Fine-tune a modelnative to your product

FlexAI keeps the workflow predictable so you can focus on results, not infrastructure.

Improve your agent's domain behavior, tool-use reliability, and response style with fine-tunes that deploy back to the same inference path. Serve them on dedicated endpoints or Token Factory on the same key.

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Results in production

What teams achieve fine-tuning on FlexAI.

75%

Lower compute cost

LegML's fine-tune beat a flagship at a fraction of the cost.

LegML

€22.5K

Total training cost

To fine-tune a 32B legal LLM end to end on FlexAI H100.

LegML

<24h

To first production inference deploy

Pixelcut went from first API call to production inference.

Pixelcut

Bring your data

Connect your datasets from cloud storage or upload directly.

Pick a checkpoint

Start from a foundation model or continue from a previous run.

Run a job you can trust

Managed execution with retries, checkpoints, and monitoring built in.

Ship a model that meets your customer needs

Deploy directly or export for evaluation and iteration.

Fine Tuning Execution

Choose the fine tuning approach that fits your goal

Optimize for accuracy, simplify execution, or let the platform orchestrate everything.

Model quality first

Quality focused

Optimize for accuracy, precision, and F1 score
Evaluate, iterate, and optimize faster using proven fine tuning recipes
Support for domain and task specific adaptation and optimization
Automatic evaluation pipelines with metric tracking

Tip: Start with accuracy to validate quality. Use ease for multi cloud execution. Scale with orchestration for production jobs.

LoRA Fine Tuning

Parameter efficient. Fast iteration.

Train a fraction of parameters with low rank adapters

Faster training cycles with lower GPU memory requirements

Stack and swap adapters without retraining the base model

Ideal for task specific adaptation and rapid experimentation

Full Fine Tuning

Maximum control. Deep adaptation.

Update all model weights for maximum domain alignment

Multi GPU parallelized execution with stable synchronization

Managed checkpoints with automatic versioning and rollback

Best for foundational shifts in model behavior and knowledge

Domain specific fine tuning at scale

Customize models for real world domains using optimized fine tuning workflows designed for accuracy, stability, and repeatability across long running jobs.

Fine tune with confidence across multimodal models (audio, text, vision, image) without infrastructure management.

See it in practice: LegML fine-tuned a 32B legal LLM that outperformed a frontier model with 50% fewer parameters. read all the case studies.