Pro usage can incur separate charges for artifact storage, metric events, API calls, and applications.
Estimate activity and data volumes alongside per-user subscription costs before forecasting monthly spend.
Evidence source ↗
ClearML
ClearML is an MLOps platform for experiment and dataset management, model tracking, workflow orchestration, and AI infrastructure management.
7 key claims verifiedOur editorial team checked this profile against vendor documentation, pricing information, product documentation. Supported by 6 documented sources.
Editorial overview
Aggregated Overview
Official documentation covers experiment tracking, data versioning, APIs, orchestration, and flexible deployment. G2 shows 13 reviews and a 4.7/5 average, while its review summary flags setup effort as a potential challenge.
This profile combines documented product information and independent evidence where available. It is not a hands-on test unless TechZella explicitly identifies one.
Overview
ClearML is an MLOps platform that connects experiment tracking, dataset management, model repositories, pipelines, and compute orchestration. Teams can use its hosted service or deploy it on their own infrastructure, with private and hybrid deployment options listed for higher-tier plans.
The Python package can automatically record source, environment, parameters, model files, metrics, logs, and related experiment information. ClearML also provides a REST API and a Python API client for programmatic management.
ClearML Decision Snapshot
A quick way to understand who this product may suit, where its limits matter, and which facts are most relevant before you compare alternatives.
Best suited to
AI and machine-learning teams seeking a shared system for experiment tracking, dataset versioning, job orchestration, and model lifecycle workflows, with options for hosted or self-managed deployment.
Look elsewhere if
Teams seeking a turnkey managed MLOps service with predictable flat pricing may find the metered Pro usage and self-hosted infrastructure responsibilities require additional planning, and should evaluate alternatives such as Weights & Biases instead.
Platforms
AWS, Cloud, Kubernetes, Self-hosted, Web
TechZella assessment
8.3/10
ClearML — What Sets It Apart?
These are the product-specific differences that may matter when deciding whether the software fits your workflow. They are based on documented evidence rather than a universal ranking.
- ClearML combines experiment tracking, dataset versioning, model management, pipelines, and compute orchestration in one product family.
- Its deployment choices include hosted service, self-hosted installations, VPC, on-premises, air-gapped, and hybrid configurations, depending on plan.
- The Python SDK and REST API allow programmatic access to tasks, projects, queues, workers, events, and models.
ClearML Pros & Cons
Pros
- Combines experiment tracking, dataset versioning, model management, pipelines, and compute orchestration.
- Provides a Python SDK, Python API client, and REST API.
- Offers hosted and self-managed deployment paths, including enterprise private infrastructure options.
- The free Community plan includes a defined storage, metric-event, and API-call allowance.
- Official documentation lists integrations with widely used ML frameworks and tools.
Cons
- Pro pricing adds metered charges for storage, metric events, API calls, and applications.
- Some governance, infrastructure, and support features are reserved for Scale or Enterprise plans.
- Self-hosted deployments require teams to manage their own infrastructure.
- G2's review summary notes setup complexity as a concern for some users.
ClearML Gotchas & Operational Limits
These are documented constraints or practical friction points that may matter before you adopt the product.
Role-based access control and LDAP integration appear in the Enterprise tier rather than Community or Pro.
Organizations that need these specific access controls should confirm plan eligibility before adopting the hosted service.
Evidence source ↗G2's review summary identifies setup complexity as a concern raised by some reviewers.
Teams should budget time for configuration and workflow adoption rather than assuming every deployment is turnkey.
Evidence source ↗ClearML Pricing & Plans
Community
$0
month
Hosted plan for teams up to 3; includes 100GB artifact storage, 1GB metric events, and 1M API calls per month.
Check current pricing →Pro
$15 per user
month
For teams up to 10; additional usage charges apply for storage, metric events, API calls, and applications.
Check current pricing →Scale
Custom Quote
Listed for organizations with 8-48 GPUs; VPC-only plan with additional infrastructure and development features.
Check current pricing →Enterprise
Request a Quote
Listed for multiple large projects using VPC or on-premises clusters; includes enterprise controls and support options.
Check current pricing →Pricing may change. Verify current plans on the vendor's website.
ClearML Competitors & Alternatives
Compare similar products directly, then see which alternatives may make more sense when your requirements differ.
At a glance
Artificial Intelligence Software| Decision factor | ClearML | Weights & Biases | MLflow |
|---|---|---|---|
| Core scope | Experiment tracking, dataset and model management, pipelines, job orchestration, and infrastructure control plane options. | Experiment tracking, asset registry and lineage, application evaluations and tracing, and collaboration features. Source ↗ | MLflow documents experiment tracking, model packaging, registry, and lifecycle management through APIs and UI. Source ↗ |
| Pricing model | Community is $0 for up to 3; Pro is $15 per user per month plus usage; Scale and Enterprise are quoted. | Free plan is $0; Pro starts at $60/month billed monthly; Enterprise is custom-priced. Source ↗ | Open-source MLflow is available at no software license price; hosting, storage, and operations depend on the chosen setup. Source ↗ |
| Deployment model | Hosted SaaS, self-hosted, VPC, on-premises, air-gapped, and hybrid options are described, subject to plan. | Cloud-hosted and privately hosted options are listed, with self-hosted server options in its pricing material. Source ↗ | MLflow supports local tracking and a self-managed tracking server, with remote backend and artifact storage configurable. Source ↗ |
| Workflow emphasis | Product documentation includes job queues, agents, pipeline execution, dataset versioning, and compute orchestration. | Product pages emphasize experiment tracking, model and artifact lineage, evaluation, tracing, and collaboration. Source ↗ | MLflow provides run tracking, artifact logging, experiment comparison, and model registry workflows; teams configure tracking server and storage components as needed. Source ↗ |
Only factors supported by the available research are shown. The comparison is descriptive, not a ranking.
Direct competitors
These products address broadly similar requirements. The descriptions focus on documented reasons a buyer might compare them with ClearML.
ClearML Features
Experiment and model management
- Experiment tracking
- Automatic logging of execution details
- Model repository and model metadata
- Experiment, dataset, and model comparison
- Artifacts and metrics
Dataset management
- Dataset versioning
- Dataset search and metadata
- Dataset SDK
- External storage connections including S3, Google Cloud Storage, Azure Storage, HTTP, and NAS
Orchestration and automation
- Agents and workers
- Job queues and scheduling
- Pipelines
- CI/CD automation through REST and Python interfaces
- Cloud autoscaling on Pro
APIs and integrations
- Python SDK and APIClient
- REST API
- PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, Optuna, Hydra, and TensorBoard integrations
Deployment and administration
- Hosted SaaS
- Self-hosted open-source deployment
- VPC, on-premises, air-gapped, and hybrid options by plan
- Kubernetes integration in Scale and Enterprise
Demo & Screenshots
ClearML Specifications
| Product focus | MLOps and AI infrastructure |
|---|---|
| Primary interface | Web UI and Python SDK |
| API | REST API and Python APIClient |
| Hosting | Hosted SaaS and self-managed options |
| Deployment choices | Self-hosted, VPC, on-premises, air-gapped, and hybrid options vary by plan |
| Free plan limits | Up to 3 users; 100GB artifact storage; 1GB metric events; 1M API calls per month |
| Pro plan limits | Up to 10 users; usage charges can apply |
| Supported integrations | PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, LightGBM, Optuna, Hydra, TensorBoard, and more |
| Storage connections | HTTP, S3, Google Cloud Storage, Azure Storage, and NAS |
| Headquarters | Berkeley, California, United States |
| Founded | 2016 |
Deployment & System Footprint
Verified installation, hosting, administration, or environment details that can affect implementation.
| Hosting options | Hosted SaaS, self-hosted, VPC, on-premises including air-gapped, or hybrid deployment options are listed, with availability varying by plan. Source ↗ |
|---|---|
| Compute integration | ClearML can orchestrate workloads across compute resources; its pricing page lists Kubernetes integration for Scale and Enterprise and cloud autoscaling for AWS, GCP, and Azure on Pro. Source ↗ |
Migration & Lock-In
Documented portability, standards, export, or switching considerations. No switching-cost score is shown unless the evidence supports one.
Dataset and artifact storage
Dataset management can use connected storage including HTTP, S3, Google Cloud Storage, Azure Storage, and NAS; plan storage migration and permissions separately when changing deployments.
Evidence source ↗Our evidence-based assessment
TechZella Score
A proprietary editorial score based on product capabilities, usability, value, performance, support and user sentiment evidence.
8.3/10Moderate confidence
Official documentation covers experiment tracking, data versioning, APIs, orchestration, and flexible deployment. G2 shows 13 reviews and a 4.7/5 average, while its review summary flags setup effort as a potential challenge.
Methodology v1.0. The assessment weighs feature coverage (25%), ease of use (15%), value for money (20%), performance (15%), support (10%) and user sentiment (15%), using researched product evidence and verified review evidence where available. It is a TechZella editorial assessment, not a direct user-review average.
Editorial transparency
Our Review Methodology
Key claims are linked to the sources used to verify them. This evidence record is separate from third-party user ratings.
TechZella evaluates software using documented product capabilities, pricing information, usability and value considerations, independent user evidence where available, and competitive context. The assessment is an editorial review, not a hands-on test unless one is explicitly identified. Our editorial assessment is based on 6 documented sources.
What TechZella adds
- ClearML combines experiment tracking, dataset versioning, model management, pipelines, and compute orchestration in one product family.
- Its deployment choices include hosted service, self-hosted installations, VPC, on-premises, air-gapped, and hybrid configurations, depending on plan.
- The Python SDK and REST API allow programmatic access to tasks, projects, queues, workers, events, and models.
Important limitations
- The hosted Pro plan has usage-based charges for storage, metric events, API calls, and applications beyond its included amounts.
- Several enterprise controls, including role-based access control and LDAP integration, are limited to Enterprise according to the pricing comparison.
- G2's review summary notes that initial setup can be complex for some users.
Evidence-backed claimsClearML offers a hosted Community plan for teams of up to three users at $0. ClearML Pro is listed at $15 per user per month plus usage. ClearML's Python SDK can automatically capture execution and experiment information. ClearML exposes a REST API and a Python API client. ClearML lists integrations with major machine-learning frameworks and tools. ClearML's global headquarters is in Berkeley, California. ClearML was founded in 2016.
The official pricing page lists Community at $0, with no credit card required, for teams up to 3.
View source ↗Verified 2026-11-11The Pro listing names the per-user monthly price and additional metered usage charges.
View source ↗Verified 2026-11-11The SDK documentation lists source code, packages, machine details, model files, parameters, metrics, logs, and debug samples among captured information.
View source ↗Verified 2026-11-11Official documentation describes the server API as RESTful and the Python APIClient as an interface to backend REST services.
View source ↗Verified 2026-11-11The integrations documentation names PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, Optuna, Hydra, TensorBoard, and other tools.
View source ↗Verified 2026-11-11The official contact page lists ClearML Inc.'s global headquarters at 2288 Fulton St, Berkeley, CA 94704, USA.
View source ↗Verified 2026-11-11CB Insights identifies ClearML, formerly Allegro.ai, as founded in 2016 and based in Berkeley, California.
View source ↗Verified 2026-11-11ClearML FAQs
Answers begin with the clearest available response, followed by the context that helps you act on it.
Does ClearML have a free plan?
Yes. ClearML lists a hosted Community plan at $0 for teams of up to three, with no credit card required.
Does ClearML offer a free trial?
The public pricing page lists a free Community plan and paid plans, but does not state a separate time-limited trial for ClearML Pro.
Does ClearML provide an API?
Yes. ClearML documents a REST API and a Python APIClient for accessing backend services.
Can ClearML be self-hosted?
Yes. ClearML says its open-source software can be self-hosted; paid offerings also list VPC, on-premises, air-gapped, or hybrid deployment options, depending on plan.
Which machine-learning frameworks does ClearML integrate with?
Its official integration list includes PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, LightGBM, Optuna, Hydra, and TensorBoard, among others.
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