Databases · head to head
OpenSearch vs Seldon

OpenSearch
Databases
Open-source search and analytics suite forked from Elasticsearch
- 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: OpenSearch diverged from Elasticsearch since 7.10, so clients, plugins and features no longer map one to one; 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: OpenSearch covers Full-text search, Seldon covers Kubernetes custom resources.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which OpenSearch and Seldon actually diverge.
| Attribute | OpenSearch | Seldon |
|---|---|---|
| Pricing model | Open source, no licence fee; managed services billed separately | freemium |
| Platforms | Linux, Docker, Kubernetes, Self-hosted | Linux |
| Category | Databases | Machine Learning |
| Founded | Unknown | 2014 |
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 OpenSearch
- Full-text search
- OpenSearch Dashboards
- Log analytics
- Vector search
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.
OpenSearch
- Log and observability storage where an Apache-2.0 licence is a requirementnot Seldon
- Replacing Elasticsearch after the licence change without changing architecturenot Seldon
- Search plus analytics on one cluster rather than two systemsnot Seldon
Seldon
- Serving an ensemble or a multi-stage inference path as one versioned deployment rather than as a chain of separate servicesnot OpenSearch
- Running genuine production experiments where a share of live traffic goes to a candidate model and the results are comparednot OpenSearch
- Regulated environments needing explanations and drift monitoring attached to the served model rather than bolted on laternot OpenSearch
- Organisations with an established Kubernetes platform team who want serving expressed as manifests under existing deployment controlsnot OpenSearch
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
OpenSearch
- Diverged from Elasticsearch since 7.10, so clients, plugins and features no longer map one to one
- Operationally heavy in the way Elasticsearch is: cluster sizing, shard strategy and JVM tuning are ongoing work
- Smaller ecosystem of third-party tooling than Elasticsearch, which most integrations still target first
- Overkill for plain application search, where a dedicated search engine is far simpler
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
OpenSearch
Free- OpenSearchFree
- Full functionality
- Self-hosted
- No usage limits
Seldon
Free- Seldon CoreFree
- Open source
- Kubernetes deployment
- Model serving
- Seldon DeployFree
- Enterprise features
- GUI
- Monitoring
Which should you pick?
Choose OpenSearch if
- You need full-text search.
- You want to start without paying.
- You work on Linux, Docker, Kubernetes, Self-hosted.
- You also want opensearch dashboards.
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 OpenSearch or Seldon better?
- Neither clearly leads. OpenSearch 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, OpenSearch or Seldon?
- OpenSearch starts at Free and Seldon at Free.
- Does OpenSearch or Seldon run on more platforms?
- OpenSearch runs on Linux, Docker, Kubernetes, Self-hosted. Seldon runs on Linux.
- Can I use OpenSearch for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is OpenSearch best used for?
- OpenSearch is most often used for log and observability storage where an apache-2.0 licence is a requirement, replacing elasticsearch after the licence change without changing architecture, search plus analytics on one cluster rather than two systems. Of those, log and observability storage where an apache-2.0 licence is a requirement and replacing elasticsearch after the licence change without changing architecture are not what Seldon is typically brought in for.
- What can OpenSearch do that Seldon cannot?
- OpenSearch covers Full-text search, OpenSearch Dashboards, Log analytics, Vector search. Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol.
Answered from the vendors’ own pages
OpenSearch: Is OpenSearch free?
Yes, Apache 2.0 licensed under the Linux Foundation. Amazon OpenSearch Service is a paid managed option.
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.
OpenSearch: Why does OpenSearch exist?
Elastic moved Elasticsearch off the Apache 2.0 licence in 2021. AWS forked the last Apache-licensed version, and the project now sits under the Linux Foundation.
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.
OpenSearch: Is OpenSearch compatible with Elasticsearch?
It was at the 7.10 fork point. Both have developed independently since, so compatibility weakens with every release and should be verified for the features you use.
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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