We believe enterprise-grade LLM fine-tuning shouldn't require a PhD, a dedicated ML team, or a six-figure budget.
Fine-Tune Lab was born from frustration. We spent months trying to fine-tune models for production and hit the same walls everyone else does:
We built Fine-Tune Lab to fix all of this. One platform where you upload a dataset, see training happen in real-time, test your model with actual context, and deploy to production with one click.
Our goal is simple: Make fine-tuning so easy that any developer can train a production-ready model in under 2 minutes.
These aren't just words on a page. They guide every decision we make, from feature design to customer support.
Built by one developer who needed to test fine-tuned models and found the process incredibly difficult. Every feature solves a real problem I faced.
Enterprise-grade AI training should be available to everyone, not just companies with ML teams and massive budgets.
No hidden costs, no confusing pricing. Real-time metrics, open documentation, and honest communication.
From dataset upload to production deployment in under 2 minutes. Because your time matters.
Real-time monitoring, automated testing, LLM-as-judge evaluation. Ship models you can trust.
We obsess over details. From API design to error messages, everything is crafted with care.
Started building on a 4-core, 4-thread Linux machine. Just me, frustrated with fine-tuning complexity.
Successfully trained and tested my first model end-to-end. The platform actually worked.
Added WebSocket streaming for live training updates. No more refreshing tabs.
Built GraphRAG testing, analytics dashboard, and automated deployment pipeline.
From a 4-core Linux box to a full production platform in 4 months.
We're a small, focused team obsessed with developer experience. Every feature goes through the "would I use this?" test.
New features every week based on user feedback
Your feature requests shape our roadmap
Discord, email, or in-app chat support
Start training production-ready models today. No credit card required.