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Machine Learning · head to head

Neptune.ai vs Ray

Neptune.ai logo

Neptune.ai

Machine Learning

Metadata store for MLOps

From
Free
Rated
-
Full reviewWebsite
Ray logo

Ray

Machine Learning

Scale AI and Python applications

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Neptune.ai free tier limited to 100 hours per month, exhausted quickly with serious ML work; Ray windows support is beta and multi node Ray clusters are untested on Windows
  • They diverge on capability: Neptune.ai covers Experiment tracking, Ray covers Distributed computing.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Neptune.ai and Ray actually diverge.

Attributes where Neptune.ai and Ray differ
AttributeNeptune.aiRay
Pricing modelUnknownfreemium
PlatformsWeb, Self-hostedLinux, Mac, Windows
Founded20172019

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Neptune.ai

  • Experiment tracking
  • Model registry
  • Metadata logging
  • Comparison views
  • Custom dashboards
  • Keras
  • XGBoost
  • Web support

Only in Ray

  • Distributed computing
  • Ray Train
  • Ray Tune
  • RLlib
  • Ray Serve
  • Hugging Face
  • Kubernetes

Both cover

  • PyTorch
  • TensorFlow
  • scikit-learn
  • Linux support
  • Mac support
  • Windows support

What people use each for

The jobs each tool is most often brought in to do.

Neptune.ai

  • Machine learningnot Ray
  • Data analysisnot Ray
  • Model trainingnot Ray
  • Predictive analyticsnot Ray

Ray

  • Distributed AI model training and servingnot Neptune.ai
  • Large-scale data processingnot Neptune.ai
  • Reinforcement learning workloadsnot Neptune.ai
  • ML inference servingnot Neptune.ai

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Neptune.ai

  • Free tier limited to 100 hours per month, exhausted quickly with serious ML work
  • Lacks hyperparameter sweeps compared to Weights and Biases
  • No pipeline orchestration or broader MLOps lifecycle management
  • Dashboard visualization limitations - automatic resizing affects visualization order and size
  • Cloud-based SaaS only (as of last available service) requires internet connectivity

Ray

  • Windows support is beta and multi node Ray clusters are untested on Windows
  • Windows lacks copy on write forking, which raises memory requirements, and Ray code assumes UNIX filenames
  • Multi node clusters are untested on Apple Silicon Macs
  • The Java API is experimental and community supported only, and requires matching Java and Python versions
  • Python 3.13 support is beta

Pricing, plan by plan

Neptune.ai

Free

No published plan breakdown. See the Neptune.ai review.

Ray

Free
  • Open SourceFree
    • Full Ray framework
    • All libraries
    • Community support
  • Anyscale PlatformFree
    • Managed infrastructure
    • Enterprise support
    • SLAs

Which should you pick?

Choose Neptune.ai if

  • You need experiment tracking.
  • You want to start without paying.
  • You work on Web, Self-hosted.
  • You also want model registry.
Neptune.ai review

Choose Ray if

  • You need distributed computing.
  • You want to start without paying.
  • You work on Linux, Mac, Windows.
  • You also want ray train.
Ray review

Questions people ask

Is Neptune.ai or Ray better?
Neither clearly leads. Neptune.ai starts at Free and Ray at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Neptune.ai or Ray?
Neptune.ai starts at Free and Ray at Free.
Does Neptune.ai or Ray run on more platforms?
Neptune.ai runs on Web, Self-hosted. Ray runs on Linux, Mac, Windows.
Can I use Neptune.ai for free?
Both have a free tier, so you can try either at no cost before committing.
What is Neptune.ai best used for?
Neptune.ai is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Ray is typically brought in for.
What can Neptune.ai do that Ray cannot?
Neptune.ai covers Experiment tracking, Model registry, Metadata logging, Comparison views. Ray covers Distributed computing, Ray Train, Ray Tune, RLlib. Both handle PyTorch, TensorFlow, scikit-learn, Linux support.

Answered from the vendors’ own pages

Neptune.ai: Does Neptune.ai support self-hosting?

Yes. Neptune can be self-hosted on a Kubernetes cluster with ClickHouse, MySQL, and Redis dependencies, allowing organizations to maintain full data control.

Source
Ray: Is Ray free?

Yes. Ray is free and open source software with over 34,800 GitHub stars and 1,000+ contributors. Users can download and use the Ray framework at no cost.

Source
Neptune.ai: What machine learning frameworks does Neptune integrate with?

Neptune integrates with PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, LightGBM, Hugging Face Transformers, and Optuna for hyperparameter optimization.

Source
Ray: Is there a paid option for Ray?

Yes. Anyscale, the managed platform built by Ray's creators, offers paid tiers with enterprise features like governance and advanced tooling. Specific Anyscale pricing details are not listed on the Ray website.

Source
Neptune.ai: What is the cost for a team of 10 data scientists?

Neptune's Team plan costs $49 per user per month, resulting in $490/month for 10 users, comparable to Weights and Biases at $50/user.

Source
Ray: Can I try Ray with credits?

Yes. New users can try Ray with $100 credit on Anyscale's managed platform to explore the service.

Source
Neptune.ai: When is Neptune.ai shutting down?

Neptune.ai is shutting down its external SaaS service on March 5, 2026, following its acquisition by OpenAI in December 2025. Customers must export and migrate data before that date.

Source
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