Seldon
Open-source MLOps framework for deploying and managing AI on Kubernetes.
About
Seldon Core is an open-source, Apache 2.0 MLOps framework for deploying, managing, and scaling machine learning models on Kubernetes. It supports multi-model serving, A/B testing, canary and shadow deployments, custom inference graphs, and drift monitoring, with a commercial Seldon Deploy product for enterprise governance.
The project remains actively maintained (last push March 2026) with roughly 4,700 GitHub stars, and it is a common choice for teams wanting Kubernetes-native inference without vendor lock-in.
It requires running your own Kubernetes cluster, so operational complexity is real, and it lacks native scale-to-zero without external configuration. For LLM-first workloads, purpose-built serving stacks like vLLM or KServe may be a better fit.
What it can do
- Kubernetes-native model deployment and autoscaling
- Multi-model serving and custom inference graphs
- A/B testing, canary, and shadow deployments
- Drift detection and model monitoring metrics
Setup
Install the Seldon Core operator via Helm on a Kubernetes cluster; see github.com/SeldonIO/seldon-core.
Prerequisites
A Kubernetes cluster (1.12+) and a packaged model or inference code.
Use cases
Works with
Trust score — 8/10
Scored 8/10 (verified tier, metrics-v1). Evidence: Verified via vendor site/docs/GitHub and reputable coverage, wave 72; Trending score 78/100 from researcher assessment.
★ 4,737 GitHub stars
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.