HTCondor
HTCondor: A broad, free scheduler for teams managing batch and interactive GPU pools. Ranked #13 of 21 in GPU Cluster Management Software by our editors (7.9/10); pricing: Free plan; best for general-purpose batch GPU pool management.
At a glance
- Editor score7.9 / 10
- PricingFree plan
- Best forGeneral-purpose batch GPU pool management
- Free planYes
- Paid fromFree
- Deployment modelSelf_hosted
- Facts checked22 Sep 2026
Where it wins
- Supports batch and interactive scheduling with GPU-aware allocation.
- Adds quotas, accounting groups, utilization reporting, and DAG workflows.
- Extends across federated pools, cloud resources, and Kubernetes deployments.
Where it doesn't
- Self-hosted deployment puts infrastructure management on the organization.
- Its broad scope may exceed the needs of simple GPU scheduling.
- No paid SaaS tiers or vendor-managed deployment are provided.
Our verdict on HTCondor
HTCondor is open-source, self-hosted software for managing distributed workloads across clusters, campus resources, cloud infrastructure, and computing grids. It is designed for organizations that need both batch job queues and interactive scheduling, with support for Linux, macOS, Windows, web access, APIs, and Kubernetes deployment. GPU-aware allocation, resource matching, and reporting make it relevant to teams operating shared GPU pools rather than a single workstation or narrowly focused scheduler.
The pricing model is straightforward: HTCondor is open source and available on a free plan, with no paid tier structure. That makes it suitable for organizations evaluating broad scheduling capabilities without a subscription commitment. Its feature set covers GPU detection and allocation, GPU utilization and memory reporting, hierarchical accounting groups, quotas, and matching jobs to machine requirements. DAG-based workflow orchestration adds structure for dependent workloads, while interactive scheduling complements conventional batch queues.
HTCondor’s main advantage is breadth. Pool federation through flocking can combine resources across administrative boundaries, htcondor annex can expand capacity into cloud resources, and Kubernetes support provides another deployment path. The tradeoff is that it remains self-hosted software rather than a vendor-managed SaaS product, so organizations must operate the deployment themselves. Teams needing detailed pool controls, multiple workload types, and expansion across cluster, cloud, or Kubernetes environments should consider HTCondor. A simpler GPU scheduler or managed service may be a better fit for organizations that want minimal infrastructure administration or only basic queue management.
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HTCondor pricing
HTCondor fact sheet
| Free plan | Yes |
|---|---|
| Paid from | Free |
| Deployment model | Self_hosted |
| Workload scheduling | Both |
| Kubernetes support | Yes |
| Quota controls | Yes |
| GPU utilization metrics | Yes |
| Cloud GPU support | Yes |
| Deployment | Self-hosted, Cloud |
| Platforms | Web, Windows, macOS, Linux |
| Support | Email, Community, Docs |
| Built for | Mid-market, Enterprise (editorial estimate) |
| Pricing | Free plan |
| Website | htcondor.com |
| Facts checked | 22 Sep 2026 |
Alternatives to HTCondor
- Backend.AIA broad GPU cluster platform for teams managing shared, multi-tenant AI infrastructure.9.4
- NVIDIA Mission ControlEnterprise GPU cluster management with orchestration, observability, and automated recovery.9.2
- NVIDIA Run:aiA broad Kubernetes-native GPU control plane for training, inference, and shared infrastructure.9.0
See all HTCondor alternatives →
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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