Neptune.ai vs ClearML
Neptune.ai and ClearML serve fundamentally different scopes within the MLOps landscape. Neptune is a deep, specialized experiment tracker built for frontier model research, now being acquired by OpenAI to power their internal training infrastructure. ClearML is a comprehensive open-source MLOps platform that covers the full machine learning lifecycle from experiment tracking through pipeline orchestration, dataset versioning, model serving, and GPU cluster management. For most ML teams, ClearML delivers far more functionality at a fraction of the cost, while Neptune occupies a narrow but critical niche in large-scale foundation model training.
Neptune.ai3.6ClearML4
MLOps
Quick Comparison
| Feature | Neptune.ai | ClearML |
|---|---|---|
| Primary Focus | Deep experiment tracking and visualization for foundation model training at scale | Full-stack MLOps covering experiments, pipelines, data, serving, and compute orchestration |
| Deployment Model | Enterprise managed service; now integrating into OpenAI's training stack | Self-hosted open source, hosted free tier, or managed cloud and on-prem options |
| Pipeline Orchestration | Not a core capability; focused on experiment tracking and monitoring | Built-in pipeline automation with caching, parallel execution, and CI/CD integration |
| Pricing Model | Contact for pricing | Open Source free, $15/unknown tier |
| Open Source | Proprietary platform with no open-source offering | Apache-2.0 licensed with 6,600+ GitHub stars and active community |
| Best For | Research teams training large foundation models that need deep metric analysis | ML teams needing a unified self-hostable platform for the full model lifecycle |
Neptune.ai
- Primary Focus:
- Deep experiment tracking and visualization for foundation model training at scale
- Deployment Model:
- Enterprise managed service; now integrating into OpenAI's training stack
- Pipeline Orchestration:
- Not a core capability; focused on experiment tracking and monitoring
- Pricing Model:
- Contact for pricing
- Open Source:
- Proprietary platform with no open-source offering
- Best For:
- Research teams training large foundation models that need deep metric analysis
ClearML
- Primary Focus:
- Full-stack MLOps covering experiments, pipelines, data, serving, and compute orchestration
- Deployment Model:
- Self-hosted open source, hosted free tier, or managed cloud and on-prem options
- Pipeline Orchestration:
- Built-in pipeline automation with caching, parallel execution, and CI/CD integration
- Pricing Model:
- Open Source free, $15/unknown tier
- Open Source:
- Apache-2.0 licensed with 6,600+ GitHub stars and active community
- Best For:
- ML teams needing a unified self-hostable platform for the full model lifecycle
Community & Adoption Signals
| Metric | Neptune.ai | ClearML |
|---|---|---|
| GitHub stars | — | 6.6k |
| PyPI weekly downloads | 39.8k | 117.1k |
| Search interest | 1 | 0 |
| Product Hunt votes | 6 | — |
As of 2026-04-27 — updated weekly.
Interface Preview
ClearML
Feature Comparison
| Feature | Neptune.ai | ClearML |
|---|---|---|
| Experiment Tracking | ||
| Automatic Logging | Tracks hyperparameters, metrics, and model artifacts across long training runs | Auto-logs hyperparameters, metrics, console output, git diffs, and uncommitted code changes with zero manual calls |
| Experiment Comparison | Compare thousands of runs and analyze metrics across layers in seconds | Side-by-side comparison of experiments, datasets, and models with 2D/3D visualizations |
| Long-Running Training Support | Purpose-built for monitoring months-long foundation model training with branches | Supports ongoing experiment tracking with remote execution via ClearML Agents |
| Pipeline & Orchestration | ||
| Pipeline Automation | Not a core capability; focused purely on experiment tracking | Turn any Python function into a pipeline step with dependency injection and result caching |
| Remote Execution | No built-in remote execution or agent-based job scheduling | ClearML Agents queue and execute experiments on GPU clusters, cloud VMs, and on-prem infrastructure |
| Hyperparameter Optimization | Not offered as a built-in capability | Built-in HPO controller supporting grid search, random search, and Bayesian optimization |
| Data & Model Management | ||
| Dataset Versioning | Tracks artifacts but does not provide dedicated dataset versioning | Built-in dataset versioning tied directly to experiments with S3, GCS, Azure, and NAS support |
| Model Serving | Not offered; focused on the training phase of the model lifecycle | Cloud-ready model serving with GPU optimization backed by Nvidia-Triton |
| Model Repository | Stores model metadata and training artifacts for comparison | Centralized model repository with versioning, lineage tracking, and deployment integration |
| Infrastructure & Compute | ||
| GPU Cluster Management | No built-in compute orchestration or GPU management | Infrastructure Control Plane manages GPU resources across on-prem, cloud, and hybrid environments |
| Fractional GPUs | ❌ | Dynamic fractional GPU allocation to maximize compute utilization across teams |
| Cloud Auto-Scaling | Not available as a platform capability | Auto-scaling across AWS, GCP, and Azure available on Pro tier and above |
| Platform & Integration | ||
| Framework Integration | Integrates with major ML frameworks for experiment tracking | Auto-logging for TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM, Matplotlib, and TensorBoard |
| Self-Hosting | No self-hosted option available | Full self-hosted deployment with unlimited experiments and complete data sovereignty |
| GenAI Deployment | Focused on training infrastructure rather than GenAI application deployment | GenAI App Engine for deploying LLMs with built-in access control, monitoring, and one-click launch |
Experiment Tracking
Automatic Logging
Neptune.aiTracks hyperparameters, metrics, and model artifacts across long training runs
ClearMLAuto-logs hyperparameters, metrics, console output, git diffs, and uncommitted code changes with zero manual calls
Experiment Comparison
Neptune.aiCompare thousands of runs and analyze metrics across layers in seconds
ClearMLSide-by-side comparison of experiments, datasets, and models with 2D/3D visualizations
Long-Running Training Support
Neptune.aiPurpose-built for monitoring months-long foundation model training with branches
ClearMLSupports ongoing experiment tracking with remote execution via ClearML Agents
Pipeline & Orchestration
Pipeline Automation
Neptune.aiNot a core capability; focused purely on experiment tracking
ClearMLTurn any Python function into a pipeline step with dependency injection and result caching
Remote Execution
Neptune.aiNo built-in remote execution or agent-based job scheduling
ClearMLClearML Agents queue and execute experiments on GPU clusters, cloud VMs, and on-prem infrastructure
Hyperparameter Optimization
Neptune.aiNot offered as a built-in capability
ClearMLBuilt-in HPO controller supporting grid search, random search, and Bayesian optimization
Data & Model Management
Dataset Versioning
Neptune.aiTracks artifacts but does not provide dedicated dataset versioning
ClearMLBuilt-in dataset versioning tied directly to experiments with S3, GCS, Azure, and NAS support
Model Serving
Neptune.aiNot offered; focused on the training phase of the model lifecycle
ClearMLCloud-ready model serving with GPU optimization backed by Nvidia-Triton
Model Repository
Neptune.aiStores model metadata and training artifacts for comparison
ClearMLCentralized model repository with versioning, lineage tracking, and deployment integration
Infrastructure & Compute
GPU Cluster Management
Neptune.aiNo built-in compute orchestration or GPU management
ClearMLInfrastructure Control Plane manages GPU resources across on-prem, cloud, and hybrid environments
Fractional GPUs
Neptune.ai❌
ClearMLDynamic fractional GPU allocation to maximize compute utilization across teams
Cloud Auto-Scaling
Neptune.aiNot available as a platform capability
ClearMLAuto-scaling across AWS, GCP, and Azure available on Pro tier and above
Platform & Integration
Framework Integration
Neptune.aiIntegrates with major ML frameworks for experiment tracking
ClearMLAuto-logging for TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM, Matplotlib, and TensorBoard
Self-Hosting
Neptune.aiNo self-hosted option available
ClearMLFull self-hosted deployment with unlimited experiments and complete data sovereignty
GenAI Deployment
Neptune.aiFocused on training infrastructure rather than GenAI application deployment
ClearMLGenAI App Engine for deploying LLMs with built-in access control, monitoring, and one-click launch
✅ Full support⚠️ Partial / limited❌ Not supported
Our Verdict
Neptune.ai and ClearML serve fundamentally different scopes within the MLOps landscape. Neptune is a deep, specialized experiment tracker built for frontier model research, now being acquired by OpenAI to power their internal training infrastructure. ClearML is a comprehensive open-source MLOps platform that covers the full machine learning lifecycle from experiment tracking through pipeline orchestration, dataset versioning, model serving, and GPU cluster management. For most ML teams, ClearML delivers far more functionality at a fraction of the cost, while Neptune occupies a narrow but critical niche in large-scale foundation model training.
When to Choose Each
Choose Neptune.ai if:
Choose ClearML if:
This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
Frequently Asked Questions
What is the main difference between Neptune.ai and ClearML?
Neptune.ai is a specialized experiment tracker designed for monitoring and visualizing foundation model training at scale, with particular strength in comparing thousands of runs and analyzing metrics across model layers. ClearML is a comprehensive open-source MLOps platform that covers the entire machine learning lifecycle, including experiment tracking, pipeline orchestration, dataset versioning, model serving, and compute orchestration. Neptune focuses deeply on the experiment tracking phase, while ClearML provides breadth across the full ML workflow.
Is ClearML really free to use?
Yes, ClearML offers a genuinely free self-hosted Community edition under the Apache-2.0 license that includes unlimited experiment tracking and full platform features. The hosted free tier provides 100GB artifact storage, 1GB metric events, and 1M API calls per month for teams up to three users. The Pro tier at $15/user/month adds cloud auto-scaling, hyperparameter optimization, and pipeline triggers for teams up to ten. Scale and Enterprise tiers offer custom pricing for organizations needing advanced infrastructure management, SSO, and dedicated support.
What happened to Neptune.ai and is it still available?
OpenAI announced a definitive agreement to acquire Neptune.ai in December 2025. The acquisition brings Neptune's experiment tracking and training monitoring tools into OpenAI's research infrastructure. Neptune has been working closely with OpenAI to develop tools that enable researchers to compare thousands of runs, analyze metrics across layers, and surface training issues. As of 2026, Neptune's standalone product availability may be limited as the platform integrates into OpenAI's training stack. Teams evaluating Neptune should confirm current availability and pricing directly.
Can ClearML replace Neptune.ai for experiment tracking?
ClearML provides robust experiment tracking that covers the core use cases Neptune.ai addresses, including automatic logging of hyperparameters, metrics, git diffs, and code changes. ClearML's auto-magical tracking requires zero manual logging calls, and it supports comparison of experiments with rich 2D and 3D visualizations. However, Neptune.ai was purpose-built for monitoring months-long foundation model training runs with multiple steps and branches, which is a more specialized capability. For most ML teams running standard training workflows, ClearML's experiment tracking is a strong alternative that also provides pipeline orchestration, dataset versioning, and model serving in the same platform.
Which platform is better for teams with limited budgets?
ClearML is the clear winner for budget-conscious teams. Its self-hosted open-source edition provides unlimited experiments, full platform access, and complete data sovereignty at zero cost. The hosted free tier covers small teams without requiring infrastructure setup. Even the Pro tier at $15/user/month is significantly cheaper than most commercial MLOps platforms. Neptune.ai operates on enterprise-only pricing with no public tiers, making it inaccessible for teams that cannot negotiate custom contracts. For startups, academic researchers, and small ML teams, ClearML delivers substantially more value per dollar.