vs

Faiss vs Milvus

Faiss: Meta's C++/Python library for fast dense-vector similarity search and clustering, CPU and GPU. Milvus: Cloud-native, distributed open-source vector database built for billions of vectors.

key differences

  • Milvus is built for larger datasets (Multi-PB) than Faiss (≤ 1 PB).
  • Horizontal scaling is manual in Faiss and native in Milvus.
  • Only Faiss has in-memory.
  • Only Milvus has secondary indexes, full-text search, json, change data capture, ttl / expiry.

choose faiss for

  • +Embedding similarity search inside ML pipelines
  • +Offline deduplication and clustering
  • +Building custom vector search services
  • +ANN research and benchmarking

best at · Vector search, Embedded, Scientific

choose milvus for

  • +Billion-scale semantic and image search
  • +RAG platforms serving many teams
  • +Recommendation and deduplication
  • +Multimodal retrieval over lakehouse data

best at · Vector search, RAG, Search

overview
Primary model
Vector
Vector
Also supports
–
Search Engine, Document
Type
Embedded library
Database engine
Status
Active
Active
Developer
Meta (FAIR)
LF AI & Data Foundation (developed mainly by Zilliz)
First release
2017
2019
Latest version
1.15.1 (Sep 16, 2026)
3.0.4 (Oct 26, 2026)
License
Open source (permissive)MIT License
Open source (permissive)Apache License 2.0
Free tier
✓
✓
popularity
WhichDB Score
44.7
45.9
Rank
#40
#35
GitHub stars
41.2K
46.5K
Stack Overflow questions
134
208
scale & guarantees
Comfortable data size
Large (≤ 1 PB)
Massive (Multi-PB)
Horizontal scaling
Manual
Native
Multi-region
None
None
ACID
None
None
Consistency
Strong
Tunable, Strong, Eventual
Schema
Schemaless
Strict
Architecture
Embedded, In memory
Distributed, Shared storage, Embedded, Client server
deployment
Self-hosted
✓
✓
Vendor-managed cloud
—
✓
Serverless
—
—
Embedded
✓
✓
Edge
—
—
Clouds
–
AWS, Google Cloud, Azure
capabilities
Transactions
—
—
Joins
—
—
Secondary indexes
—
✓
Full-text search
—
✓
Vector search
✓
✓
Geospatial
—
—
JSON
—
✓
Change data capture
—
✓
Triggers
—
—
Stored procedures
—
—
Materialized views
—
—
TTL / expiry
—
✓
Pub/Sub
—
—
Columnar storage
—
—
In-memory
✓
—
Durable persistence
—
✓
Compression
✓
✓
Encryption at rest
—
—
Role-based access
—
✓
Time travel
—
—
Branching
—
—
Online schema changes
—
✓
Streaming ingest
—
✓
Offline sync
—
—
Runs in WebAssembly
—
—
interfaces
Query languages
C++ API, Python API
Milvus API (boolean filter expressions)
Client languages
C++, Python, C
Python, Java, Go, Node.js, C#, Rust
Best for
Vector search, Embedded, Scientific
Vector search, RAG, Search

Faiss vs Milvus: FAQ

What is the difference between Faiss and Milvus?

Faiss: Meta's C++/Python library for fast dense-vector similarity search and clustering, CPU and GPU. Milvus: Cloud-native, distributed open-source vector database built for billions of vectors. Milvus is built for larger datasets (Multi-PB) than Faiss (≤ 1 PB). Horizontal scaling is manual in Faiss and native in Milvus. Only Faiss has in-memory.

When should I choose Faiss over Milvus?

Choose Faiss for embedding similarity search inside ML pipelines; offline deduplication and clustering; and building custom vector search services. Choose Milvus for billion-scale semantic and image search; RAG platforms serving many teams; and recommendation and deduplication.

Is Faiss or Milvus more popular?

Milvus ranks #35 on the WhichDB Score (45.9/100) and Faiss ranks #40 (44.7/100). Popularity is not fit: check the requirements that matter for your project.

Are Faiss and Milvus open source?

Yes. Faiss is open source. License: MIT License. Yes. Milvus is open source. License: Apache License 2.0.