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Neptune.ai

https://neptune.ai/ Research Team Data
Acquired Acquired by OpenAI

Experiment tracker for foundation-model training, debugging, and training observability; acquisition by OpenAI announced.

Neptune helps AI teams track experiments, monitor training, debug model behavior, and understand complex foundation-model training runs as they happen.

Positioning

Experiment tracker for foundation-model training

Key facts

HQ location
Warsaw, Poland / remote
Founded
2017
Employee range
51-200 (51-200 pre-acquisition)
Funding stage
Bootstrapped
Company type
Unknown (Acquired / OpenAI announced)
Pricing model
Subscription (SaaS subscription; hosted service winding down post-acquisition)
Last updated
Jun 21, 2026

Revenue estimate

Unknown

Valuation estimate

Deal terms undisclosed; Reuters reported under $400M stock value estimate

Investments

Acquisition by OpenAI announced Dec 2025; prior public funding was modest/undisclosed in detail

Target customers

AI labs, ML researchers, and teams training large/foundation models

Key competitors

Weights & Biases, Comet, MLflow, WhyLabs, TensorBoard

Known customers

OpenAI, Samsung, Roche, HP and foundation-model teams publicly referenced

Classification (raw research text)

Core focus
Foundation-model experiment tracking
Core industry
AI Development Tools / MLOps
Core category
Experiment tracking and training observability

Shown verbatim from the research spreadsheet — deriving structured segment/industry tags from this text is a future phase.

Attribute breakdown

  • AI Workflows Secondary feature
  • AI Fine-tuning / Custom Model Training Secondary feature
  • System / API Integration Secondary feature
  • Traditional Machine Learning Primary focus
  • AI Quality Assurance / LLM Evaluation Secondary feature
  • AI Observability / Monitoring Primary focus
  • AI Asset Inventory / Model Registry Secondary feature
  • Analytics / BI / Decision Intelligence Secondary feature
Show all 32 attributes
  • AI Workflows Secondary feature
  • AI Automation / Business Process Automation Not emphasized
  • AI Fine-tuning / Custom Model Training Secondary feature
  • Agent Builder / Agent Configuration Not emphasized
  • Multi-agent Orchestration / Runtime Not emphasized
  • System / API Integration Secondary feature
  • Prompt Management / Prompt Engineering Not emphasized
  • Retrieval-Augmented Generation Not emphasized
  • Graph RAG / Knowledge Graph Retrieval Not emphasized
  • Enterprise Search / Knowledge Management Not emphasized
  • AI / LLM Data Pipeline Not emphasized
  • Document AI / Document Processing Not emphasized
  • Model Deployment / Inference Infrastructure Not emphasized
  • Traditional Machine Learning Primary focus
  • AI Quality Assurance / LLM Evaluation Secondary feature
  • AI Observability / Monitoring Primary focus
  • AI Security / Guardrails Not emphasized
  • Data Privacy / PII / Confidential AI Not emphasized
  • AI Governance / Policy Management Not emphasized
  • AI Risk / Compliance Not emphasized
  • AI Asset Inventory / Model Registry Secondary feature
  • Human-in-the-Loop Review / Feedback Not emphasized
  • Call Transcription / Speech-to-Text Data Capture Not emphasized
  • Conversation Intelligence / Speech Analytics Not emphasized
  • Text Chatbots / Conversational Assistants Not emphasized
  • Voice AI Agents Not emphasized
  • Voice Infrastructure / STT / TTS Not emphasized
  • AI for Customer Experience / Support Automation Not emphasized
  • Sales / Revenue Intelligence Not emphasized
  • Analytics / BI / Decision Intelligence Secondary feature
  • Enterprise App / Internal Tool Builder Not emphasized
  • Vertical-Specific AI Not emphasized