Train, Fine-Tune, and Deploy AI Models at Scale
Supervised fine-tuning, reinforcement learning, benchmarking, multi-tower pipelines, and GPU deployment — all in one platform.

Pipeline builder
Build multi-tower model pipelines visually
Combine multiple models in the same pipeline — time series analysis, transformer classifiers, ensemble architectures — all connected in a visual editor.
- Multi-tower architecture
- Chain specialized models together: feed time series data through an analyzer, then route outputs to a transformer for classification or generation.
- Visual pipeline editor
- Drag and drop models, configure data flows, and set up branching logic without writing orchestration code.
- Unified data routing
- Automatically handle data format conversion between models. Connect any output to any input across your pipeline.

From data to deployment — one platform.
Why Treni AI
Most ML teams juggle a dozen disconnected tools: one for data prep, another for training, a third for evaluation, and yet another for deployment. Treni AI replaces that fragmented stack with a unified platform where every step — from dataset curation to production serving — lives in one place.
We support both commercial models (OpenAI, Anthropic, Google) and open-source models (Llama, Mistral, Phi, Qwen) on the same platform. Run benchmarks across all of them, fine-tune the open-source ones, and deploy whichever performs best — without switching tools.
Built-in GPU management means you never have to SSH into machines, write SLURM scripts, or negotiate with cloud providers. Select your hardware tier, click deploy, and Treni AI handles provisioning, scaling, health checks, and cost optimization automatically.
The visual pipeline builder lets you design multi-tower architectures by connecting model blocks in a drag-and-drop canvas. Chain a time series forecaster into a transformer classifier, add an ensemble layer, and deploy the entire graph as a single endpoint.

Capabilities
Everything you need to train and deploy
No-Code Training
Launch fine-tuning jobs without writing training scripts. Configure everything through an intuitive UI.
Scale to Any GPU
From A100s to H100s, deploy on the hardware you need. Automatic provisioning and cluster management.
Fully Configurable
Customize hyperparameters, data splits, and model architectures. Full control over every aspect of your training pipeline.
Developer-first
Integrate with a few lines of code
JavaScript
// Fine-tune a model with Treni AI
const job = await treni.fineTune({
model: 'llama-3-70b',
dataset: 'my-dataset',
method: 'sft'
});Python
# Launch a training pipeline
from treni import Pipeline
pipeline = Pipeline()
pipeline.add_model('time-series-analyzer')
pipeline.add_model('transformer-classifier')
pipeline.run(dataset='my-data', gpus=4)Trusted by ML teams
What our users say
“Treni AI cut our fine-tuning iteration time from days to hours. The multi-tower pipeline feature is a game-changer for our ensemble models.”

Dr. Sarah Chen
ML Research Lead
“We benchmark across 15 models monthly. Treni AI's unified platform replaced our entire custom evaluation infrastructure.”

Marcus Rodriguez
Head of AI
Stop configuring. Start training.
While other platforms make you manage infrastructure, Treni AI lets you focus on what matters: your models and data. Launch your first training job in minutes.




