Platform deployment

The Union.ai platform uses a split-plane model with separate control and data planes.

In both BYOC and Self-managed deployments, your code, input and output data, container images and logs reside entirely on the data plane, which runs in your cloud account, while the control plane runs on Union.ai’s cloud account, providing the workflow orchestration logic.

The control plane does not have access to the code, data, images, or logs in the data plane.

If you choose a Self-managed deployment, your data isolation is further enhanced by the fact that you manage your data plane entirely on your own, without providing any access to Union.ai customer support.

If you choose a BYOC deployment, Union.ai manages the Kubernetes cluster in your data plane for you. The data isolation of the control vs. data plane is still enforced - for example, Union.ai has no access to your object storage or logs. However, Union.ai customer support will have some access to your cluster, though strictly for upgrades, provisioning, and other actions related to maintaining cluster health.

These are the BYOC docs. You can switch to the Union.ai Self-managed docs with the product selector above.

BYOC deployment

The BYOC deployment offers a fully “serverless in your cloud”, turnkey solution where all infrastructure management is offloaded to Union.ai:

  • The data plane resides in your cloud provider account but is managed by Union.ai, who will handle deployment, monitoring, Kubernetes upgrades, and all other operational aspects of the platform. BYOC deployment supports data planes on Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure.

  • The control plane, as with all Union.ai deployment options, resides in the Union.ai AWS account and is administered by Union.ai. However, as mentioned, data separation is maintained between the data plane and the control plane, with no control plane access to the code, input/output, images or logs in the data plane.

Data plane

The data plane runs in your cloud account and VPC. It is composed of the required services to run and monitor workflows:

  • Kubernetes cluster
  • Object storage bucket
  • Container image registry
  • Secrets manager
  • Logging solution
  • IAM role with proper access

When you run your workflow:

  1. Your code is sent to the object storage bucket
  2. Container images are built on a builder node and pushed to the registry
  3. Pods are created and assume the IAM role
  4. Container images are pulled down from the registry for each pod as needed
  5. Containers load their inputs from, and save their outputs to, the object store

All of this happens in the data plane, with the control plane aware only of the workflow execution state, and not the code, data, logs, secrets, or any other proprietary information. The data plane communicates with the control plane through an outgoing port through a zero trust proxy. There is no open incoming port to the data plane.

Control plane

Union.ai operates the control plane in its own cloud infrastructure in Amazon Web Services (AWS). The control plane has access to:

  • Workflow execution state information
  • Names of tasks and other deployed entities
  • Pointers to object storage locations in the data plane (but not any user data)
  • Union.ai IDP