Enterprise-Grade A Orchestration: Powering Hybrid Cloud with Union.ai and Koyeb

The Evolution of AI Infrastructure Needs

Over the past decade, AI infrastructure has evolved dramatically. We’ve moved from static ETL pipelines to dynamic machine learning workflows, and now to intelligent, autonomous agents. 

As AI systems grow more complex and compute-intensive, the need for flexible, scalable, and reliable orchestration has become mission-critical.

unTraditional tools fall short in hybrid environments where compute needs shift rapidly, data governance is non-negotiable, and real-time decision-making is required. That’s where Union.ai and Koyeb come in. Check out Union Self Managed Deployment.

Why This Partnership Matters

Koyeb is a modern serverless platform that offers multi-cloud flexibility, specialized hardware support, and cost-effective, high-performance compute for AI workloads. It’s designed for developers who want to deploy and scale AI applications without managing infrastructure.

Union.ai, built on the open-source Flyte project, provides a powerful orchestration layer for managing complex AI workflows from distributed training to evaluation, deployment, and monitoring. And new dynamic features with Flyte 2.0.

Together, Union.ai and Koyeb deliver a hybrid cloud AI solution that enables enterprises to orchestrate, scale, and deploy AI systems across any environment—on-prem, GPU cloud, or serverless edge.

Hybrid Cloud Flexibility for the Enterprise

For organizations with strict data governance, complex infrastructure, or demanding security requirements, Union.ai and Koyeb enable a true hybrid cloud strategy.

You can:

• Train models on-prem or in a specialized GPU cloud
• Deploy inference endpoints to Koyeb’s serverless platform
• Orchestrate the entire lifecycle with Union.ai

This approach delivers:

• Enhanced Security & Control: Keep sensitive data within your environment while orchestrating workflows across trusted infrastructure.
• Cost Optimization: Leverage existing infrastructure and avoid vendor lock-in by dynamically choosing the most cost-effective compute.
• Scalability on Demand: Burst to Koyeb’s serverless platform for peak workloads or access to specialized hardware when needed.
• Unified Management: Use Union.ai to orchestrate and monitor workflows across environments with full observability and control.

Example Use Case: From Training to Production

Let’s walk through a real-world hybrid AI workflow:

1. Orchestrate with Union.ai

Define your ML pipeline using Union: training, evaluation, and deployment steps are modular and reproducible.

@workflow
def train_and_deploy():
    model = train_model()
    best_model = evaluate(model)
    deploy_to_koyeb(model=best_model)

2. Train Anywhere

Run distributed training on-prem, in your private cloud, or on a GPU provider. Union.ai handles orchestration and resource provisioning.

3. Evaluate Automatically

Union.ai tracks model performance and automates evaluation to select the best checkpoint.

4. Deploy to Koyeb

Deploy the selected model to Koyeb’s serverless platform for low-latency, autoscaling inference.

5. Manage the Lifecycle

Control versioning, rollbacks, and monitoring—all from Union.ai’s interface.

Looking Ahead: Orchestrating Intelligent AI Systems

As enterprises adopt LLM-powered agents and autonomous AI systems, orchestration must evolve.

Union.ai’s dynamic workflow engine, combined with Koyeb’s elastic compute, enables AI systems that can:

• Plan and adapt in real time
• Invoke other agents or tools
• Scale compute dynamically
• Recover from failure gracefully

This is orchestration not just for “what runs next,” but for “what thinks next.”

The Future of AI Infrastructure is Hybrid and Intelligent

Union.ai and Koyeb are building the orchestration and compute layers that power the next generation of AI—dynamic, distributed, and agentic.

Together, we enable enterprises to move beyond static pipelines and into a world where AI systems can think, adapt, and scale across any environment.

By 2026, more than 80% of enterprises will have used generative artificial intelligence (GenAI) application programming interfaces (APIs) or models, and/or deployed GenAI-enabled applications in production environments, up from less than 5% in 2023, according to Gartner, Inc.

Get Started Today

Ready to orchestrate your AI workflows across hybrid cloud infrastructure?

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