ClearML
MLOps platforms
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- Ease of use
- Ease of management
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$15 per user per month
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What is ClearML
ClearML is an MLOps platform for tracking machine learning experiments, managing datasets and artifacts, and orchestrating training and inference workloads. It is used by data science and ML engineering teams to improve reproducibility and operationalize model development across local, on-prem, and cloud environments. The product combines an open-source stack (server, agent, SDK) with optional managed offerings, and it emphasizes experiment lineage, remote execution, and pipeline automation.
Strong experiment tracking lineage
ClearML captures experiment metadata, code versions, parameters, metrics, logs, and artifacts in a centralized system. It supports reproducibility by linking runs to datasets and model outputs and by enabling comparisons across experiments. This is particularly useful for teams that need auditable training histories and consistent handoffs from research to engineering.
Remote execution and orchestration
ClearML Agent enables dispatching training jobs to remote machines and managed compute while keeping the same codebase and configuration. It supports queue-based scheduling and resource utilization across heterogeneous environments (e.g., developer workstations, GPU servers, cloud instances). This helps teams scale training without building a custom job runner from scratch.
Open-source deployable architecture
ClearML provides open-source components that can be self-hosted, which can fit organizations with data residency or network isolation requirements. The SDK-first approach integrates with common Python ML workflows and can be adopted incrementally. Teams can start with experiment tracking and expand to pipelines, dataset management, and model management as needs grow.
UI and workflow learning curve
The platform spans multiple modules (experiments, datasets, pipelines, queues, models), which can require upfront configuration and process alignment. Teams may need to define conventions for projects, naming, and artifact storage to avoid clutter and inconsistent lineage. Users coming from more guided, end-to-end analytics environments may find initial setup less prescriptive.
Enterprise governance varies by edition
Some enterprise-grade capabilities (e.g., advanced access controls, SSO/SAML, multi-tenancy, and centralized policy enforcement) depend on the specific deployment/edition and may require the managed or enterprise offering. Organizations with strict compliance requirements should validate identity, audit, and retention features for their target architecture. This can add procurement and implementation steps compared with platforms that bundle governance by default.
Not a full data platform
ClearML focuses on ML lifecycle management rather than providing a comprehensive lakehouse/warehouse, BI layer, or broad data engineering toolset. Many teams still need separate systems for feature engineering, large-scale ETL, and governed data access. Integrations exist, but end-to-end workflows can require additional tooling and operational ownership.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Community | $0 (Free) | For teams up to 3. Core features: Dataset Versioning, Model Training, Experiment Management, Model Repository, Artifacts, Pipelines, Agent Orchestration, CI/CD Automation, Reports. Included quotas: 100 GB artifact storage, 1 GB metric events, 1M API calls/month. No credit card required. |
| Pro | $15 per user/month + usage | For teams up to 10. Includes Community features plus Cloud Auto Scaling (AWS/GCP/Azure), Hyperparameter Optimization, Pipeline Triggers & Automations, Dashboards. Included quotas: 120 GB artifact storage, 1.2 GB metric events, 1.2M API calls/month. Overage rates: $0.10 per GB artifact storage; $0.01 per 1 MB metric events; $1 per 100K API calls; $0.04/hr per application. |
| Scale (VPC) | Custom quote / Pay-for-what-you-use (VPC only) | For organizations with ~8–48 GPUs. “Pay for what you use” VPC offering. Adds Scale-tier AI Dev Center features (Hyper-Datasets, fine-tuning, IDE launcher, vector DB integration, Kubernetes integration, SSO, SLA) plus Infrastructure Control Plane features (hardware- and cloud-agnostic orchestration, job scheduling, multi-cluster support, fractional GPUs). |
| Enterprise | Custom quote | VPC or on-prem (including air-gapped) or hybrid. Enterprise entitlements: ClearML custom apps, configuration vault, Slurm/PBS integration, LDAP integration, role-based access control, white-glove support with custom SLA, professional services, advanced scheduling, quota & resource policy management. |
| Self-hosted (Open Source) | Free | 100% open-source ClearML on GitHub. Self-hosted server is free (user responsible for hosting/infra). |
Seller details
ClearML Inc.
Tel Aviv, Israel
2018
Private
https://clear.ml/
https://x.com/clearml
https://www.linkedin.com/company/clearml/
