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Databases · head to head

Vespa vs Milvus

Vespa logo

Vespa

Databases

Distributed AI search platform for retrieval, ranking, and inference

From
Free
Rated
-
Full reviewWebsite
Milvus logo

Milvus

Machine Learning

Open-source vector database for scalable similarity search

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Vespa pricing not publicly listed, requires contacting sales; Milvus vector dimensions are capped at 32,768
  • They diverge on capability: Vespa covers Vector search, Milvus covers Billion-scale vectors.

Where they differ

Only the attributes on which Vespa and Milvus actually diverge.

Attributes where Vespa and Milvus differ
AttributeVespaMilvus
Pricing modelcontact-salesfreemium
PlatformsCloud, Self-hostedLinux, Mac, Windows, Web
CategoryDatabasesMachine Learning
Founded20232017

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 Vespa

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

Only in Milvus

  • Billion-scale vectors
  • Multiple index types
  • GPU acceleration
  • Hybrid search
  • Data partitioning
  • PyTorch
  • TensorFlow
  • Hugging Face

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

Milvus

  • Self hosting a vector database for semantic searchnot Vespa
  • Storing and querying embeddings for retrieval augmented generationnot Vespa
  • Similarity search over images, audio or text at scalenot 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

Milvus

  • Vector dimensions are capped at 32,768
  • A collection is limited to 64 fields, 1,024 partitions and 16 shards
  • Only 1 index is allowed per field
  • Search returns at most 16,384 vectors as top-k, and nq is capped at 16,384
  • Input and output per RPC is capped at 64 MB for insert, search and query
  • VARCHAR values are limited to 65,535 characters
  • Data loaded into query nodes cannot exceed 90% of available memory
  • An instance supports at most 65,536 collections

Pricing, plan by plan

Vespa

Free

No published plan breakdown. See the Vespa review.

Milvus

Free
  • Open SourceFree
    • Full features
    • Self-hosted
    • Community support
  • Zilliz CloudFree
    • Managed service
    • Free tier available

Which should you pick?

Choose Vespa if

  • You need vector search.
  • You want to start without paying.
  • You work on Cloud, Self-hosted.
  • You also want text and structured search.
Vespa review

Choose Milvus if

  • You need billion-scale vectors.
  • You want to start without paying.
  • You work on Linux, Mac, Windows, Web.
  • You also want multiple index types.
Milvus review

Questions people ask

Is Vespa or Milvus better?
Neither clearly leads. Vespa starts at Free and Milvus at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Vespa or Milvus?
Vespa starts at Free and Milvus at Free.
Does Vespa or Milvus run on more platforms?
Vespa runs on Cloud, Self-hosted. Milvus runs on Linux, Mac, Windows, Web.
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 Milvus is typically brought in for.
What can Vespa do that Milvus cannot?
Vespa covers Vector search, Text and structured search, Machine-learned ranking, Real-time serving. Milvus covers Billion-scale vectors, Multiple index types, GPU acceleration, Hybrid 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
Milvus: How much does Milvus cost?

Milvus is open-source and free to use and modify. The self-hosted version has no licensing cost. Zilliz Cloud (the managed SaaS version) does not publish pricing on the website.

Source
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
Milvus: Is there a free or open-source version of Milvus?

Yes, Milvus is fully open-source and available for free. Milvus Lite is a lightweight option for learning and prototyping that can be installed via pip.

Source
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
Milvus: Does Milvus offer a managed cloud service?

Yes, Zilliz Cloud is a fully managed Milvus cloud offering with serverless and dedicated cluster options. Pricing must be requested from the company as it is not listed on the public website.

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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