MLOps & experiment tracking · Seldon Technologies Ltd. (acquired by TrueFoundry, June 2026)

Seldon

Kubernetes-native model deployment and serving platform for putting trained ML models into production.

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Seldon (Seldon Core) is a model-deployment and serving tool for Kubernetes, not an experiment tracker or a standalone monitoring product: it wraps trained models in standardized inference servers and manages rollout patterns such as canary releases, A/B tests, and multi-armed-bandit routing between model versions. It supports common ML and deep-learning frameworks through prebuilt or custom inference servers and integrates with separate monitoring/explainability libraries (Alibi Detect, Alibi Explain) that the company also maintains. Seldon Core moved from Apache-2.0 to the Business Source License in 2024, though the lighter-weight MLServer component remains Apache-2.0; it is deployed self-hosted on a Kubernetes cluster or as a managed/VPC offering through enterprise contracts. Seldon Technologies was acquired by TrueFoundry in June 2026, which plans to fold Seldon Core into its own deployment and observability platform.

At a glance

Vendor Seldon Technologies Ltd. (acquired by TrueFoundry, June 2026)
Pricing model Quote only
Free tier —
Deployment Cloud, Self-hosted
Open source No (BSL-1.1)
Best for Platform teams deploying and rolling out models on Kubernetes who need built-in canary/A-B routing.

Pricing

Seldon ML Server remains open source and free; commercial Seldon Core / Core+ deployments are sold through custom, sales-negotiated contracts with no published self-serve pricing found on the vendor's site.

Pricing has not been verified yet — see the vendor's site.

Features

  • Kubernetes-native model deployment and serving
  • Canary, shadow, and A/B rollout patterns
  • Multi-armed-bandit model routing
  • Prebuilt inference servers for common frameworks
  • Custom inference server support
  • Integration with Alibi Detect for drift/outlier detection
  • Integration with Alibi Explain for model explainability

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

BentoML Open-source framework for packaging and serving ML models as production inference APIs, with a paid managed cloud option. Kubeflow Open-source, Kubernetes-native toolkit for orchestrating ML pipelines, notebooks, and training jobs.

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Terms to know

Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Data governance The policies, roles and processes that determine how data is defined, owned, accessed and kept compliant. Anomaly detection Identifying data points or patterns that deviate significantly from expected or normal behavior. MLOps (machine learning operations) The set of practices for deploying, monitoring and maintaining machine learning models reliably in production, extending DevOps to ML systems.

Related guides

How to choose an MLOps & experiment tracking tool "MLOps" covers five different jobs — tracking, monitoring, orchestration, serving, versioning — and most teams need more than one, not the biggest single tool.