Amazon SageMaker
Fully managed machine learning service to build, train, and deploy ML models at scale
Seldon Core 2
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.
Overview
Overview
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.
Capabilities
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.
Deployment
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.
Highlights
Composable pipelines with Kafka‑based real‑time streaming
Native and custom autoscaling for models and components
Multi‑model serving to consolidate inference workloads
Experiment routing for A/B tests, shadow deployments, and drift detection
Pros
- Kubernetes‑native scalability and reliability
- Modular architecture enables flexible AI application design
- Cost‑saving overcommit and shared inference servers
- Built‑in experiment management for rapid model iteration
Considerations
- Requires operational expertise with Kubernetes
- Business Source License may limit unrestricted commercial redistribution
- Complexity when building custom components or pipelines
- Steeper learning curve compared to lightweight single‑model servers
Managed products teams compare with
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.
Fit guide
Great for
- Enterprises needing production‑grade model serving at scale
- Teams building data‑centric AI pipelines with real‑time requirements
- Organizations that run many models and want to share infrastructure
- Developers experimenting with A/B testing and LLM integrations
Not ideal when
- Small projects without access to a Kubernetes cluster
- Users requiring a permissive open‑source license for redistribution
- Teams lacking ops resources to manage cluster lifecycle
- Simple single‑model deployments where a lightweight server suffices
How teams use it
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.
Tech snapshot
Go54%
Jupyter Notebook21%
Kotlin14%
JavaScript3%
Smarty3%
Java2%
Frequently asked questions
What Kubernetes version is required?
Seldon Core 2 supports Kubernetes 1.21 and newer; consult the documentation for specific version compatibility.
How does autoscaling work?
Autoscaling can be driven by native metrics (CPU, memory) or custom logic defined in Seldon resources.
Can I run Seldon Core 2 on‑premises?
Yes, the platform is cloud‑agnostic and runs on any on‑prem Kubernetes cluster.
What are the licensing terms?
The project is distributed under the Business Source License; contributions inherit the same license.
How do I add custom components?
Implement a Dockerized service that conforms to Seldon's component API and reference it in your pipeline definition.
Project at a glance
Active- Stars
- 4,710
- Watchers
- 4,710
- Forks
- 857
Repo age8 years old
Last commit2 days ago
Primary languageGo
Last synced 12 hours ago