Modern applications run within containers that package the application’s code along with all its files and libraries. Developers can run large applications as hundreds or even thousands of containers that can be switched on and off as needed.

AI applications take containerization to the next level. AI pipelines often involve multiple containers working together across a cluster (a group of servers) to handle data preprocessing, training and inference.

What is cluster orchestrationWhat is cluster orchestration

Cluster orchestration is the automated management of the distributed computing resources, called clusters, required to efficiently run and scale AI workloads.

A compute cluster is a group of interconnected server nodes that work together to handle large-scale computational tasks that exceed the capacity of a single machine. Typical cluster components include:

• Cluster nodes (servers, VMs or cloud instances with corresponding GPUs, CPUs, TPUs and RAM)
• Networks and network interfaces
• IP addresses of nodes
• Server templates and plug-ins

Each node in the cluster hosts identical application components, but the servers themselves may have different configurations. Cluster orchestration automates various coordination tasks to manage the cluster effectively.

Key functions of cluster orchestration toolsKey functions of cluster orchestration tools

Cluster orchestration tools and platforms contain several functional components that are responsible for specific aspects of workload coordination.

SchedulerScheduler

The scheduler is responsible for deciding where and when to run each workload. It matches containerized jobs to available cluster nodes based on factors like CPU/GPU availability, memory and priority. In AI orchestration, the scheduler also considers GPU topology, memory constraints and job deadlines.

Resource managerResource manager

The resource manager tracks and allocates compute resources (CPU, GPU, memory, storage) across the cluster. It ensures that workloads don’t exceed available capacity. It also implements quotas and supports dynamic scaling.

Service discoveryService discovery

Service discovery enables containers and services within the cluster to locate and communicate with each other. It uses DNS-based discovery or internal registries to route requests. This is essential for multi-phase tasks, such as a training service communicating with a data loader or an inference API finding a model registry.

ControllerController

The controller, or control plane, maintains the system’s desired state. If a container crashes, the controller detects the failure and automatically restarts it. It also handles scaling instructions, manages rollout strategies and ensures system consistency.

Networking and load balancingNetworking and load balancing

Orchestration tools manage internal networking between containers and nodes and often include load balancing features to distribute requests across running instances. For AI inference workloads, load balancing helps maintain low latency under high traffic.

Storage orchestrationStorage orchestration

AI workloads often require access to large datasets or model checkpoints. Storage orchestration ensures containers can mount persistent volumes or access object stores, regardless of where they run. This includes managing shared file systems or integrating with external storage solutions.

Monitoring and loggingMonitoring and logging

Many orchestration platforms integrate with monitoring and logging systems, such as Prometheus and Fluentd. These tools collect metrics, logs and health checks, enabling observability and debugging across distributed workloads.

What kinds of cluster orchestration tools are thereWhat kinds of cluster orchestration tools are there

There are numerous cluster orchestration platforms and frameworks available.

• General-purpose orchestration platforms, such as Kubernetes and Docker Swarm, are widely adopted for both AI and non-AI workloads.

• High-performance computing (HPC) tools such as Slurm and OpenPBS (Portable Batch System) are optimized for large-scale, batch-based processing.

• AI/ML-native orchestration frameworks, such as Ray and Kubernetes extensions like Kubeflow and Volcano, are purpose-built for AI tasks.

• Cloud-based services offer serverless workflow orchestration.

You can choose the one that best suits your needs. For example, HPC tools are more common in academic and scientific AI research. AI-native frameworks offer features such as GPU-aware scheduling, distributed training and hyperparameter tuning. Cloud-based services simplify orchestration by handling infrastructure and scaling automatically, though they may limit portability and create vendor lock-in.

What tool is best for cluster orchestrationWhat tool is best for cluster orchestration

Choosing the best orchestration tool depends on your AI workload, infrastructure and team expertise. In this section, we describe some top favorites and compare key features.

KubernetesKubernetes

Kubernetes is the most widely adopted orchestration platform. More than 96% of organizations surveyed by the CNCF Annual Survey 2023 use Kubernetes, with 72% using it in production environments. It is valued for its scalability, community support and vast ecosystem.

Although originally designed for general-purpose applications, it now supports AI workloads through extensions such as Kubeflow. It is ideal for teams that need fine-grained control across hybrid or multi-cloud environments.

Nebius provides Managed Kubernetes optimized for modern AI workloads. You scale your clusters by adding new nodes that have drivers pre-installed. You can reduce operational complexity on multi-host installations and ensure quick compute expansion when needed.

Apache MesosApache Mesos

Apache Mesos uses a resource-centric architecture that allows multiple frameworks to share the same cluster. While powerful, it has lost traction in recent years as Kubernetes became the industry standard. It still finds relevance in legacy systems or specialized environments that need fine-grained resource isolation.

RayRay

Ray is purpose-built for AI/ML workloads in Python-based environments. It supports distributed training, hyperparameter tuning and model serving with minimal configuration. Ray is lightweight compared to Kubernetes and is ideal for teams focused exclusively on AI without needing general infrastructure management.

SlurmSlurm

Slurm is an open-source workload manager designed to handle job scheduling and resource allocation in Linux-based compute clusters. It operates independently without requiring any kernel changes and offers a structure to launch, manage and monitor parallel tasks across cluster nodes. It is designed for batch job scheduling and resource reservation, making it a natural fit for GPU-heavy AI training pipelines.