Machine Learning · head to head
Haystack vs Seldon
Haystack
Machine Learning
Open-source AI orchestration framework for LLM applications
- From
- Free
- Rated
- -
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: Haystack requires Python programming knowledge for advanced customization; 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: Haystack covers Modular pipeline composition, Seldon covers Kubernetes custom resources.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Haystack and Seldon actually diverge.
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 Haystack
- Modular pipeline composition
- Multi-provider LLM support
- Retrieval-augmented generation
- Agent framework
- Memory management
- Observability and debugging
- Kubernetes-ready deployment
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.
Haystack
- Building production LLM applications with full controlnot Seldon
- Creating retrieval-augmented generation systemsnot Seldon
- Developing autonomous AI agentsnot Seldon
- Multi-provider LLM orchestrationnot Seldon
- Enterprise AI infrastructurenot Seldon
Seldon
- Serving an ensemble or a multi-stage inference path as one versioned deployment rather than as a chain of separate servicesnot Haystack
- Running genuine production experiments where a share of live traffic goes to a candidate model and the results are comparednot Haystack
- Regulated environments needing explanations and drift monitoring attached to the served model rather than bolted on laternot Haystack
- Organisations with an established Kubernetes platform team who want serving expressed as manifests under existing deployment controlsnot Haystack
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Haystack
- Requires Python programming knowledge for advanced customization
- Steeper learning curve compared to no-code platforms
- Community support only on free tier may limit enterprise adoption
- Ongoing maintenance dependency for open-source framework
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
Haystack
Free- Open SourceFree
- Full framework access
- Community Discord support
- GitHub community contributions
- Enterprise Support$undefined/custom
- Private secure engineering support
- Best practices templates and deployment guides
- Flexible services and integrations
Seldon
Free- Seldon CoreFree
- Open source
- Kubernetes deployment
- Model serving
- Seldon DeployFree
- Enterprise features
- GUI
- Monitoring
Which should you pick?
Choose Haystack if
- You need modular pipeline composition.
- You want to start without paying.
- You work on Python, Cloud-agnostic.
- You also want multi-provider llm support.
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 Haystack or Seldon better?
- Neither clearly leads. Haystack 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, Haystack or Seldon?
- Haystack starts at Free and Seldon at Free.
- Does Haystack or Seldon run on more platforms?
- Haystack runs on Python, Cloud-agnostic. Seldon runs on Linux.
- Can I use Haystack for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Haystack best used for?
- Haystack is most often used for building production llm applications with full control, creating retrieval-augmented generation systems, developing autonomous ai agents, multi-provider llm orchestration. Of those, building production llm applications with full control and creating retrieval-augmented generation systems are not what Seldon is typically brought in for.
- What can Haystack do that Seldon cannot?
- Haystack covers Modular pipeline composition, Multi-provider LLM support, Retrieval-augmented generation, Agent framework. Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol.
Answered from the vendors’ own pages
Haystack: Is Haystack completely free to use?
Yes, the open-source Haystack framework is completely free. deepset offers optional paid enterprise support packages for organizations needing secure engineering support and deployment guidance.
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.
Haystack: What LLM providers does Haystack support?
Haystack supports multiple LLM providers including OpenAI, Anthropic, Mistral, Cohere, and others, allowing teams to avoid vendor lock-in and switch providers as needed.
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.
Haystack: Can I deploy Haystack in production environments?
Yes, Haystack is designed for production use with Kubernetes-ready pipelines, built-in reliability features, and observability tools for enterprise-scale deployments.
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.
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