Python-native workflow orchestration
Build dynamic workflows in Python with runtime logic, conditions, retries, and fanout, so complex orchestration stays in code instead of a visual glue layer.
AI runtime for durable workflows and real-time apps in your cloud
Union.ai is an AI runtime for orchestrating durable workflows, real-time applications, and compute optimization in a customer-managed cloud environment. The product is positioned around running in your infrastructure rather than Union.ai’s, with orchestration metadata handled by the service and workflow execution staying in your cloud.
The platform is built for teams that want to write dynamic AI and ML workflows in Python, recover from interruptions, and manage scaling across clusters and regions. The pricing page also shows a Team plan with monthly credits and a custom Enterprise plan, while the FAQ and plan comparison point to options for bring-your-own-cloud and self-hosted deployment in Enterprise.
Build dynamic workflows in Python with runtime logic, conditions, retries, and fanout, so complex orchestration stays in code instead of a visual glue layer.
Run workflows and real-time apps in your own cloud while keeping execution data, logs, and secrets inside your infrastructure.
Reuse containers across short-lived tasks to reduce startup overhead and make fine-grained execution practical at scale.
Automatically recover interrupted workflows and continue from where they left off, which is important for long-running or compute-heavy jobs.
Track and optimize compute use with action-based pricing, resource reporting, and cost-aware workflow execution.
Serve real-time applications with Union handling authentication, authorization, auto-scaling, scale-to-zero behavior, and monitoring.
Orchestrate long-running AI or ML pipelines that need branching logic, retries, and resumable execution without shifting data out of your cloud.
Build real-time applications that need orchestration plus scaling, authentication, and monitoring in the same platform.
Run compute-heavy jobs with many short-lived tasks and reuse containers to reduce startup overhead during fanout-heavy workloads.
Deploy in regulated or security-sensitive environments where data must stay in your infrastructure, including self-hosted or airgapped setups on Enterprise.
Track spend on actions and allocated resources while tuning workflows to avoid idle capacity and manage compute more predictably.
Union.ai uses monthly credits on the Team plan. The monthly fee is issued back as usage credits, so the plan fee becomes the minimum monthly spend and any usage above that is billed separately.
Yes. The pricing page says Union.ai supports bring-your-own-cloud deployments, including AWS, GCP, Azure, and neo-cloud environments.
Yes. Union.ai says self-hosted control plane deployment is available on an Enterprise plan, including on-prem, hybrid, and airgapped configurations.
Union.ai says no sensitive data transits its infrastructure. Workflow inputs, outputs, logs, and code bundles stay in your cloud and are served directly from inside your own infrastructure through a secure tunnel.
The pricing page states that the Team plan starts at $950 per month plus usage, and Enterprise is custom.
Traffic data is for reference only.
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