TinyTune
No-code platform for fine-tuning open-source LLMs with private data.
Product memo
Developers and teams use TinyTune to create custom AI models from open-source LLMs like Gemma, Qwen, and Mistral. It removes the need for machine learning expertise or infrastructure management, letting users fine-tune models with their own private data. This approach gives teams full ownership of their custom models, making it easier to deploy AI for specific tasks such as customer support or content generation.
- For who
- Developers and teams fine-tuning LLMs
- Solves what
- Custom AI models from open-source LLMs without ML expertise.
- No-code LLM fine-tuning
- Private data handling
- One-click deployment
Operator & company
Operators
1 source claim
Jacques Marais
Founder claim · Identity unverified
Listed as founder; no verified public profile is linked yet.
Company
- Founded
Nov 2025
Operating model
- Business model
One Time
- Platform
Web app
- Audience
Developers
- Payments
Polar
Builder strategy
- Strategy Type
- Niche Specialist
- Stage
- Pre Revenue
- Effort
- Solo Buildable
Source evidence
Evidence sources
About TinyTune Expand
TinyTune simplifies the process of creating custom AI models by allowing developers and teams to fine-tune open-source large language models (LLMs) using their own private data. The platform supports popular LLMs such as Gemma, Qwen, and Mistral, making advanced AI customization accessible without requiring specialized machine learning expertise.
This helps teams deploy tailored AI products for specific domain tasks, ensuring data privacy and full model ownership through a straightforward, credit-based purchasing model. It removes the maintenance work of managing complex ML infrastructure, letting users focus on application development.
In their own words
Fine-tune AI models
in minutes, not months
Fine-tune large language models with your own data. Simple, powerful, and affordable.
Competitive context
5 peers · Same primary niche.
Commercial cues
- Model
- one time
- Free tier
- No
- Trial
- No
Pricing strategy
- • One-time credit purchases remove subscription friction for project-based work.
- • Tiered credit bundles offer clear volume discounts for higher usage needs.