, & Gemini is their flagship series. Its biggest claim to fame is its native multimodality – it was built from the ground up to understand text, images, audio, & video seamlessly. It also offers a fantastic balance of performance & cost, making it a very strategic choice for many projects.
Best For: Applications that involve video or image analysis, tasks that require a long context window (Gemini can handle massive documents), & projects where cost-effectiveness is a major priority.
Keep in Mind: While incredibly powerful, some users find it can be a bit less "creative" than GPT in certain writing tasks.
The Wildcards: Grok, DeepSeek, & Open Source
Grok: Backed by xAI, Grok's unique selling point is its real-time access to data from X (formerly Twitter). It also has a reputation for having a more "unhinged" or rebellious personality, which could be a feature or a bug, depending on your brand voice.
DeepSeek: This is a name that keeps popping up in conversations about value. It offers performance that's surprisingly close to the top-tier models but at a fraction of the cost. If you're on a tight budget but don't want to compromise too much on quality, DeepSeek is definitely one to watch.
This brings us to a HUGE fork in the road...
The Great Debate: Proprietary vs. Open Source
This is one of the most fundamental decisions you'll make. Do you go with a polished, ready-to-use model from a big company (proprietary), or do you roll up your sleeves with an open-source model?
Proprietary Models (The "API Crew")
These are the models we just discussed: GPT, Claude, Gemini, etc. You access them through an API (Application Programming Interface).
Pros:
Ease of Use: This is the biggest win. You just sign up, get an API key, & you can start making calls in minutes. The big companies handle all the messy infrastructure for you.
State-of-the-Art Performance: Companies like OpenAI & Google are pouring billions into research, so their models are often at the cutting edge of what's possible.
Support & Reliability: You get dedicated support, regular updates, & a certain level of reliability that comes with a paid product.
Cons:
Cost: You're paying per "token" (think of tokens as pieces of words). For a popular side project, this can get expensive FAST. We're talking anywhere from a few cents to several dollars per million tokens, depending on the model.
Black Box: You have no real visibility into how the model works. You can't peek under the hood or modify its core architecture.
Vendor Lock-In: It can be a pain to switch providers if your entire project is built around one specific API.
Open-Source Models (The DIY Heroes)
These are models like Meta's Llama series, Mistral, or the thousands of models available on platforms like Hugging Face.
Pros:
Control & Customization: You have full control. You can download the model, modify it, fine-tune it on your own data, & run it wherever you want. This is HUGE for creating a truly unique AI experience.
Cost-Effective (in theory): There are no licensing fees. You're not paying per API call.
Transparency: You can see the code & understand how the model is built, which is great for privacy-sensitive applications or academic projects.
Cons:
The Infrastructure Headache: This is the big one. YOU are responsible for running the model. This means dealing with servers, GPUs, & all the technical complexities that come with it. It can be a significant undertaking & cost.
Requires Expertise: You need a deeper level of technical knowledge to effectively deploy, manage, & maintain an open-source model.
Quality Varies: While some open-source models are incredible, the quality can be inconsistent. You have to do your homework to find a good one.
So, which one is for you? If you're building a quick prototype or want to move fast without getting bogged down in infrastructure, a proprietary API is your best bet. If you have a unique dataset, need deep customization, or are building something for the long haul where you want full ownership, open-source is the way to go.
Fine-Tuning vs. Pre-Trained: Do You Need to Go to School?
Here's another layer to the onion. Most of the models we've talked about are pre-trained. They've already been trained on a massive chunk of the internet & have a general understanding of language, reasoning, & coding. For many, MANY side projects, a pre-trained model is all you need.
But what if your project has a very specific domain? Like, you're building a chatbot that needs to understand the nuances of ancient Roman history, or a tool that writes in a very specific legal jargon. A general-purpose model might struggle with that.
This is where fine-tuning comes in.
Fine-tuning is the process of taking a powerful pre-trained model & training it a little bit more on your own, smaller, task-specific dataset. It’s like sending a brilliant college grad to a specialized trade school. They already have the foundational knowledge; you're just teaching them the specific skills for your job.
When to Fine-Tune:
You have a unique, high-quality dataset that the model hasn't seen before.
You need the model to adopt a very specific tone, style, or knowledge base.
You're in a niche industry with its own jargon (e.g., medical, legal, finance).
When to Stick with Pre-Trained:
Your task is fairly general (summarization, general Q&A, content creation).
You don't have a specific dataset to train on.
You want to get up & running as quickly & cheaply as possible.
Honestly, for most side projects, starting with a pre-trained model is the right move. You'd be surprised how far you can get with clever prompting. Only consider fine-tuning if you hit a wall & find the general models just aren't cutting it for your specific use case.
Let's Talk About Money: The Cost of AI
This is the part that often trips people up. The cost of building an AI-powered project isn't just about the model itself. It's a whole ecosystem of expenses.
API Costs: If you go the proprietary route, this will be your main recurring cost. OpenAI, for example, has different pricing tiers for its models, from the cheaper "nano" versions to the powerful but expensive flagship models. A simple project might only cost a few dollars a month, but a popular one could easily run into the hundreds or thousands.
Hardware & Infrastructure: If you're using an open-source model, you have to pay for the servers to run it on. GPUs are expensive, & cloud platforms like AWS or Google Cloud charge by the hour. This can range from a few hundred to tens of thousands of dollars a month for large-scale training.
Data Costs: AI is hungry for data. If you need to acquire, clean, or label data for fine-tuning, that's a significant cost. Data preparation can sometimes account for 30% or more of the entire project budget.
Development Costs: Your time is valuable! Or, if you're hiring someone, their time is expensive. Building, integrating, & maintaining the AI system takes effort. A simple MVP might be in the $10,k-$50k range, while a more complex system can easily exceed $150k.
For a side project, the key is to start small. Use a cost-effective proprietary model like Gemini or a cheaper GPT variant to validate your idea. Track your API usage like a hawk. Only scale up or move to a more expensive setup once you have proof that your project is gaining traction.
A Practical Example: Building an AI Customer Service Chatbot
Let's make this real. Imagine you're building a customer service chatbot for your e-commerce store that sells handmade leather goods.
Your goal is to have a chatbot on your website that can answer common questions 24/7, like "Where's my order?", "What's your return policy?", & "Do you do custom designs?". You want it to have a friendly, slightly rustic tone that matches your brand.
Here's how you might approach it:
Define the Mission: The chatbot needs to answer factual questions based on your store's data (FAQs, policies, etc.) & engage visitors. Speed is important, but it doesn't need to write a novel. The tone should be conversational & helpful.
Model Choice: A pre-trained proprietary model is the PERFECT starting point here. You don't want the headache of managing your own server. A model like GPT-4o or Claude 3.5 Sonnet would be great because they're strong at conversational AI & following instructions. You probably don't need the absolute top-tier, most expensive model right away.
The "Fine-Tuning" Alternative: Instead of true fine-tuning, you could use a platform that makes this process easier. This is where a tool like Arsturn comes in. Arsturn helps businesses create custom AI chatbots trained on their own data. You could simply upload your FAQ pages, policy documents, & product descriptions, & Arsturn would handle the process of creating a chatbot that knows your business inside & out. It's a no-code way to get a "fine-tuned" experience without the technical overhead, providing instant customer support & engaging with website visitors 24/7.
Implementation: You'd use the API from your chosen model (or the Arsturn platform) to power the chat interface on your website. When a user asks a question, you send that question along with some context (like your store's policies) to the AI, & it generates a response.
Cost Management: You'd start with a lower-cost model & monitor your API calls. If your store gets a TON of traffic, you might explore how Arsturn can help manage those interactions efficiently, as it's designed as a business solution to boost conversions & provide personalized customer experiences without breaking the bank.
Tying It All Together
Okay, that was a LOT. But here's the bottom line.
Choosing an AI model isn't a one-time, perfect decision. It's a process of iteration. Start by deeply understanding your project's needs. Pick a model that seems like the best fit based on its strengths – whether that's coding, creativity, or cost-effectiveness.
Don't be afraid to start with a simple, pre-trained proprietary model to get your idea off the ground. You can ALWAYS get more complex later. Test different models. See what works for YOUR specific use case & your actual data. The "best" model on a benchmark might not be the best model for you.
The AI world moves at lightning speed, but the fundamentals of good product development don't. Start with the problem, understand the trade-offs, & build something that genuinely works. The right AI model is just a tool to help you get there.
Hope this was helpful. Now go build something awesome, & let me know what you think.