By Erez Schnaider, Technical Product Marketing Manager, ClearML
AI engineering today goes far beyond simply training a model. Teams are fine-tuning large language models on high-end GPUs, running massive, distributed experiments, and orchestrating hybrid workflows spanning on-premises clusters, private and public clouds. With great power comes great responsibility, and with powerful hardware comes complexity. Without robust controls, things can quickly descend into costly chaos: Who’s using what? Is GPU capacity underutilized? Why is Team A idle while Team B runs out of resources? Why are on‑prem GPUs sitting idle while cloud costs skyrocket?
To tackle this, ClearML built the Resource Allocation Policy Manager – a flexible, enterprise-grade control plane built into its Infrastructure Control Plane. It empowers platform and DevOps teams to fully define and automate at a granular level how compute is reserved, prioritized, shared, and pre‑empted across users, teams, clusters, and clouds.
Full Visibility Across All Resources
The foundation of effective resource management is visibility. ClearML’s Policy Manager provides a real-time, system-wide view of available compute resources, how they’re being used, and by whom.
Platform administrators can see:
- Total capacity across heterogeneous clusters (on-prem, cloud, hybrid)
- Real-time resource usage
- Reserved vs. idle vs. over-quota usage
- Available capacity in the cluster
This visibility makes it possible to allow fair-share resource allocation, prioritize specific hardware types over others, and ensure critical projects don’t get blocked due to underutilized capacity elsewhere.
Quotas and Over-Quota Allocation
At its core, the Policy Manager allows defining resource quotas per user or team, such as how many GPUs can be allocated to a team. But real-world environments are rarely that clean-cut. Resources often sit idle, and hard enforcement leads to wasted capacity.
To address this, ClearML supports over-quota allocation. When a team needs to exceed its quota, their jobs can still run using available capacity, using idle machines or capacity reserved for other teams. This ensures that infrastructure is fully utilized when demand allows.
When another team needs to reclaim their quota, ClearML automatically pre-empts over-quota jobs, freeing up capacity. Pre-emption behavior is fully configurable; administrators can define which queues allow over-quota access, which users can exceed limits, and whether jobs are paused or terminated.
This blend of fairness and flexibility eliminates bottlenecks while maintaining organizational policy. Idle resources don’t go unused: over-quota jobs can tap into unused capacity, and if needed, running jobs are pre-empted (with graceful abort callbacks) to enforce reserved quotas.
Cross-Cluster Spillover and Resource Hierarchies
ClearML’s Resource Policy Manager also supports Dynamic Resource Allocation – essentially cross-cluster spillover that allows jobs to shift automatically across infrastructure boundaries when primary resources are unavailable.
This includes:
- Moving jobs from on-prem clusters to cloud when local resources are saturated
- Falling back from high-end GPUs to lower-cost alternatives
- Orchestrating workloads across multiple cloud regions or availability zones
- Ensuring using on-prem resource before spilling over to the cloud, thus controlling costs
Administrators can define resource hierarchies – where jobs first attempt to run on preferred hardware (e.g., A100s on-prem) and only move to secondary options (e.g., cloud instances) when no capacity is available. This is a key feature for cost control, as organizations ensure their expensive on-prem resources are fully utilized before moving to the cloud, which incurs heavy ongoing costs.
This approach also unlocks further cost saving opportunities; for example, burst workloads can run on spot instances or shared clusters, while priority jobs are guaranteed access to dedicated, high-performance nodes.
Abstracted Resource Access Through Queues
To reduce operational complexity for users, ClearML introduces job queues that abstract away the underlying infrastructure. Users don’t need to choose clusters, machine types, or GPU models. Instead, they submit jobs to a named queue like 1xGPU, low-priority, or llm-inference. Behind the scenes, ClearML maps each queue to available resources (or pools of resources).
This abstraction:
- Simplifies the user experience
- Ensures even non-technical users can leverage compute resources
- Ensures a single interface across different resources
As infrastructure changes, new clusters added, nodes removed, costs optimized; users don’t need to adjust anything. The queue abstraction keeps workflows stable and portable.
Resource Access Control via RBAC
ClearML implements fine-grained Role-Based Access Control (RBAC) for compute resources. This allows administrators to define exactly who can access which resources, under what conditions.
Examples include:
- Restricting high-end GPUs to specific teams
- Preventing external contractors from running jobs on sensitive infrastructure
- Assigning different queues and priorities to different organizational units
This also applies for multi-tenant environments that require roles within a specific tenant. Each user or team operates within their assigned boundaries, with no visibility into others’ resources.
Unlocking On-Demand AI Services for CSPs and Telcos
ClearML’s Resource Allocation Policy Manager is designed for CSPs and telcos delivering AI infrastructure at scale. With built-in multi-tenancy and a centralized control plane, providers can securely isolate resources per customer, enforce quotas, and dynamically allocate resources per each tenants’ needs.
Dynamic allocation lets providers shift idle capacity between tenants in real time, maximizing utilization without compromising security. This enables not just reserved environments, but true on-demand compute offerings with support for advanced services like GPU-as-a-Service and Model-as-a-Service built on top.
ClearML gives CSPs and telcos the tools to:
- Offer secure, scalable multi-tenant AI environments
- Monetize both reserved and on-demand GPU access
- Build premium services on top of a shared GPU pool
Why It Matters Now
Built with enterprise needs in mind, the ClearML Resource Allocation Policy Manager offers granular quotas, priority-based queueing, real-time dashboards, and tight integration with fractional GPU scheduling, all without disrupting existing workflows. With GPU demand at an all-time high, and AI workloads becoming larger and more distributed, smart resource management is no longer optional.
ClearML gives you the power to:
- Improve operational efficiency
- Optimize cluster ROI
- Reduce developer friction
- Adapt to real-time demands
It’s everything you need to ensure your compute infrastructure is working for your teams, not against them.
Ready to Take Control of Your Compute Resources?
Whether you’re managing a 10-node lab or a global hybrid GPU fleet, ClearML makes it easy to build a high-performing, cost-efficient, and flexible resource allocation strategy. To learn more, explore our Resource Policies documentation or get in touch with our team to see it in action.