Kubernetes-native Data & ML Workflows at Scale

As a managed Flyte™ solution, Union empowers data and ML teams to concentrate on ML production. It eliminates infrastructure constraints and complex setup processes, streamlining their workflow for 10x productivity.

Snowflake, SQAlchemy, DoltHub, BigQuery, Hive and DuckDBDatabricks, AWS Athena and Apache SparkPandera and Great ExpectationsVaex, Polars and FlyteKuneflow, Dask, Ray, Dask, HuggingFace, DeepSpeed, UnionML, AWS Sagemaker and JaxW&B and WhylogsOnnx, BentoML, AWS Sagemaker and BananaWhylogs and MLFlowKuneflow, Dask, Ray, Dask, HuggingFace, DeepSpeed, AWS Sagemaker and Jax

Overcome the complex challenges of building scalable ML products

Union accelerates data processing and machine learning for businesses in every industry. It's built on the trusted open-source project Flyte™, and combines the power and efficiency of Kubernetes with enhanced observability and enterprise features, all fully managed in your cloud account. Data and ML teams can more easily collaborate on optimized infrastructure, boosting their velocity.

Accelerate Experimentation

Break down siloed teams & infrastructure

When data and ML teams work with distributed tooling and infrastructure, communication and collaboration can become difficult. Siloed teams use different tools, processes, data formats, and infrastructure, which can lead to delays, errors, or even scrapping projects due to a lack of alignment.

With Union, simplify the process of sharing work across teams and environments with reusable tasks, versioned workflows, and an extensible plugin system.

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Any Cloud

Infrastructure doesn’t have to be difficult

On-prem, hybrid cloud, multi-cloud, multi-region, the options today are endless for choosing the right infrastructure for your projects. These choices offer flexibility to users, but the use of multiple clouds can lead to issues with data consistency, networking, security, and service integrations. This can result in the failure of ML projects and the breakage of infrastructure and applications.

With Union, it is simple to consume resources and services across clouds in one unified platform.

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Increased Control

Cost optimization for complex workflows

When infrastructure becomes distributed across different providers and instances, it can be a daunting task to track and forecast usage and spend. It is all too normal to see this lack of visibility leading to tremendous compute costs from underutilized resources with little understanding of when and how it happened.

Union provides real-time visibility and monitoring at the workflow and task level as well as a resource dashboard that includes all of your projects.

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Increase control & oversight across teams and projects

Without proper governance, teams might not adhere to consistent standards or regulatory requirements for data management, model development, testing or deployment.

With Union, you can simplify the management and security of your platform through enterprise-grade Role-Based Access Control—allowing you to scope individual users to view and execute on specific Projects and Workflows.

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Maximize your ML & data pipelines’ performance

Union gives your teams end-to-end transparency and control to get the most from your resources and eliminate barriers to productivity.

Faster time-to-market

In today’s fast-paced business environment, the ability to quickly develop and deploy machine learning models can be the difference between success and failure.

Union helps businesses accelerate their ML projects by automating many of the processes involved in model development and deployment, reducing the time and effort required to get models into production.

View Union features

Scalable ML workflows

Scaling machine learning efforts can be challenging due to the need for specialized infrastructure, in-house expertise in distributed systems management, and tools to handle large-scale data processing and model training.

Union enables reproducibility, observability at the workflow, task, and data level, and provides plugins for model deployment and distributed model training tools and frameworks.

Read MethaneSAT case study

Reduce ML technical debt

Without standardized operations and processes in place, many teams struggle to promote models to production resulting in sunk costs and wasted compute resources.

Union enables more efficient and accurate workflows through automated validation and optimization throughout the development and deployment process.

Read ML use case

Integrate with existing tooling

Whether you are working with ML frameworks like TensorFlow and PyTorch, or using tools like Jupyter notebooks and Apache Spark, Union is designed with an extensible plugin system that spans both data science and infrastructure stacks.

This allows users to leverage the power of a managed platform without disrupting existing processes.,

View Union integrations

Get started with Union

Union is immediately available for AWS and GCP. Union offers free trials for qualified users. To get started with Union check out the documentation or sign up to be an early user below.

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An open platform for your entire team & stacks

Union is designed for data and ML teams who don’t want the overhead of maintaining and managing Flyte™ deployments, setting up Kubernetes infrastructures, and provisioning security and data policies.

Data Engineer

for Data Engineers

Union, as a data orchestrator, empowers DataOps and data engineering in a modern data stack by providing advanced automation capabilities. Data and analytics professionals can leverage Union to create, deploy, and run fully automated and reproducible end-to-end data pipelines.

“Union runs my data pipelines and even connects with Apache Airflow.”

Data Scientist

for Data Scientists

Data science teams benefit from Union’s data science capabilities, which enable efficient data science workflows. With advanced automation features, Union allows data scientists to create, deploy, and execute end-to-end pipelines that are fully automated and reproducible.

“From my research work to effective collaboration on one platform.”

ML Engineer

for ML Engineers & MLOps

ML engineers need a streamlined and scalable approach to their machine learning workflows. With its comprehensive set of tools and efficient design, Union enables ML engineers to easily build, deploy, and manage complex workflows, accelerating their development and delivering more robust and accurate models.

“Deploying and monitoring all my workflows around the clock.”


for DevOps & Engineering

DevOps and engineers are responsible for managing a wide range of tools, frameworks, and services to build and maintain end-to-end data ecosystems. This can be a complex and time-consuming task, requiring significant expertise and resources. An orchestrator like Union greatly simplifies this process by providing a unified platform for managing and automating data and ML workflows, enabling DevOps and engineers to streamline their workflows and focus on delivering more value to their organization.

“Running our Kubernetes cluster while using Union to manage our data plane.”

Union for orchestration 1, 2, 3


Supercharge your code

Turn your ETL and ML code into scalable tasks and workflows.

Move the slider to see the Flyte™ Decorators that identify your code as tasks and workflows.


Get ready to execute

Union is an out-of-the-box experience for machine learning engineers and data scientists who need to deliver at scale and often without any help from IT or infrastructure teams.


Observe & optimize

As your workflows become more complex, it becomes increasingly important to gain deeper insights into their performance.

Union’s built-in dashboards, logging, and task-level resource monitoring enables users to identify resource bottlenecks, long execution times, and simplify the debugging process, resulting in optimized resources and faster experimentation.

Data, ML, research & production

These fine companies among many others create data and ML products with Union’s Flyte™ engine.