GPU Cluster Management Software
Alternatives to dstack
dstack coordinates AI infrastructure and scheduling across GPU clouds, Kubernetes, virtual machines, and bare-metal clusters. YAML configurations cover fleets, development environments, tasks, services, presets, and volumes, while the CLI and HTTP API manage resources. It supports NVIDIA, AMD, TPU, and Tenstorrent accelerators, distributed frameworks including Ray and Spark, and inference gateways with HTTPS, auto-scaling, custom domains, and rate limits. Teams can run its server on a laptop or another connected environment, using self-hosted infrastructure or dstack Sky.
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ClearML ClearML suits teams managing AI/ML work across GPU infrastructure and model development, especially those needing cloud, on-premises, hybrid, or self-hosted deployment.
From $15/mo Free plan Web · Windows · macOS · Linux8.7 Visit Specs - 02 Backend.AI
Backend.AI serves users ranging from small teams with a single GPU to institutions operating large clusters.
Free plan Web · Linux · API · Self-hosted8.3 Visit Specs - 03
GPUStack GPUStack suits teams deploying AI models across on-premise, cloud, or hybrid infrastructure, including Kubernetes and air-gapped environments.
Free plan Web · Windows · macOS · Linux8.3 Visit Specs - 04
HAMi HAMi suits teams running AI, machine-learning or HPC workloads on Kubernetes that need to share accelerator resources.
Free plan Linux · Self-hosted8.3 Visit Specs - 05
Apache YuniKorn It suits Kubernetes users managing batch, data, or machine learning workloads who need queue controls and scheduling policies.
Free plan Web · API · Self-hosted8.1 Visit Specs - 06 Kueue
Kueue suits teams managing batch workloads and quotas on Kubernetes clusters.
Free plan API · Self-hosted8.0 Visit Specs - 07 HTCondor
It suits academic, government, and commercial organizations managing computational workloads across networked machines.
Web · Windows · macOS · Linux7.9 Visit Specs - 08 OpenPBS
OpenPBS is suited to organizations managing HPC workloads across clusters, clouds, or supercomputers.
Free plan macOS · Linux · API · Self-hosted7.6 Visit Specs - 09 Koordinator
It suits Kubernetes operators managing mixed latency-sensitive, batch, AI, and data workloads.
Linux · API · Self-hosted7.5 Visit Specs - 10 Slurm Workload Manager
Slurm suits teams managing high-performance computing or AI workloads on self-hosted Linux clusters.
Free plan Linux · API · Self-hosted7.5 Visit Specs - 11 TORQUE Resource Manager
TORQUE suits administrators managing compute clusters who can build and deploy a self-hosted resource manager.
Free plan macOS · Linux · API · Self-hosted7.5 Visit Specs - 12 KAI Scheduler
KAI Scheduler suits teams running GPU workloads on Kubernetes across shared cloud or on-premises clusters.
Self-hosted7.4 Visit Specs - 13 Moab HPC Suite
It suits organizations operating Linux-based HPC environments that need policy-driven scheduling, resource allocation, analytics, and multi-cluster control.
Linux · API · Self-hosted7.2 Visit Specs - 14
NVIDIA Mission Control Web · Linux · Self-hosted7.2 Visit Specs - 15
SkyPilot It is aimed at AI teams managing large GPU fleets and workloads across cloud, Kubernetes, Slurm, virtual machines and on-premises infrastructure.
Web · Linux · API · Self-hosted6.9 Visit Specs - 16 NVIDIA Base Command Manager
It suits administrators managing Linux-based servers, nodes, or systems who need provisioning and lifecycle controls.
Free plan Web · Linux6.5 Visit Specs - 17 Voda Scheduler API · Self-hosted6.5 Visit Specs
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Volcano API · Self-hosted6.5 Visit Specs - 19 IBM Spectrum LSF Windows · Linux5.8 Visit Specs
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NVIDIA Run:ai Web · Linux5.6 Visit Specs
Choosing instead of dstack
An alternative may suit you if you need fractional GPU sharing, integrated experiment development, model-serving engines, or high-throughput batch scheduling instead. dstack OSS is free, while Sky Marketplace usage consumes prepaid credits at provider-dependent GPU-hour prices; BYOC currently has no charge. Compare deployment environments, accelerator coverage, workload controls, interfaces, and whether Kubernetes or other scheduling ecosystems fit your operation. Also account for dstack's single-host TPU limit of eight cores per instance, its lack of quota controls, and server data and project secrets stored in plaintext by default unless encryption is configured.