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Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Deploy modular, data-centric AI applications at scale on Kubernetes
Seldon Core 2 provides a Kubernetes-native MLOps and LLMOps platform for deploying, managing, and scaling modular AI applications, supporting pipelines, autoscaling, multi-model serving, and experiments.
Seldon Core 2 is a Kubernetes‑native framework that enables data‑centric AI teams to package, deploy, and operate both machine‑learning and large‑language‑model workloads at production scale. It abstracts the complexities of Kubernetes while giving fine‑grained control over model serving, routing, and resource management.
The platform supports composable pipelines that stream data via Kafka, automatic scaling of models and custom components, and multi‑model serving that consolidates many models onto shared inference servers. Experiments let you run A/B tests, shadow deployments, and drift detection without disrupting live traffic. Overcommit functionality further reduces infrastructure spend by allowing more models than physical memory would normally permit.
Deployments are defined declaratively and can run on‑premises or in any cloud that hosts a Kubernetes cluster. Seldon Core 2 integrates with existing CI/CD pipelines and provides out‑of‑the‑box monitoring and performance tuning tools, making it suitable for enterprises seeking a production‑ready MLOps solution.
When teams consider Seldon Core 2, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Real‑time fraud detection pipeline
Stream transaction data through Kafka‑linked models to flag anomalies instantly while auto‑scaling under load.
Recommendation model A/B testing
Route live traffic between candidate models, collect performance metrics, and promote the best performer without downtime.
Scalable LLM chat service with drift detection
Deploy LLMs alongside custom components that monitor response quality and trigger fallback models when drift is detected.
Consolidated IoT inference for edge devices
Host dozens of lightweight models on shared servers, using overcommit to maximize hardware utilization across edge workloads.
Seldon Core 2 supports Kubernetes 1.21 and newer; consult the documentation for specific version compatibility.
Autoscaling can be driven by native metrics (CPU, memory) or custom logic defined in Seldon resources.
Yes, the platform is cloud‑agnostic and runs on any on‑prem Kubernetes cluster.
The project is distributed under the Business Source License; contributions inherit the same license.
Implement a Dockerized service that conforms to Seldon's component API and reference it in your pipeline definition.
Project at a glance
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