Neptune.ai

Neptune.ai is an experiment tracking and model observability platform built for extreme-scale machine learning workloads.

Reviewed by 7wData
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Publisher review

Neptune.ai is an experiment tracking and model observability platform built for extreme-scale machine learning workloads. Founded in 2017 and headquartered in Warsaw, Poland, it became known for handling 100,000+ concurrent experiment runs without UI lag—a documented advantage over Weights & Biases at high-metadata volumes. The platform ingests over 1 million data points per second and indexes complete, unsampled metrics (no downsampling), enabling teams to track per-layer losses, gradients, and activations that reveal training instabilities invisible to aggregate metrics alone.

Neptune serves data science teams, ML researchers, and AI labs training foundation models. It integrates with 25+ frameworks including PyTorch, TensorFlow, XGBoost, Hugging Face Transformers, and Optuna. The platform offered cloud-hosted and self-hosted deployment options and supported run forking, model registry, and role-based access control.

Critical status: OpenAI acquired Neptune in December 2025 for under $400 million in stock. The SaaS service shuts down March 5, 2026, at 10 am PST. All hosted data will be irreversibly deleted; customer data will not transfer to OpenAI. Users must export runs and migrate to alternatives (migration guides are available for Weights & Biases, MLflow, Comet, and ZenML) before the deadline. This makes Neptune non-viable for new deployments as of June 2026.

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How it works

  1. Hyperscale metadata ingestion

    Ingest 1+ million data points per second without UI lag, compare 100,000+ runs in milliseconds, no downsampling of metrics.

  2. Per-layer gradient and activation logging

    Track gradients, layer-wise losses, and activations across training to detect instabilities W&B misses at aggregate-only visibility.

  3. Run forking and branching

    Branch experimental runs at checkpoints to explore multiple configurations without restarting from scratch.

  4. Framework auto-detection

    One-line integration auto-detects PyTorch, TensorFlow, XGBoost, and 22+ other frameworks; automatic logging of hyperparameters and model internals.

  5. Model registry and versioning

    Version control for trained models with staging (development, staging, production) and model lineage tracking tied to experiment runs.

  6. Cloud and self-hosted deployment

    Choose cloud-hosted SaaS (now discontinued) or self-hosted on-premises for security and compliance-critical deployments.

  7. Run comparison and visualization

    Compare multiple runs side-by-side with responsive charts for thousands of per-layer metrics; no long loading times at scale.

Strengths and trade-offs

Strengths

  • Handles orders of magnitude higher metadata throughput than Weights & Biases, with 100,000+ runs rendering in milliseconds without UI lag.
  • Complete unsampled metric retention enables detection of training instabilities (gradient spikes, activation saturation) invisible to downsampled dashboards.
  • Strong framework integration ecosystem (25+ frameworks) with automatic gradient and parameter logging via lightweight Python client.

Trade-offs

  • Platform shutting down March 5, 2026; no new sign-ups accepted and all SaaS data will be permanently deleted with no transfer to OpenAI.
  • Steep learning curve for advanced features; free tier was discontinued, with paid plans starting at $150/user/month (Startup) or $250/user/month (Lab).
  • High infrastructure cost for organizations logging millions of data points per run; storage overage charged at $2/GB/month beyond tier limits.

Pricing context

Neptune charged on a data-volume model: Startup plan ($150/user/month) included 1 billion data points per month and 1 TB storage; Lab plan ($250/user/month) provided 10 billion data points per month and 10 TB storage. Enterprise plans required direct negotiation with sales. Overages were billed at $2/GB per month.

The platform discontinued free and $50/month tiers in late 2025. All pricing is now moot due to OpenAI acquisition and March 5, 2026 shutdown.

Getting started with Neptune.ai

  1. Export your Neptune runs

    Log into your Neptune account and navigate to the project dashboard. Use the built-in export tool to download all experiment runs, metadata, and artifacts as JSON or CSV files before the March 5, 2026 shutdown.

  2. Install migration client

    Run `pip install neptune-client` in your Python environment to install the latest Neptune client. This client includes export and migration utilities to transfer your data to alternative platforms.

  3. Connect to alternative platform

    Choose a migration target like Weights & Biases or MLflow. Create an account on the new platform and obtain your API key. Set the key as an environment variable for authentication.

  4. Migrate experiment data

    Use Neptune's migration script to transfer runs, metrics, and model registry entries to the new platform. Run `neptune migration start --target wandb` and follow the prompts to map fields.

  5. Verify migration completeness

    Log into your new platform and compare the imported runs against your original Neptune dashboard. Check that all metrics, parameters, and artifacts are present and correctly formatted.

Frequently Asked Questions

What is Neptune.ai and why is it shutting down?

Neptune.ai is an experiment tracking and model observability platform for large-scale machine learning. OpenAI acquired it in December 2025 for under $400 million in stock. The SaaS service shuts down March 5, 2026, and all hosted data will be permanently deleted.

When does Neptune.ai stop working and what happens to my data?

Neptune.ai's SaaS service shuts down March 5, 2026, at 10 am PST. All hosted data will be irreversibly deleted and will not transfer to OpenAI. You must export your runs and migrate to an alternative platform before that deadline to avoid losing your work.

What are the best alternatives to Neptune.ai for experiment tracking?

Top alternatives include Weights & Biases, MLflow, Comet, and ZenML. Neptune.ai provides migration guides for these platforms. Each offers experiment tracking and model management features. Choose based on your team's scale, budget, and deployment preferences.

How does Neptune.ai compare to Weights & Biases for large-scale ML?

Neptune.ai handles 100,000+ concurrent experiment runs without UI lag and ingests over 1 million data points per second with no downsampling. This gives it an advantage over Weights & Biases at high metadata volumes, especially for tracking per-layer gradients and activations.

What was Neptune.ai's pricing before the shutdown?

Neptune.ai charged per user per month: Startup plan at $150/user/month with 1 billion data points and 1 TB storage, Lab plan at $250/user/month with 10 billion data points and 10 TB storage. Overages were billed at $2/GB per month. Free tiers were discontinued in late 2025.

How do I export my data from Neptune.ai before the shutdown?

Neptune.ai provides export tools and migration guides for Weights & Biases, MLflow, Comet, and ZenML on their transition hub. You must export your runs and metadata before March 5, 2026, when all hosted data will be irreversibly deleted with no transfer to OpenAI.

Alternatives

How Neptune.ai compares

Direct head-to-head against 3 competitors. Picked by 7wData.

This tool

Neptune.ai

Pricing
Neptune charged on a data-volume model: Startup plan ($150/user/month) included 1 billion data points per month and 1 TB storage; Lab plan ($250/user/month) provided 10 billion data points per month and 10 TB storage. Enterprise plans required direct negotiation with sales. Overages were billed at $2/GB per month. The platform discontinued free and $50/month tiers in late 2025. All pricing is now moot due to OpenAI acquisition and March 5, 2026 shutdown.
Target
Neptune.ai is an experiment tracking and model observability platform built for extreme-scale machine learning workloads.
Strength
Handles orders of magnitude higher metadata throughput than Weights & Biases, with 100,000+ runs rendering in milliseconds without UI lag.
Watch for
Platform shutting down March 5, 2026; no new sign-ups accepted and all SaaS data will be permanently deleted with no transfer to OpenAI.

MLflow

Pricing
Open source (free); managed Databricks tier starts at $0.70/DBU
Target
Teams needing open-source experiment tracking and model registry
Deployment
Self-hosted or managed
Strength
Widely adopted open-source framework with 23k+ GitHub stars
Watch for
Limited to basic tracking; no built-in pipeline orchestration

Comet ML

Pricing
Free for individuals; Team starts at $19/user/month; Enterprise custom
Target
Teams needing end-to-end model evaluation with LLM support
Deployment
SaaS or self-hosted
Strength
LLM evaluations and production monitoring
Watch for
Pricing can escalate with team size and advanced features

Amazon SageMaker

Pricing
Pay-as-you-go; Studio costs vary by instance; no separate license fee
Target
Enterprises already on AWS needing full ML lifecycle management
Deployment
Managed cloud service
Strength
Deep integration with AWS ecosystem and managed infrastructure
Watch for
Complex setup and vendor lock-in to AWS

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Sources

Reporting on this tool draws on these publicly available sources.

  1. docs.neptune.ai — Neptune shutdown timeline (March 5, 2026), data deletion, export tools, migration guides to Weights & Biases, MLflow, Comet, ZenML, and others.
  2. www.zenml.io — Neptune core strengths vs Weights & Biases, comparison of visualization and metric tracking, recommendation that Neptune users migrate before shutdown.
  3. notesfrompoland.com — Neptune.ai founded 2017, headquartered Warsaw Poland, OpenAI acquisition details.
  4. medium.com — Neptune's architecture, data ingestion throughput, run forking, responsive UI at scale, framework integrations.
  5. www.oreateai.com — Neptune pricing tiers (Startup $150/user/month, Lab $250/user/month), storage allocations, overage charges, discontinuation of free tier.
  6. news.ycombinator.com — Community discussion of OpenAI acquisition, market consolidation concerns, migration urgency, emerging alternative platforms.