Infrastructure for AI, ML & Data
For developers managing AI, ML, and data workflows in production, the challenges extend well beyond scheduling and orchestrating DAGs. Union.ai addresses these complexities by offering a comprehensive infrastructure management platform designed for the nuances of such environments.
Union optimizes resources across teams and implements cost-effective strategies that can reduce expenses by up to 66%. Moreover, it's engineered to fit within your own cloud ecosystem, ensuring a robust and tailored infrastructure that scales with your technical demands.
Powerful DAGs, observability & cost-efficient engineering
Union is a fully-managed Flyte platform deployed in your VPC that provides a single-endpoint workflow orchestration and compute service to engineers building data and ML products.
Get built-in dashboards, live-logging, and task-level resource monitoring, enabling users to identify resource bottlenecks and simplifying the debugging process, resulting in optimized infrastructure and faster experimentation.
AI engineering for engineers
Union is an open AI orchestration platform that simplifies AI infrastructure so you can develop, deploy, and innovate faster. Unlike popular—but simple—AI engineering orchestrators, Union wrangles the infrastructure setup and management as well.
Write your code in Python, collaborate across departments, and enjoy full reproducibility and auditability. Union lets you focus on what matters.
Purpose-built for lineage-aware pipeline orchestration
Bring your own Airflow code (BYOAC) and take advantage of modern AI orchestration features—out of the box! Get full reproducibility, audibility, experiment tracking, cross-team task sharing, compile-time error checking, and automatic artifact capture.
Easily experiment and iterate in isolation with versioned tasks and workflows.
A centralized infrastructure for your team and organization, enables multiple users to share the same platform while maintaining their own distinct data and configurations.
Strongly typed inputs and outputs can simplify data validation and highlight incompatibilities between tasks making it easier to identify and troubleshoot errors before launching the workflow.
Caching the output of task executions can accelerate subsequent executions and prevent wasted resources.
As a data-aware platform, it can simplify rollbacks and error tracking.
Immutable executions help ensure reproducibility by preventing any changes to the state of an execution.
Rerun only failed tasks in a workflow to save time, resources, and more easily debug.
Enable human intervention to supervise, tune and test workflows - resulting in improved accuracy and safety.
We manage the infrastructure so you can build what matters
Union is the AI orchestration and infrastructure platform of choice for many top data and ML teams globally. Esteemed companies such as Woven Planet and AbCellera have transitioned their workflows from Airflow or Kubeflow to Union.
Union is up to 66 percent more cost-efficient with your compute resources, solves complex infrastructure challenges, and is built for rapid iteration across teams.
Globally trusted & tested
Join our developer community
“We got over 66% reduction in orchestration code when we moved to Flyte™ — a huge win!”
“With Flyte™, we want to give the power back to biologists. We want to stand up something that they can play around with different parameters for their models because not every … parameter is fixed. We want to make sure we are giving them the power to run the analyses.”
“To my great surprise, the migration to Flyte™ was as smooth and easy as the development of our initial active learning pipeline in Airflow had been painful: It literally took just a few weeks to revamp our platform’s main pipeline entirely, to the delight of users and developers alike.”
“Because we are a spot-based company, a lot of our workflows run into the majority of issues. Thankfully, with Flyte™, we can debug and do quick iterations.”
“It’s not an understatement to say that Flyte™ is really a workhorse at Freenome!”
“We're going to have 10,000-plus CPUs that we plan to use every day to process the raw data. There'll be 30 different targets approximately that we're collecting data on every day. That's about 200 GB of raw data and probably 2 TB or so on the output — a lot of data process. We're leaning heavily on Flyte™ to make that happen.”
“The multi-tenancy that Flyte™ provides is obviously important in regulated spaces where you need to separate users and resources and things like amongst each other within the same organization.”
“We’ve migrated about 50% of all training pipelines over to Flyte™ from Kubeflow. In several cases, we saw an 80% reduction in boilerplate between workflows and tasks vs. the Kubeflow pipeline and components. Overall, Flyte™ is a far simpler system to reason about with respect to how the code actually executes, and it’s more self-serve for our research team to handle.”
“Flyte™’s scalability, data lineage, and caching capabilities enable us to train hundreds of models on petabytes of geospatial data, giving us an edge in our business.”