H2O AutoML vs Together AI

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	H2O AutoML Fine-tuning	Together AI Fine-tuning
Tagline	Open-source automated machine learning that handles feature engineering, model selection, and stacked ensembling out of the box.	Fine-tune & serve open-weight models (Llama, Mistral, DeepSeek).
Category	Fine-tuning	Fine-tuning
Pricing	Free· Free and open-source (Apache 2.0); paid Driverless AI sold separately	Paid· Pay-per-token; fine-tuning per-token
Model	H2O-3 (GBM, XGBoost, GLM, DRF, Deep Learning, Stacked Ensembles)	Llama / Mistral / Qwen / DeepSeek and others
Editorial score	—	8.6 / 10
Use cases	automltabular-mlmodel-ensemblinghyperparameter-tuningclassification-regression	open modelsfine-tuninginference
Pros	Fully open-source under Apache 2.0 with no usage limits Strong stacked-ensemble baselines with minimal code First-class R, Python, and GUI interfaces Scales from laptop to Hadoop/Spark/Kubernetes clusters MOJO/POJO export for low-latency production deployment	Wide open-model catalogue Competitive inference pricing Fine-tune + serve in one place Dedicated endpoints for production
Cons	Focused on tabular data, not LLMs or unstructured inputs JVM-based runtime can be heavy to operate Documentation assumes existing ML literacy	Latency varies by model Less polish than OpenAI
Website	h2o.ai	www.together.ai

Pick H2O AutoML if

Pick Together AI if