Neptune.ai: Advanced Experiment Tracking for Foundation Model Training

Neptune.ai is a powerful, scalable experiment tracking platform tailored for AI researchers and machine learning teams building foundation models. It enables real-time monitoring of thousands of per-layer metrics—such as losses, gradients, and activations—without lag or data downsampling, making it ideal for deep debugging and optimizing complex training workflows. With support for both cloud and self-hosted deployments, including on-premises and private cloud via Kubernetes Helm charts, Neptune ensures high availability, enterprise-grade security, and seamless integration with popular ML frameworks like PyTorch, TensorFlow, Hugging Face, and Kubeflow. Its unique ability to fork training runs and visualize entire training histories on a single chart enhances reproducibility and collaboration. The platform is trusted by leading organizations including OpenAI and supports air-gapped environments, role-based access control (RBAC), SSO/LDAP, and compliance with SOC2 Type 2 and GDPR. Neptune also offers a public sandbox with large-scale example projects (up to 600k data points per run, 50k metric series) for instant testing and learning, along with dedicated onboarding, migration tools from Weights & Biases, and 24/7 support. Whether you're a researcher, engineer, or enterprise team, Neptune delivers high-performance, secure, and flexible tracking for MLOps and LLMOps success.

Key Features:

• Real-Time Experiment Tracking: Log and visualize thousands of per-layer metrics (losses, gradients, activations) with zero lag and no data downsampling.
• Deep Model Debugging: Detect issues like vanishing or exploding gradients with granular insights into model internals.
• Run Forking & Lineage Preservation: Maintain full training history by forking runs, enabling branching and comparison across experiments.
• High-Throughput Ingestion: Handle up to 500k data points per 10 minutes (Startup), 5M (Lab), and 100M+ (Enterprise/self-hosted) with minimal latency.
• Instant Chart Rendering: Render large datasets and complex visualizations in under 1 second, even with over 1 million data points.
• Flexible Deployment Options: Deploy in the cloud (SaaS), on-premises, or in private clouds (AWS, GCP, Azure) using Kubernetes Helm charts.
• Self-Hosted & Dedicated Instances: Custom, scalable self-hosted plans with no storage limits, high availability (HA), and SRE support.
• Enterprise Security & Compliance: SOC2 Type 2 and GDPR compliant; supports SSO via SAML 2.0 (Okta, OneLogin), RBAC, and service accounts for CI/CD.
• Air-Gapped Environment Support: No forced internet access required, ideal for sensitive or isolated infrastructure.
• Multi-Zone & High Availability: Deploy across multiple availability zones with automated failover and daily consistent backups (<1 min ingestion freeze).
• Seamless Upgrades: Upgrade with minimal downtime (up to 5 minutes pause), no disruption to ongoing training jobs.
• Integration with ML Ecosystem: Native support for PyTorch, TensorFlow/Keras, Hugging Face, Lightning, Kubeflow, XGBoost, LightGBM, Kedro, AzureML, SageMaker, Airflow, and more.
• Custom Integrations: Connect with Kafka, MySQL, ClickHouse, Redis, and other internal services.
• Monitoring & Observability: Built-in Prometheus, Grafana, and OpenTelemetry with pre-configured dashboards and alerts.
• Migration Tools: Easy migration from Weights & Biases (WandB) with support available within 24 hours.
• Public Sandbox: Explore the platform with example projects featuring up to 600k data points per run and 50k metric series.
• Learning Resources: Access the 100 Second Research Playlist, learning hubs on Experiment Tracking, LLMOps, and MLOps, and expert-led product walkthroughs.
• Dedicated Support: Priority email and chat support, dedicated Customer Success Manager (Lab and Enterprise), and onboarding assistance.
• Usage Alerts & Quota Management: Receive alerts at 75% and 100% of quota usage to avoid unexpected charges.

Pricing: Neptune.ai offers a freemium model with a free trial and a free license for academic researchers, students, and Kagglers. Paid plans are usage-based, pricing based on data points logged and storage used, with tiered options including Startup ($150/user/month), Lab ($250/user/month), and custom pricing for self-hosted and enterprise deployments. Additional charges apply for exceeding quotas ($10/million data points, $2/GB storage), and enterprise plans include dedicated support, SRE services, and high reliability with a 99.99% ingestion SLA.