AI · head to head
LangGraph vs Seldon
Seldon
Machine Learning
Kubernetes model serving whose current version is licensed under the Business Source Licence
- From
- Free
- Rated
- -
The short version
- Each has a real cost: LangGraph steeper learning curve compared to high-level abstractions; 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: LangGraph covers Human-in-the-loop controls, Seldon covers Kubernetes custom resources.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which LangGraph and Seldon actually diverge.
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).
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 LangGraph
- Human-in-the-loop controls
- Customizable workflows
- Memory management
- Token-by-token streaming
- Low-level control
- Multi-agent support
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.
LangGraph
- Building production AI agents with auditable workflowsnot Seldon
- Designing multi-agent systems for complex tasksnot Seldon
- Implementing human oversight in autonomous systemsnot Seldon
- Creating reliable agentic applications at scalenot Seldon
Seldon
- Serving an ensemble or a multi-stage inference path as one versioned deployment rather than as a chain of separate servicesnot LangGraph
- Running genuine production experiments where a share of live traffic goes to a candidate model and the results are comparednot LangGraph
- Regulated environments needing explanations and drift monitoring attached to the served model rather than bolted on laternot LangGraph
- Organisations with an established Kubernetes platform team who want serving expressed as manifests under existing deployment controlsnot LangGraph
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
LangGraph
- Steeper learning curve compared to high-level abstractions
- Requires understanding of graph-based architecture
- Debugging complex workflows can be challenging
- Not optimized for simple, one-off use cases
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
LangGraph
Free- Open SourceFree
- MIT-licensed framework
- Self-hosted deployment
- Full API access
- LangGraph Platform$35/month
- Managed hosting
- Enterprise deployment
- Integrated tooling
Seldon
Free- Seldon CoreFree
- Open source
- Kubernetes deployment
- Model serving
- Seldon DeployFree
- Enterprise features
- GUI
- Monitoring
Which should you pick?
Choose LangGraph if
- You need human-in-the-loop controls.
- You want to start without paying.
- You work on Python, JavaScript, Web.
- You also want customizable workflows.
Choose Seldon if
- You need kubernetes custom resources.
- You want to start without paying.
- You work on Linux.
- You also want inference graphs.
Questions people ask
- Is LangGraph or Seldon better?
- Neither clearly leads. LangGraph 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, LangGraph or Seldon?
- LangGraph starts at Free and Seldon at Free.
- Does LangGraph or Seldon run on more platforms?
- LangGraph runs on Python, JavaScript, Web. Seldon runs on Linux.
- Can I use LangGraph for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is LangGraph best used for?
- LangGraph is most often used for building production ai agents with auditable workflows, designing multi-agent systems for complex tasks, implementing human oversight in autonomous systems, creating reliable agentic applications at scale. Of those, building production ai agents with auditable workflows and designing multi-agent systems for complex tasks are not what Seldon is typically brought in for.
- What can LangGraph do that Seldon cannot?
- LangGraph covers Human-in-the-loop controls, Customizable workflows, Memory management, Token-by-token streaming. Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol.
Answered from the vendors’ own pages
LangGraph: Is LangGraph free to use?
Yes. The core LangGraph framework is MIT-licensed and completely free. You only pay if you use the optional managed LangGraph Platform for hosting.
SourceSeldon: 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.
LangGraph: What programming languages does LangGraph support?
LangGraph provides first-class support for Python and JavaScript, enabling cross-platform agent development.
SourceSeldon: 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.
LangGraph: Can I deploy LangGraph in production?
Yes. LangGraph can be self-hosted on your own infrastructure or deployed through LangGraph Platform with enterprise support and SLA guarantees.
SourceSeldon: 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.
Related pages
Other head to heads
- LangGraph vs AutoGen
- LangGraph vs Together AI
- LangGraph vs Stable Diffusion
- LangGraph vs Aider
- LangGraph vs Helicone
- LangGraph vs Gumloop
- LangGraph vs Deepgram
- LangGraph vs Voiceflow
- LangGraph vs Cartesia
- LangGraph vs Replicate
- LangGraph vs Tabnine
- LangGraph vs C3 AI Suite
- LangGraph vs Black Forest Labs
- LangGraph vs Ideogram
- LangGraph vs Jasper
- LangGraph vs Lindy
- LangGraph vs AWS SageMaker
- LangGraph vs DataRobot
- LangGraph vs Azure Machine Learning
- LangGraph vs Google Vertex AI
- LangGraph vs BentoML
- LangGraph vs Kubeflow
- LangGraph vs Pachyderm
- LangGraph vs MLflow
- LangGraph vs DVC
- LangGraph vs Weights & Biases
- LangGraph vs Comet ML
- LangGraph vs Dataiku
- LangGraph vs Anaconda
- LangGraph vs Domino Data Lab
- LangGraph vs H2O.ai
- LangGraph vs Hugging Face
- Seldon vs AutoGen
- Seldon vs Together AI
- Seldon vs Stable Diffusion
- Seldon vs Aider
- Seldon vs Helicone
- Seldon vs Gumloop
- Seldon vs Deepgram
- Seldon vs Voiceflow
- Seldon vs Cartesia
- Seldon vs Replicate
- Seldon vs Tabnine
- Seldon vs C3 AI Suite
- Seldon vs Black Forest Labs
- Seldon vs Ideogram
- Seldon vs Jasper
- Seldon vs Lindy
- Seldon vs AWS SageMaker
- Seldon vs DataRobot
- Seldon vs Azure Machine Learning
- Seldon vs Google Vertex AI
- Seldon vs BentoML
- Seldon vs Kubeflow
- Seldon vs Pachyderm
- Seldon vs MLflow
- Seldon vs DVC
- Seldon vs Weights & Biases
- Seldon vs Comet ML
- Seldon vs Dataiku
- Seldon vs Anaconda
- Seldon vs Domino Data Lab
- Seldon vs H2O.ai
- Seldon vs Hugging Face