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Machine Learning · head to head

Neptune.ai vs Seldon

Neptune.ai logo

Neptune.ai

Machine Learning

Metadata store for MLOps

From
Free
Rated
-
Full reviewWebsite
Seldon logo

Seldon

Machine Learning

Kubernetes model serving whose current version is licensed under the Business Source Licence

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; Seldon seldon Core v2 is under the Business Source Licence rather than Apache 2.0, so production use requires a commercial agreement, and a team that evaluated it believing it was open source discovers the licence is the blocker exactly when the project is ready to ship.
  • They diverge on capability: Neptune.ai covers Experiment tracking, Seldon covers Kubernetes custom resources.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Neptune.ai and Seldon differ
AttributeNeptune.aiSeldon
Pricing modelUnknownfreemium
PlatformsWeb, Self-hostedLinux
Founded20172014

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
  • PyTorch
  • TensorFlow
  • Keras

Only in Seldon

  • Kubernetes custom resources
  • Inference graphs
  • Traffic strategies
  • Open Inference Protocol
  • Alibi Explain
  • Alibi Detect
  • Kafka-backed pipelines in v2
  • Commercial control plane

What people use each for

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

Neptune.ai

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

Seldon

  • Serving an ensemble or a multi-stage inference path as one versioned deployment rather than as a chain of separate servicesnot Neptune.ai
  • Running genuine production experiments where a share of live traffic goes to a candidate model and the results are comparednot Neptune.ai
  • Regulated environments needing explanations and drift monitoring attached to the served model rather than bolted on laternot Neptune.ai
  • Organisations with an established Kubernetes platform team who want serving expressed as manifests under existing deployment controlsnot 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

Seldon

  • Seldon Core v2 is under the Business Source Licence rather than Apache 2.0, so production use requires a commercial agreement, and a team that evaluated it believing it was open source discovers the licence is the blocker exactly when the project is ready to ship.
  • Core v1 remains Apache 2.0 but is in maintenance, so taking the free route means running software that receives no new development while the architecture it belongs to moves on without it.
  • Version 2 is a different system rather than a newer release, with different custom resources, a scheduler component and a Kafka-based pipeline model, so migrating from v1 is a re-implementation of every deployment manifest rather than an upgrade.
  • Kafka is a dependency for v2 pipelines, so an organisation that does not already operate it takes on a distributed log with its own storage, retention, rebalancing and failure modes purely in order to serve models.
  • Everything assumes Kubernetes fluency and the failure modes are Kubernetes failure modes, custom resource version mismatches, an operator that will not reconcile, admission webhooks and resource limits terminating an inference pod mid-request, so it needs a platform engineer rather than a data scientist.

Pricing, plan by plan

Neptune.ai

Free

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

Seldon

Free
  • Seldon CoreFree
    • Open source
    • Kubernetes deployment
    • Model serving
  • Seldon DeployFree
    • Enterprise features
    • GUI
    • Monitoring

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 Seldon if

  • You need kubernetes custom resources.
  • You want to start without paying.
  • You work on Linux.
  • You also want inference graphs.
Seldon review

Questions people ask

Is Neptune.ai or Seldon better?
Neither clearly leads. Neptune.ai starts at Free and Seldon at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Neptune.ai or Seldon?
Neptune.ai starts at Free and Seldon at Free.
Does Neptune.ai or Seldon run on more platforms?
Neptune.ai runs on Web, Self-hosted. Seldon runs on Linux.
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 Seldon is typically brought in for.
What can Neptune.ai do that Seldon cannot?
Neptune.ai covers Experiment tracking, Model registry, Metadata logging, Comparison views. Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol.

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
Seldon: Is Seldon open source?

Partly, and this is the thing to check before you build on it. Core v1 is Apache 2.0 but in maintenance. Core v2 was moved to the Business Source Licence in 2024, which allows evaluation but not unlicensed production use. Verify the current licence of each component you intend to run, including MLServer and the Alibi libraries.

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
Seldon: What is the difference between v1 and v2?

Architecture, not just version number. v2 introduces a scheduler, a different set of custom resources and Kafka-backed pipelines. Manifests, mental model and operations all change, so treat a move as a project.

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
Seldon: Do I need Kubernetes?

Yes. It is a Kubernetes-native system and there is no meaningful deployment without a cluster and someone competent to run it.

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
Seldon: What is MLServer?

Seldon's Python inference server implementing the Open Inference Protocol, usable inside Seldon deployments or on its own. Check its current licence alongside Core's, since the company has moved projects onto the Business Source Licence.

Seldon: Do I have to run Kafka?

For v2 pipelines, yes. If you only need single models served, that dependency is a large amount of infrastructure for the benefit, and a simpler serving layer may be the better answer.

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