We’ve reached a weird point in AI where building something cool often feels like burning money.

The default move? Plug into OpenAI’s API.
The reality? Not everyone can afford it.

If you're a backend dev building an AI feature for your startup—or even a solo hacker building an app—you’ve probably hit the same wall: ChatGPT API gets expensive, fast. You wouldn’t hire a neurosurgeon to mop a floor, right? Well, that’s kind of what it feels like when you use a general-purpose, billion-dollar model to do simple AI tasks in your app.

So what’s everyone actually doing behind the scenes?
They’re using LLaMA: an open-source LLM from Meta—and fine-tuning it.

That’s right. The big secret? Most AI features you’re seeing in products today are not using GPT-4. They’re using smaller, cheaper, local models like LLaMA, Mistral, Mixtral, etc., with just enough training to make them useful for a specific domain.

We’ve been doing this for the last 9 months, and here’s what we’ve learned.

🧠 Fine-tuning a model is like onboarding an intern
Fine-tuning sounds like a scary ML concept—but it's really just structured training in three key areas:

  1. Vocabulary
    Generic models don’t know your industry terms.
    Just like a new intern, they need to be taught the glossary: what "CAC", "NPS", or "TVL" mean in your context.

  2. Tooling (aka Agents)
    The model has to learn which tools to use when.
    If you ask it to value a company, it should know to use DCF (Discounted Cash Flow).
    If you ask it to do basic math, it should know to just use a calculator.
    You have to teach it which “tool” applies to which type of problem.

  3. Reasoning
    Finally, it has to learn how to think—how to approach certain types of questions logically and consistently.

This is how you go from a generic model that kind of understands everything… to a focused AI assistant that gets your domain and delivers real value.

🛠️ The current problem: Fine-tuning is still messy
If you're an ML engineer, you probably have your go-to stack: Hugging Face, Axolotl, LoRA, maybe some Colab or AWS tricks.

But what we’re seeing more and more is backend developers being pulled into the AI world—not to build models from scratch, but to integrate LLM features into real apps. And for them, this is where things break down.

There’s no unified framework.
There’s no plug-and-play setup.
It’s more art than science right now.

🚀 What we’re building

So here’s the idea:
We’re working on a tool for backend developers that makes fine-tuning LLaMA models as simple as clicking a button.

Think Alchemy, but for LLM fine-tuning.

Got a use case? Upload your domain data.

Want a specific behavior? Configure the model’s reasoning flow.

Need to run it cheap? Export to local or rented compute.

One click = production-ready, fine-tuned LLM relevant to your app or industry.

🧠 Why this matters
Let’s say you’re building something like a Reddit-native CRM.
You want the AI to qualify leads based on Reddit discussions.

If you use ChatGPT for this, you’ll go broke.
If you use a generic LLM, it won’t understand Reddit culture.
If you want it to get better, you’ll have to fine-tune it—probably on domain-specific posts, upvotes, or subreddits.

But unless you’re an ML expert, you’re going to need tools made for devs.

That’s the gap we’re aiming to close.

👇 Over to you
Are you building AI features with open models?

What’s been the hardest part of fine-tuning for your team?

Would a tool like this be useful for you?

Let’s start a conversation — we’re building this for devs who just want to ship great AI features without becoming ML researchers overnight.