Machine Learning · head to head
Milvus vs Vespa

Milvus
Machine Learning
Open-source vector database for scalable similarity search
- From
- Free
- Rated
- -

Vespa
Databases
Distributed AI search platform for retrieval, ranking, and inference
- From
- Free
- Rated
- -
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.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Milvus and Vespa actually diverge.
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
FreeNo 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.
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.
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.
SourceVespa: 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.
SourceMilvus: 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.
SourceVespa: 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.
SourceMilvus: 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.
SourceVespa: 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.
SourceVespa: 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.
SourceRelated pages
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- Vespa vs Azure Machine Learning
- Vespa vs DataRobot
- Vespa vs Pinecone
- Vespa vs Weaviate
- Vespa vs Ray
- Vespa vs Fal AI
- Vespa vs Jupyter
- Vespa vs Keras
- Vespa vs LangChain
- Vespa vs Weights & Biases
- Vespa vs Alteryx
- Vespa vs Anaconda
- Vespa vs Domino Data Lab
- Vespa vs DVC
- Vespa vs Semantic Kernel
- Vespa vs Zilliz
- Vespa vs Elasticsearch
- Vespa vs Meilisearch
- Vespa vs Typesense
- Vespa vs SurrealDB
- Vespa vs OpenSearch
- Vespa vs Apache Solr
- Vespa vs Marqo
- Vespa vs Chroma
- Vespa vs Qdrant
- Vespa vs Materialize
- Vespa vs Estuary
- Vespa vs Fivetran HVR
- Vespa vs Grist
- Vespa vs IBM Db2
- Vespa vs Instaclustr
- Vespa vs Knack
- Vespa vs LanceDB
