Setting the new standard for ML ops

No more deployment bottlenecks

Our global community is at the core of everything that we do: helping us to shape, fix and refine our projects to deliver benefits for all of our users.

There are hundreds of engineers in our Slack community and on Github to help you get started. And when your business wants the added assurance of Seldon engineers to assist with production deployments, we provide support plans and managed services that perfectly complement your project requirements and data science team workflows.

Agnostic & independent

Runs any framework across clouds and on-premises

Inference graphs

Save R&D time managing complex model deployments


Enable better versioning and reproducibility

Designed to fit the way you work

Data scientists
Seldon liberates data scientists by reducing the amount of tasks that require support from DevOps teams and the time to operationalize models. This enables your data scientists to focus on building better models, to iterate faster and optimize based on real-world KPIs.
DevOps teams hold the keys to production and are responsible for maintaining a scalable and secure infrastructure. Seldon gives your teams new machine learning superpowers based on rock-solid, cloud-native technologies, so they can focus on supporting production workflows and iterate at the pace that modern data science teams require to succeed.
Machine learning is the ultimate opportunity to revolutionise business. Managers are under increasing pressure to identify high value use cases, and move rapidly to production where KPIs can be evaluated and improved. Seldon empowers you by providing clearer insights and streamlining workflows between your data science and devops team.


Fees are based on your unique requirements, we’ll send a proposal following a pre-sales tech call or meeting.


Move from R&D into production

  • Seldon Core managed service
  • Up to 5 live deployments
  • Continuous Deployment (CD) of your models

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Scaleup and streamline your ML pipeline

  • Up to 20 live deployments
  • Fixed CI/CD pipelines to train and deploy yourmodels from popular ML toolkits
  • Model inference optimisation
  • Quarterly customer success check-ins

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Bespoke integration running at scale

  • Unlimited live deployments
  • Kubeflow CI/CD
  • Bespoke wrappers for proprietary models
  • Custom metrics
  • Monthly customer success check-ins

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