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Machine Learning · head to head

LangChain vs Seldon

LangChain logo

LangChain

Machine Learning

Build applications with LLMs through composability

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: LangChain the free Developer plan of LangSmith is limited to 1 seat; 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: LangChain covers Chains and agents, Seldon covers Kubernetes custom resources.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which LangChain and Seldon actually diverge.

Attributes where LangChain and Seldon differ
AttributeLangChainSeldon
PlatformsLinux, Mac, WindowsLinux
Founded20222014

Identical on both: starting price (Free), pricing model (freemium), 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 LangChain

  • Chains and agents
  • Retrieval-augmented generation
  • Memory management
  • Tool integration
  • Prompt templates
  • OpenAI
  • Anthropic
  • Hugging Face

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.

LangChain

  • Building LLM applications and agents in Python or JavaScriptnot Seldon
  • Tracing and debugging LLM chains and agent runsnot Seldon
  • Evaluating prompt and model changes against datasetsnot Seldon

Seldon

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

Where each one falls short

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

LangChain

  • The free Developer plan of LangSmith is limited to 1 seat
  • Base traces are retained for 14 days only; 400 day retention costs extra
  • Included traces are capped at 5,000 per month on Developer and 10,000 per month on Plus, with everything beyond billed pay as you go
  • Self hosted and hybrid deployment of LangSmith is Enterprise only
  • Custom SSO, RBAC and ABAC are Enterprise only
  • A support SLA is Enterprise only
  • Enterprise pricing is by quote with no published rate

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

LangChain

Free
  • Open SourceFree
    • Full framework
    • Community support
  • LangSmith$39/month
    • Debugging
    • Monitoring
    • Testing

Seldon

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

Which should you pick?

Choose LangChain if

  • You need chains and agents.
  • You want to start without paying.
  • You work on Linux, Mac, Windows.
  • You also want retrieval-augmented generation.
LangChain 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 LangChain or Seldon better?
Neither clearly leads. LangChain 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, LangChain or Seldon?
LangChain starts at Free and Seldon at Free.
Does LangChain or Seldon run on more platforms?
LangChain runs on Linux, Mac, Windows. Seldon runs on Linux.
Can I use LangChain for free?
Both have a free tier, so you can try either at no cost before committing.
What is LangChain best used for?
LangChain is most often used for building llm applications and agents in python or javascript, tracing and debugging llm chains and agent runs, evaluating prompt and model changes against datasets. Of those, building llm applications and agents in python or javascript and tracing and debugging llm chains and agent runs are not what Seldon is typically brought in for.
What can LangChain do that Seldon cannot?
LangChain covers Chains and agents, Retrieval-augmented generation, Memory management, Tool integration. Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol.

Answered from the vendors’ own pages

LangChain: Does LangChain charge for its services?

LangChain's main website does not display pricing. However, LangSmith (a related platform) offers both free and paid plans. Visit the dedicated pricing page or contact LangChain for details.

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.

LangChain: How can I learn about LangChain pricing?

Click on the Pricing link in navigation or use the Try LangSmith or Get a demo options to explore pricing for LangChain's commercial offerings.

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.

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.

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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