Softwr

Databases · head to head

Vespa vs OpenSearch

Vespa logo

Vespa

Databases

Distributed AI search platform for retrieval, ranking, and inference

From
Free
Rated
-
Full reviewWebsite
OpenSearch logo

OpenSearch

Databases

Open-source search and analytics suite forked from Elasticsearch

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Vespa pricing not publicly listed, requires contacting sales; OpenSearch diverged from Elasticsearch since 7.10, so clients, plugins and features no longer map one to one
  • They diverge on capability: Vespa covers Text and structured search, OpenSearch covers Full-text search.

Where they differ

Only the attributes on which Vespa and OpenSearch actually diverge.

Attributes where Vespa and OpenSearch differ
AttributeVespaOpenSearch
Pricing modelcontact-salesOpen source, no licence fee; managed services billed separately
PlatformsCloud, Self-hostedLinux, Docker, Kubernetes, Self-hosted
Founded2023Unknown

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 Vespa

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

Only in OpenSearch

  • Full-text search
  • OpenSearch Dashboards
  • Log analytics

Both cover

  • Vector search

What people use each for

The jobs each tool is most often brought in to do.

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

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

Where each one falls short

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

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

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

Pricing, plan by plan

Vespa

Free

No published plan breakdown. See the Vespa review.

OpenSearch

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

Which should you pick?

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.
Vespa review

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.
OpenSearch review

Questions people ask

Is Vespa or OpenSearch better?
Neither clearly leads. Vespa starts at Free and OpenSearch at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Vespa or OpenSearch?
Vespa starts at Free and OpenSearch at Free.
Does Vespa or OpenSearch run on more platforms?
Vespa runs on Cloud, Self-hosted. OpenSearch runs on Linux, Docker, Kubernetes, Self-hosted.
Can I use Vespa for free?
Both have a free tier, so you can try either at no cost before committing.
What is Vespa best used for?
Vespa is most often used for build rag systems with semantic search over documents, power e-commerce search with ml ranking, create recommendation engines for personalization, implement real-time search for news or feeds. Of those, build rag systems with semantic search over documents and power e-commerce search with ml ranking are not what OpenSearch is typically brought in for.
What can Vespa do that OpenSearch cannot?
Vespa covers Text and structured search, Machine-learned ranking, Real-time serving, SQL interface. OpenSearch covers Full-text search, OpenSearch Dashboards, Log analytics. Both handle Vector search.

Answered from the vendors’ own pages

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: Is OpenSearch free?

Yes, Apache 2.0 licensed under the Linux Foundation. Amazon OpenSearch Service is a paid managed option.

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: 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: 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
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: 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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