The Best Flama Alternatives in 2026

BentoML helps build and serve AI apps and models through inference APIs, job queues, and multi-model pipelines.

Merlin supports machine learning model management, deployment, and serving in Kubernetes environments.

Mosec serves machine learning models with dynamic batching and CPU or GPU pipelines for inference workloads.

ClearML combines experiment and data management, pipelines, scheduling, orchestration, and model serving for AI workloads.

ClearML manages AI experiments, data, pipelines, orchestration, scheduling, and model serving in one MLOps platform.

Flexible, high-performance serving system for machine learning models.

Sagify describes its project as a way to work with large language models and machine learning more easily.

ORMB packages machine learning models as OCI artifacts, in a workflow described as Docker for ML models.

Cog packages machine learning models into containers to support deployment across environments.

DJL is a Java framework for running deep learning models across different engines.

PaddlePaddle provides a deep learning framework for training models across devices and platforms.

Xinference runs language, speech, and multimodal models through a unified inference API across cloud, on premises, or laptop setups.

ModelDB tracks machine learning model versions, metadata, and experiments.

Torchvision provides datasets, transforms, and models for computer vision with PyTorch.

The cONNXr project provides a pure C runtime for ONNX models intended for embedded devices.

Train and run models with OneFlow, a scalable deep learning framework for distributed machine learning workloads.

Kubeflow Pipelines orchestrates and manages machine learning workflows on Kubeflow.

Catniff provides JavaScript developers with a Torch-like deep learning framework.

TPOT uses genetic programming to optimize machine learning pipelines in Python.

The ChefBoost Python framework supports decision trees such as ID3, C4.5, CART, CHAID, regression trees, and ensemble methods.

Rockpool supports training spiking neural networks with PyTorch and JAX and deploying them to neuromorphic hardware.

QKeras provides quantization tools for deep learning with TensorFlow Keras.

The Datumbox framework is written in Java for developing machine learning and statistical applications.

Build neural networks and support machine learning research with Sonnet, a library based on TensorFlow.