OriginChainDB vs Milvus
Milvus or
OriginChainDB?
Milvus supports vector search, filters and full-text search. OriginChainDB also provides SQL and graph queries. Test the full job your app needs to do.
A task to try
Start here
Which fits your app?
Our view: choose based on what your app needs to do.
Your app needs SQL records and relationships as well as search, using separate calls.
Explore the databaseSide by side
Compare the basics.
Features from the linked docs. Test speed and cost with your own data.
Scroll sideways to compare both databases.
| What to compare | Milvus | OriginChainDB |
|---|---|---|
| Search | Supports vector similarity, field filters, multiple vector fields and BM25 text search. Source: Milvus overview and deployment options | Search by meaning or words, then use IDs to find the source records. |
| Other queries | Fetch records by key or use filter expressions alongside vector search. Source: Milvus overview and deployment options | Use SQL for records and the graph API for relationships. Combine results in your app. |
| Where it runs | Choose Lite in Python, Standalone on one machine or Distributed on Kubernetes. Source: Milvus overview and deployment options | Choose a shared or dedicated setup and check its recovery options. |
Try it with your data
Find an image you can use.
Try the same task in both systems. Use the same data and judge the full result.
The question
Which similar images are licensed for this campaign?
- 01
Use the same images
Keep the same embeddings, image IDs and licence details in both tests.
- 02
Apply the filters
Search only images the campaign can use. Include cases with very few allowed images.
- 03
Open the result
Find the source file. Check that its licence is still valid and the file still exists.
What to check
Useful results
Check the matches against a set your team has reviewed.
Filtered results
Check quality when most images are filtered out.
Total cost
Include embeddings, storage, indexing and record reads.
Before you move data
Plan your move.
Copy the data. Then check your queries, access rules and updates before switching.
-
Keep vector settings
Record the embedding model, dimensions and distance metric. A new model may require new embeddings.
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Map fields and IDs
Decide where records, search fields and relationships belong. Preserve the IDs that connect them.
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Compare the results
Replay real searches and updates. Check rankings and filters before switching.
Common questions.
Does Milvus support text search as well as vectors?
Yes. Milvus documents BM25 full-text search and hybrid search across vector fields. Include these in your test.
Is a vector benchmark enough?
Only if vector search is the whole job. Otherwise, include record reads, permissions, relationships and operating costs.
Sources
Check the details.
Reviewed
Features vary by version and setup. Check the docs for the version you plan to use.
Build with OriginChainDB