By Noam Harel, Co-founder and CMO, ClearML
AI is certainly transforming industries, but delivering it at scale is a harder task
The shift to enterprise-grade AI isn’t just about building better models. It’s about managing the growing sprawl of infrastructure, tools, and people involved in every phase of your AI production From building and training to production deployment, teams are bogged down by fragmented workflows, manual provisioning, inconsistent environments, and underutilized compute.
From our ongoing conversations with IT teams and AI builders, as well as through insights from our AI Infrastructure at Scale survey, it’s clear that what organizations really need now is a unified, scalable, and intelligent way to orchestrate AI workloads – without adding more operational complexity.
That’s where ClearML’s Infrastructure Control Plane comes in.
Designed for IT teams and AI builders, the ClearML Infrastructure Control Plane is the backbone that streamlines workload delivery, maximizes compute utilization, and enables resource management and allocation with built-in policy-based governance across every phase of AI production. It eliminates bottlenecks, maximizes your GPU utilization, and helps teams move faster effortlessly, with greater visibility and control.
This blog post explores how.
Why AI Workloads Are So Hard to Manage Today
The traditional approach to managing AI workloads is disjointed. Data scientists submit jobs through ad hoc scripts. Engineers provision infrastructure manually, or worse, wait for shared clusters to become available. MLOps teams struggle to enforce consistent environments across training and inference. Governance and security are often afterthoughts.
This operational chaos leads to:
- Idle GPUs: Valuable hardware sits unused due to poor workload placement or long queues.
- Developer friction: Data scientists lose time wrangling infrastructure instead of building models.
- Inconsistent deployments: Environments drift, models behave differently in production than in training.
- High cloud costs: Over-provisioned or orphaned resources drive up bills without adding value.
- Security risks: Lack of access controls, audit trails, and policy enforcement opens the door to compliance issues.
What’s missing is a control layer – a centralized, automated system for managing infrastructure as a first-class citizen in the AI lifecycle.
Enter ClearML: Your Unified AI Infrastructure Control Plane
ClearML’s Infrastructure Control Plane is built to address these pain points head-on. It sits between your AI workloads and your compute layer (on-prem, cloud, or hybrid) and acts as a smart broker, orchestrator, and policy enforcer.
With ClearML, you get:
- Dynamic workload orchestration with intelligent queueing and scheduling
- Seamless multi-cloud and on-prem support through Kubernetes or native VMs
- Automated provisioning and resource scaling
- Real-time usage visibility and GPU-aware job scheduling
- Role-based access controls and policy-driven compute governance
ClearML can also implement secure multi-tenancy with per-tenant IDP, network, storage, and real-time billing based on consumption for CSPs and Telcos interested in increasing the utilization of their compute infrastructure.
It’s not just about making things run, it’s about making them run smarter, faster, and more cost-effectively. For example:
1) Automate and Optimize Workload Scheduling
At the core of ClearML’s Infrastructure Control Plane is its ability to intelligently schedule AI jobs across your entire infrastructure. Whether it’s training, fine-tuning, hyperparameter optimization, or inference, workloads are automatically assigned to the best-fit resources based on availability, priority, and policies.
This dynamic orchestration supports:
- GPU-aware placement: Match workloads with the right compute (full GPUs, fractional GPUs, CPU-only nodes, etc.).
- Priority queues: Ensure that mission-critical jobs run first without blocking others.
- Auto-scaling: Spin up or down cloud instances as needed, tied to real-time demand.
That helps you achieve zero idle capacity, lower wait times, and maximum throughput across your hardware estate.
2) Unify and Simplify DevOps for AI Teams
Data scientists don’t want to become infrastructure engineers. ClearML bridges that gap with self-service capabilities and transparent automation.
Teams can:
- Launch jobs with a click from their Jupyter notebook, CLI, or ClearML SDK
- Automatically spin up containers or virtual machines in the right environment
- Reuse pre-configured templates to ensure reproducibility
- Version and track everything from code and datasets to models and experiments
With infrastructure abstracted away, teams can stay focused on experimentation and iteration, while the control plane handles the rest.
3) Maximize GPU Utilization with Fractional Allocation
ClearML’s support for Dynamic Fractional GPUs is a game changer for resource optimization.
Instead of allocating an entire GPU to a single lightweight job, ClearML can dynamically divide a single GPU among multiple smaller workloads. This is ideal for tasks like prompt engineering, fine-tuning small LLMs, or running batch inference.
Benefits include:
- 200% improvement in GPU utilization
- Shorter job queues
- Reduced cloud spend and higher on-prem ROI
No more wasting GPU capacity on underutilized jobs.
4. Govern Access with Policy-Based Controls
Security and compliance matter, especially in regulated industries. ClearML offers robust governance features to ensure only the right users can access the right resources at the right time.
Admins can define:
- Role-based access to compute environments and data
- Quota-based limits per team or user
- Resource hierarchies (e.g., jobs run on-prem and spillover to the cloud)
This gives platform and DevOps teams the peace of mind they need, without sacrificing agility for control.
5) Support Any Environment: On-Prem, Cloud, or Hybrid
Whether you’re running on AWS, GCP, Azure, private bare metal, or a hybrid mix, ClearML fits into your stack with minimal disruption.
Through native Kubernetes integration or direct VM orchestration, ClearML abstracts the complexity of managing environments while enabling seamless workload portability.
You can:
- Use cloud for burst capacity or experimentation
- Run production inference on your local GPU servers
- Define policies to route jobs to cost-effective environments
- Leverage a silicon-agnostic platform, so you’re never locked into a single vendor
- Provide a multi-tenant experience for any deployment
And because it’s all controlled centrally, the experience is seamless across environments.
In addition to supporting a wide range of environments, ClearML significantly simplifies life for both Kubernetes administrators and users. It abstracts direct cluster interaction and provides a streamlined interface that enables:
- Simplified access to workloads without exposing users to Kubernetes internals
- Cross-cluster workload prioritization and spillover
- Enhanced visibility and management of workload queues
- True multi-tenancy on top of Kubernetes, with per-tenant SSO and RBAC
This architecture benefits both cluster operators, by centralizing control, and end users, who can submit and manage workloads through an intuitive interface without needing Kubernetes expertise.
6) Enable Collaboration Across the AI Lifecycle
ClearML goes beyond infrastructure orchestration: it connects the dots across your entire AI workflow, including:
- Experiment tracking
- Data lineage
- Model versioning
- Pipeline orchestration
- Continuous training and deployment (CI/CD)
The Infrastructure Control Plane ties everything together so that data scientists, MLOps engineers, and platform teams can collaborate without stepping on each other’s toes.
Everyone gets what they need without needing to manually coordinate.
A Better Way to Deliver AI at Scale
The future of AI is faster, more collaborative, and more infrastructure-aware. As organizations push from proof-of-concept to production, the bottlenecks are no longer just technical, they’re operational.
ClearML’s Infrastructure Control Plane eliminates those bottlenecks with:
- Smart workload orchestration
- Infrastructure abstraction and automation
- GPU-aware optimization
- End-to-end policy enforcement
- Multi-environment portability
- Seamless collaboration and observability
By giving AI teams the tools to move faster (and platform teams the controls to manage responsibly) ClearML helps organizations unlock the full value of their AI investment.
Get Started
Whether you’re managing dozens or thousands of AI workloads, ClearML helps you do more with your infrastructure, accelerate delivery, and take control of your AI workloads end to end.
Learn more about the ClearML Infrastructure Control Plane or book a demo to see it in action.