Custom AI Models Without Writing Training Code
Ertas Studio lets vibecoders and no-code builders create fine-tuned AI models through a visual interface — no Python scripts, no terminal commands, no ML background required.
The Challenges You Face
AI APIs Are a Recurring Cost You Cannot Control
Every API call to a hosted LLM adds up. As your app grows, so does your bill — and you have no leverage to reduce per-token costs. A viral moment can turn your side project into a financial liability overnight.
Generic Models Do Not Understand Your Domain
Off-the-shelf models give generic answers. When your app is about a specific niche — whether it is astrology, recipe generation, or fitness coaching — the base model lacks the depth and tone your users expect, and prompt engineering only goes so far.
Fine-Tuning Tutorials Assume You Are an ML Engineer
Most guides on model customization dive straight into Hugging Face Trainer arguments, CUDA setup, and distributed training. If your strength is building products with visual tools, these tutorials feel like a foreign language.
Vendor Lock-In Limits Your Options
Building on a single API provider means your product lives or dies by their pricing decisions, rate limits, and content policies. If they deprecate a model or change their terms, you scramble to migrate.
How Ertas Solves This
Ertas Studio was designed for people who build with intuition and speed rather than infrastructure expertise. The entire fine-tuning workflow — from data upload to model export — happens through a visual interface that feels more like a design tool than a machine-learning platform.
You bring your examples in a simple JSONL format (or paste them into the built-in editor), pick a base model from the catalog, and click train. Studio handles the cloud GPU orchestration, LoRA adapter configuration, and checkpoint management behind the scenes. When training finishes, you download a GGUF file and run it locally — zero ongoing API costs.
This means you can create a model that speaks in your brand voice, understands your niche domain, and runs on hardware you already own. Your AI feature becomes a fixed cost, not a variable one, and you are free from the whims of any single API provider.
Key Features for Vibecoders & No-Code Builders
Drag-and-Drop Dataset Builder
Paste examples, import CSVs, or upload JSONL files through a visual editor. Studio validates your data format in real time and flags issues before you waste compute on a bad dataset.
One-Click Training
Select a base model, review the auto-configured training settings, and click Start. No terminal, no scripts, no environment setup. Training runs on managed cloud GPUs and notifies you when it is done.
Local Model Ownership
Exported GGUF models run on your laptop, a home server, or any device that supports llama.cpp. You own the weights outright — no subscriptions, no per-query fees, no usage caps.
Model Playground
Test your fine-tuned model with an interactive chat interface before deploying it. Compare outputs between different training runs to pick the best performer without leaving the browser.
Why It Works
- Vibecoders have used Studio to ship custom AI features in apps built on Bubble, FlutterFlow, and Retool — connecting to locally-hosted models via simple HTTP endpoints.
- Replacing a $200/month API bill with a self-hosted fine-tuned model typically pays for the Studio subscription within the first billing cycle.
- No-code builders with zero ML background have successfully fine-tuned models on their first attempt using Studio's guided workflow.
- The visual training interface reduces the learning curve from weeks of ML study to a single afternoon of experimentation.
- Every model you create is portable — export it once and run it anywhere, from a MacBook to a cloud VM to an edge device.
Example Workflow
Say you are building a no-code app that generates personalized meal plans. You compile 200 examples of user profiles paired with ideal meal plans. You open Ertas Studio, upload the JSONL file, and select a 7B instruction-tuned base model. The defaults look good, so you click Start Training.
While you wait, you continue wiring up your Bubble app. Twenty minutes later, Studio notifies you that training is complete. You open the playground, test a few prompts, and the model nails the tone and format. You export the GGUF, spin up an Ollama instance on a $10/month VPS, point your app at it, and your meal-plan AI is live — with a fixed hosting cost instead of a per-request API fee.
Related Resources
Ship AI that runs on your users' devices.
Early bird pricing starts at $14.50/mo — locked in for life. Plans for builders and agencies.