Compute scarcity is not the only struggle AI teams face. Optimal utilization is also key. Friction also emerges when they outgrow informal coordination methods such as shared spreadsheets, manual SSH access, or ad hoc GPU allocation, or when their orchestration stack no longer scales with their workloads.
As your workload evolves from single-node experiments to distributed jobs, and as experimentation overlaps with production services, the orchestration becomes critical. Scheduling behavior, resource allocation, and configuration overhead directly affect how much time engineers spend managing infrastructure instead of running workloads.
Lambda supports multiple orchestration layers, allowing teams to align the platform with their requirements. This post compares four options available on our cloud:
- Slurm: Optimized for large, performance-sensitive GPU-based training. Provides predictable scheduling and strong resource guarantees, but is centered on queued batch jobs.
- Kubernetes: A flexible, container-based platform for running training, inference, and supporting services on GPU clusters. Extensible and widely adopted, but operationally complex.
- SkyPilot: A portable job launcher for GPU workloads across clusters. Simplifies execution and can optimize for cost and availability, but offers less direct control over cluster behavior.
- dstack: A vendor-agnostic control plane for deploying AI workloads across cloud providers, Kubernetes, and on-premises clusters. Simplifies multi-environment management, but newer with a smaller ecosystem.
Slurm
Slurm is a workload manager widely used in high-performance computing environments. It schedules batch jobs across shared nodes connected by high-speed interconnects such as InfiniBand.
It follows a submit-and-queue model: users request specific resources (GPUs, CPUs, memory, time), and the scheduler grants exclusive access once those resources are available. Jobs run directly on the host operating system rather than through a container orchestration layer.
While Slurm is not inherently container-native, Lambda supports containerized workflows via Pyxis and Enroot, enabling users to run OCI-compatible container images within Slurm jobs without requiring a full Kubernetes stack.
Pros:
- Bare-metal performance: Minimal abstraction between the workload and the hardware.
- Native gang scheduling: Ensures multi-node jobs start simultaneously, which is critical for MPI-based distributed training.
- Predictable resource allocation: Explicit resource requests make cluster utilization easier to reason about.
Cons:
- Batch-focused design: Optimized for queued training jobs rather than interactive or service-style workloads.
- Limited native support for inference services: Not designed to manage HTTP traffic or long-lived APIs.
- Basic multi-tenancy model: While Slurm supports fair-share and account-based scheduling, it does not provide the namespace isolation, policy controls, or API-driven resource governance found in Kubernetes.
Slurm works well when the primary goal is to maximize performance and predictability for large training jobs.
Kubernetes
Kubernetes is a general-purpose container orchestration platform originally built for long-lived, networked services such as web applications and APIs. Its core abstractions manage containers, enforce resource limits, handle service discovery, and maintain desired state.
Support for batch and AI workloads has expanded through features like Jobs and GPU scheduling, as well as ecosystem projects such as:
- Kubeflow for AI pipelines
- Ray for distributed execution
- Volcano for improved batch and GPU scheduling
Lambda offers both managed and unmanaged Kubernetes for Superclusters and 1-Click Clusters, with optional installations of these higher-level components. For a deeper look, see how we compare Kubernetes schedulers for AI workloads.
Pros:
- Broad workload support: Can run training jobs, inference services, data pipelines, and supporting infrastructure on the same platform.
- Container-native portability: Standardized container workflows improve consistency across environments.
- Extensible ecosystem: Mature tooling for observability, autoscaling, networking, and policy control.
- Multi-tenancy controls: Namespaces, quotas, and RBAC enable structured resource sharing.
Cons:
- Operational complexity: Requires understanding platform-level abstractions that are not AI-specific.
- Additional components for advanced scheduling: Large distributed jobs often depend on extensions such as Volcano.
- Configuration overhead: Declarative manifests and layered tooling can increase setup and maintenance effort.
- Indirect performance tuning: Debugging distributed training can involve multiple abstraction layers.
Kubernetes is often chosen when teams want a unified platform for both AI and non-AI workloads.
SkyPilot
SkyPilot focuses on workload portability and placement rather than direct cluster management. Users define a job once and run it across supported clusters and environments. The system can select execution targets based on GPU availability or pricing.
Rather than exposing low-level scheduling primitives, SkyPilot emphasizes a consistent interface for launching and managing AI jobs.
Pros:
- Cross-environment portability: The same job can run on existing clusters or on dynamically provisioned instances.
- Cost- and availability-aware placement: Can choose execution targets based on resource availability and pricing.
- Simplified user interface: Reduces exposure to infrastructure-specific concepts.
- Well suited for burst workloads: Useful when capacity sourcing varies over time.
Cons:
- Less granular control over scheduling behavior: Abstracts away some infrastructure details.
- Not a full-cluster platform or orchestration system: Focused on job provisioning, orchestration, and execution, rather than managing long-lived, multi-tenant cluster services and policies.
- Additional abstraction layer for debugging: Issues may involve both SkyPilot and the underlying platform, making it more difficult to diagnose the root cause of problems.
SkyPilot is often a good fit when flexibility and cost optimization across environments are primary concerns.
dstack
dstack aims to create a unified method for deploying AI workloads across different GPU cloud service providers, Kubernetes, and on-premises clusters.
Pros:
- Vendor-agnostic: One single control plane for all of your GPU infrastructure.
Cons:
- Newcomer status: Newer to the MLOps landscape, with a smaller community and fewer integrations.
Choose what fits
There is no perfect, universal orchestration stack. Slurm, Kubernetes, SkyPilot, and dstack represent different approaches for scheduling and managing workloads. The right choice depends on what you are optimizing for: tightly controlled batch training, a unified platform for mixed workloads, or portable job execution across environments can dictate which orchestration stack makes sense.
Selecting the orchestration layer that aligns with how your team actually runs workloads reduces operational overhead as your usage scales.
Not sure which orchestration solution fits your organization's AI workloads best? See options on Lambda or talk to our team.