#1 AIFree
sktime — alternative #1 to cuML
Unified framework for machine learning with time series. scikit-learn compatible API for forecasting, classification, clustering, and anomal...
GitHub source
Recorded alternatives
Alternatives to cuML
Every candidate below sits in AI. What ends up on this page is decided entirely by a recorded score — not by bidding, not by editor taste. Review both records, check current vendor terms, then decide.
#2 AIFree
Featuretools — alternative #2 to cuML
Open-source Python library for automated feature engineering. Transforms transactional and relational datasets into feature matrices for mac...
GitHub source
#3 AIFree
MLJ.jl — alternative #3 to cuML
Comprehensive Julia machine learning framework providing a unified interface to 200+ models with meta-algorithms for selection, tuning, and...
GitHub source
#4 AIFree
NVIDIA KAI Scheduler — alternative #4 to cuML
Kubernetes-native GPU scheduler for AI workloads at large scale. Originally developed by Run:ai, now open-sourced by NVIDIA. Optimizes GPU r...
GitHub source
#5 AIFree
OpenMLDB — alternative #5 to cuML
Open-source machine learning database providing a feature platform for consistent features between training and inference. Real-time relatio...
GitHub source
#6 AIFree
Polyaxon — alternative #6 to cuML
MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle. Reproducible and scalable machine learning workflows on Kubernetes...
GitHub source
#7 AIFree
SynapseML — alternative #7 to cuML
Distributed machine learning on Apache Spark. Scalable, composable APIs for text analytics, vision, anomaly detection with seamless Python/S...
GitHub source
#8 AIFree
HAMi — alternative #8 to cuML
Heterogeneous GPU Sharing on Kubernetes. CNCF sandbox project providing GPU virtualization, slicing, and scheduling for efficient AI workloa...
GitHub source
Questions about this page
How were these alternatives selected?
Each candidate is a live catalogue record in the same primary category, picked by a deterministic score that Oryxen records in its catalogue: how many meaningful words the two listings share, whether their pricing classes oppose (a paid product against a free one), whether both are open source, and whether either record is featured. The same catalogue state always produces the same list — no randomness, no manual ranking.
Are these alternatives ranked by payment?
No. Rows are ordered only by the recorded score, highest first, with alphabetical ties — payment never influences the order, nothing here is paid placement, and Oryxen charges no listing or placement fee.
Always verify current terms with cuML directly — Oryxen records pricing classes, not live quotes.