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Seldon Core

Free
mlopsmachine learningkubernetesopen sourcemodel deploymentmodel servingpythondata sciencedevopsai

An open-source, cloud-native MLOps framework for Kubernetes that empowers data scientists and DevOps engineers to deploy, scale, manage, and monitor machine learning models securely and efficiently in production.

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Seldon Core is a powerful open-source MLOps platform designed to run natively on Kubernetes. It provides the crucial infrastructure to deploy, scale, and manage thousands of machine learning models in production environments. Primarily serving machine learning engineers, data scientists, and DevOps teams, Seldon Core streamlines the complex journey from a trained model to a production-ready inference microservice. Its unique value proposition lies in its framework-agnostic nature, supporting models from any library, and its built-in advanced deployment capabilities like A/B testing, canary rollouts, and explainability. By offering a standardized, declarative API for ML deployments, it effectively bridges the gap between data science experimentation and robust production operations.

Pros

  • Completely open-source (Apache 2.0 license) and free to use, fostering strong community support.
  • Kubernetes-native design enables highly scalable and resilient ML model serving.
  • Framework-agnostic, supporting any ML library (TensorFlow, PyTorch, etc.) or programming language.
  • Built-in support for advanced deployment patterns like A/B testing, canary deployments, and multi-armed bandits.
  • Includes integrated tooling for model explainability (Alibi Explain) and outlier/drift detection (Alibi Detect).

Cons

  • Requires significant prerequisite knowledge of Kubernetes, presenting a steep learning curve.
  • Initial setup and configuration for complex inference graphs can be intricate.
  • Enterprise features like a UI, advanced governance, and dedicated support require upgrading to the paid Seldon Deploy product.
  • Community-based support model may not be sufficient for business-critical production issues.

Key features

  • Kubernetes Operator for ML Deployments
  • Complex Inference Graphs (Models, Routers, Combiners)
  • Advanced Deployment Strategies (A/B Tests, Canary Releases, Shadow Deployments)
  • Model Explainability (SHAP, LIME via Alibi Explain)
  • Outlier, Drift, and Adversarial Detection (via Alibi Detect)
  • Language & Framework Agnostic (Python, Java, R; TensorFlow, PyTorch, etc.)
  • Real-time Metrics for Monitoring (Prometheus Integration)
  • REST and gRPC API Endpoints

Integrations

KubernetesIstioAmbassador Edge StackKnativePrometheusGrafanaTensorFlowPyTorchScikit-learnJupyter

Target audience

Machine Learning Engineers, Data Scientists, and DevOps/Platform Engineers responsible for deploying and managing ML models in production Kubernetes environments.

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Founded

2014

Headquarters

London, UK

Pricing Tiers

Seldon Core

The complete open-source MLOps framework for Kubernetes. Includes model serving, inference graphs, advanced deployment patterns, explainability, outlier detection, and community support.

Free

Frequently Asked Questions

Top Alternatives to Seldon Core

KServe (formerly KFServing)

KServe is a strong alternative that is tightly integrated with the Kubeflow ecosystem, making it a natural choice for teams already invested in Kubeflow for other parts of their ML pipeline.

BentoML

BentoML offers a more developer-focused approach to packaging models into production-ready services, which can be simpler for use cases that do not require complex, Kubernetes-native inference graphs from the start.

NVIDIA Triton Inference Server

Triton excels at high-performance, low-latency inference, particularly on NVIDIA GPUs, making it ideal for organizations prioritizing raw model throughput over flexible deployment orchestration.

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