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Neptune.ai

neptune.ai
Summary

Neptune.ai is an MLOps platform specializing in experiment tracking for foundation models. It allows AI researchers and engineers to monitor, compare, and debug thousands of per-layer metrics during model training at scale. The platform offers both SaaS and self-hosted deployment options, catering to enterprise needs for data security and compliance.

Features
4/13
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Must Have

3 of 5

API Access

Safety & Alignment Framework

Enterprise Solutions

Conversational AI

Fine-Tuning & Custom Models

Other

1 of 8

Research & Publications

Image Generation

Code Generation

Multimodal AI

Security & Red Teaming

Synthetic Media Provenance

Threat Intelligence Reporting

Global Affairs & Policy

Pricing
Tiered
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Startup

$150.00 monthly
  • Unlimited tracked hours
  • Unlimited users & projects
  • 1B data points/month
  • 1 TB storage
  • 99.99% ingestion SLA
  • SSO/LDAP
  • Standard email & chat support

Lab

$250.00 monthly
  • Everything from Startup+
  • 10B data points/month
  • 10 TB storage
  • Forking of runs
  • Role-based access control
  • 24/7 support
  • Dedicated Customer Success Manager and user support Slack channels

Self-hosted

Custom
  • Everything from Lab+
  • Deployment on your own infrastructure or private cloud
  • Dedicated instance deployments
  • Ingestion speed & storage limits depending on the infrastructure setup
  • High reliability and availability deployment options
  • Site Reliability Engineer support
Rationale

Neptune.ai is an MLOps platform focused on experiment tracking for foundation models. While it doesn't directly offer conversational AI or multimodal generation, it provides tools for AI researchers and enterprises to monitor, debug, and manage their AI model training, which aligns with the broader concept of an AI platform. It explicitly mentions API access for logging and querying, enterprise solutions with self-hosting and security features, and publishes research reports. The safety and alignment framework is inferred from their focus on security, compliance (SOC2, GDPR), and robust monitoring for stable training, which indirectly contributes to safer AI deployments.