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Machine Learning · head to head

Milvus vs Vespa

Milvus logo

Milvus

Machine Learning

Open-source vector database for scalable similarity search

From
Free
Rated
-
Full reviewWebsite
Vespa logo

Vespa

Databases

Distributed AI search platform for retrieval, ranking, and inference

From
Free
Rated
-
Full reviewWebsite

The short version

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

Where they differ

Only the attributes on which Milvus and Vespa actually diverge.

Attributes where Milvus and Vespa differ
AttributeMilvusVespa
Pricing modelfreemiumcontact-sales
PlatformsLinux, Mac, Windows, WebCloud, Self-hosted
CategoryMachine LearningDatabases
Founded20172023

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 Milvus

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

Only in Vespa

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

What people use each for

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

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

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

Where each one falls short

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

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

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

Milvus

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

Vespa

Free

No published plan breakdown. See the Vespa review.

Which should you pick?

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

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

Questions people ask

Is Milvus or Vespa better?
Neither clearly leads. Milvus 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, Milvus or Vespa?
Milvus starts at Free and Vespa at Free.
Does Milvus or Vespa run on more platforms?
Milvus runs on Linux, Mac, Windows, Web. Vespa runs on Cloud, Self-hosted.
Can I use Milvus for free?
Both have a free tier, so you can try either at no cost before committing.
What is Milvus best used for?
Milvus is most often used for self hosting a vector database for semantic search, storing and querying embeddings for retrieval augmented generation, similarity search over images, audio or text at scale. Of those, self hosting a vector database for semantic search and storing and querying embeddings for retrieval augmented generation are not what Vespa is typically brought in for.
What can Milvus do that Vespa cannot?
Milvus covers Billion-scale vectors, Multiple index types, GPU acceleration, Hybrid search. Vespa covers Vector search, Text and structured search, Machine-learned ranking, Real-time serving.

Answered from the vendors’ own pages

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: 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: 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: 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: 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: 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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