Alibaba Platform for AI (PAI)

Fully managed ML platform (formerly Amazon SageMaker) for building, training and deploying models: Studio notebooks, managed training jobs, HyperPod clusters for foundation-model training, hosted inference endpoints, Canvas AutoML and Feature Store.

End-to-end platform for building and deploying ML models with automated ML, designer (drag-and-drop), managed compute clusters, MLflow tracking, and responsible AI dashboards

Databricks' AI platform (built on the 2023 MosaicML acquisition) for building, fine-tuning, serving, evaluating and governing ML models, foundation models and AI agents on the lakehouse, including Model Serving, AI Search and Agent Bricks.

Hosted MLflow with managed tracking server, model registry, and deployment integration — open-source ML lifecycle tooling tightly coupled to Databricks Unity Catalog and Mosaic AI

Serverless real-time endpoints for custom ML models, agents and hosted foundation models (pay-per-token or provisioned throughput), with autoscaling, gateway guardrails and Unity Catalog governance.

AI platform for managing training and inference across on-premises, hybrid and cloud GPU environments, including air-gapped deployments, with JupyterLab, Slurm and MLflow tooling

Google Cloud's AI development platform (formerly Vertex AI): Model Garden access to Gemini and 200+ models, Agent Studio and ADK for building agents, Agent Runtime, managed training and tuning on TPUs/GPUs, pipelines, model registry and Vector Search.

End-to-end AI development platform with AutoML, data labelling, distributed training on Ascend and GPU clusters, and one-click deployment to cloud or edge

Enterprise AI studio for training, validating, tuning, and deploying foundation models and traditional ML models, with IBM's Granite model family, Hugging Face integration, prompt lab, synthetic data generation, and governance via watsonx.governance

Self-managed AI/ML platform for OpenShift (formerly Red Hat OpenShift Data Science), built on the upstream Open Data Hub project, with workbenches, pipelines, model registry and KServe model serving; the managed Cloud Service edition ended in October 2025

Fully managed machine learning platform with JupyterLab notebooks, conda-environment library, job orchestration, model deployments as HTTPS endpoints, feature store, and model catalog — integrated with Autonomous Database and Object Storage for end-to-end ML workflows

AI development platform covering data preparation, Jupyter notebooks, CPU/GPU training (including automated ExeML) and deployment of models as online APIs, batch jobs or edge services

Managed GPU-backed training jobs for custom ML models, with H100 / H200 / L40s / L4 GPU options, distributed-training orchestration, and Jupyter integration

Managed serverless deployment of containerised AI/ML inference workloads with autoscaling and per-second billing, for hosting custom models built outside the LLM-focused AI Endpoints product

Managed Jupyter and VS Code notebooks with dedicated CPU or GPU resources, pre-installed frameworks (PyTorch, TensorFlow, Scikit-learn, Hugging Face) and per-minute billing.

Integrated machine-learning platform inside Snowflake — feature store, model registry, distributed training on warehouses or the container runtime, model serving and ML observability.