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Databases · head to head

OpenSearch vs Vespa

OpenSearch logo

OpenSearch

Databases

Open-source search and analytics suite forked from Elasticsearch

From
Free
Rated
-
Vespa logo

Vespa

Databases

Distributed AI search platform for retrieval, ranking, and inference

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; Vespa pricing not publicly listed, requires contacting sales
  • They diverge on capability: OpenSearch covers Full-text search, Vespa covers Text and structured search.

Where they differ

Only the attributes on which OpenSearch and Vespa actually diverge.

Attributes where OpenSearch and Vespa differ
AttributeOpenSearchVespa
Pricing modelOpen source, no licence fee; managed services billed separatelycontact-sales
PlatformsLinux, Docker, Kubernetes, Self-hostedCloud, Self-hosted
FoundedUnknown2023

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Databases).

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

Only in Vespa

  • Text and structured search
  • Machine-learned ranking
  • Real-time serving
  • SQL interface
  • Automatic scaling
  • Open-source

Both cover

  • Vector search

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 Vespa
  • Replacing Elasticsearch after the licence change without changing architecturenot Vespa
  • Search plus analytics on one cluster rather than two systemsnot Vespa

Vespa

  • Build RAG systems with semantic search over documentsnot OpenSearch
  • Power e-commerce search with ML rankingnot OpenSearch
  • Create recommendation engines for personalizationnot OpenSearch
  • Implement real-time search for news or feedsnot OpenSearch
  • Deploy private semantic search over sensitive datanot 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

Vespa

  • Pricing not publicly listed, requires contacting sales
  • Steeper learning curve compared to simpler search tools
  • Operational complexity for self-hosted deployments
  • Smaller ecosystem compared to cloud-native alternatives

Pricing, plan by plan

OpenSearch

Free
  • OpenSearchFree
    • Full functionality
    • Self-hosted
    • No usage limits

Vespa

Free

No published plan breakdown. See the Vespa review.

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 Vespa if

  • You need text and structured search.
  • You want to start without paying.
  • You work on Cloud, Self-hosted.
  • You also want machine-learned ranking.

Questions people ask

Is OpenSearch or Vespa better?
Neither clearly leads. OpenSearch starts at Free and Vespa at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, OpenSearch or Vespa?
OpenSearch starts at Free and Vespa at Free.
Does OpenSearch or Vespa run on more platforms?
OpenSearch runs on Linux, Docker, Kubernetes, Self-hosted. Vespa runs on Cloud, Self-hosted.
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 Vespa is typically brought in for.
What can OpenSearch do that Vespa cannot?
OpenSearch covers Full-text search, OpenSearch Dashboards, Log analytics. Vespa covers Text and structured search, Machine-learned ranking, Real-time serving, SQL interface. Both handle Vector search.

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.

Vespa: Is Vespa open-source?

Yes, Vespa is open-source under the Apache 2.0 license. The code is available on GitHub, and you can self-host or use the managed cloud service.

Source
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.

Vespa: What latency can Vespa achieve?

Vespa is designed for sub-100 millisecond latencies with thousands of queries per second, suitable for real-time search and recommendation applications.

Source
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.

Vespa: Does Vespa support vector search?

Yes, Vespa provides native vector search capabilities alongside text, structured data, and tensor operations for building comprehensive search and AI applications.

Source
Vespa: What is the pricing model for Vespa Cloud?

Vespa Cloud pricing is not publicly listed and requires contacting their sales team to discuss your specific use case and scale requirements.

Source
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