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High‑performance serving system for TensorFlow models (and other formats) with gRPC/REST APIs, versioning, and batching support.
#3 TorchServe
Official model serving framework for PyTorch, offering multi‑model hosting, logging, metrics, and easy scaling on Kubernetes.
Kubernetes‑native serverless inference platform supporting TensorFlow, PyTorch, ONNX, XGBoost, and custom models with canary rollouts.
#5 BentoML
Open‑source framework to package ML models as prediction services, with Docker, Kubernetes, and serverless deployment options.
#6 MLflow
End‑to‑end open‑source platform for experiment tracking, model packaging, and serving via REST API or Azure/AWS integrations.
Production‑scale ML pipeline framework that includes components for data validation, model training, and serving with TensorFlow Serving.
Microsoft’s cloud MLOps suite offering automated model deployment, scaling, monitoring, and CI/CD pipelines on Azure Kubernetes Service.
Fully managed service for building, training, and deploying models at scale with built‑in A/B testing, model monitoring, and serverless endpoints.
#10 Google Vertex AI
Google Cloud’s unified AI platform that handles model training, deployment, feature store, and continuous monitoring.
#11 Algorithmia
Enterprise model serving marketplace that lets you deploy, version, and monetize algorithms with auto‑scaling and monitoring.
#12 DataRobot
Automated machine‑learning platform with MLOps capabilities for model deployment, governance, and real‑time monitoring.
#13 H2O.ai MLOps
Open‑source and enterprise tools for model training, deployment, and lifecycle management with auto‑ML and model interpretability.
Kubernetes‑native workflow engine for building, deploying, and managing end‑to‑end ML pipelines, including model serving via KServe.
#15 Valohai
MLOps platform that automates model training, versioning, and deployment on any cloud or on‑premise Kubernetes cluster.
IBM’s cloud service for training, deploying, and managing AI models with built‑in governance, drift detection, and scaling.
#17 Flyte
Open‑source workflow automation platform for large‑scale ML pipelines, supporting model registration and serving via custom plugins.
#18 Cortex
Open‑source platform that turns any Docker container into a production‑grade, autoscaling API for ML models.
#19 Clipper
Low‑latency prediction serving system that abstracts away model framework differences, enabling unified APIs and batch inference.
#20 Weights & Biases
Experiment tracking and model registry tool that integrates with CI/CD pipelines for automated deployment and monitoring.
While Seldon itself is the reference, many users adopt community‑maintained Helm charts, custom operators, or lightweight forks to meet specific needs.