Milvus vs OpenSearch for Vector Search
Comparing Milvus and OpenSearch for large-scale vector search, covering architecture, scalability, performance, and operational considerations.
Milvus is a purpose-built vector database designed for billion-scale similarity search. OpenSearch is a search and analytics engine with vector search capabilities. When choosing between them, the decision often comes down to scale requirements and whether you need capabilities beyond vector search.
Architecture
Milvus is built as a cloud-native distributed system. It separates compute from storage, using object storage (S3) for persistence and a message queue (Pulsar, Kafka) for streaming. This architecture enables independent scaling of query and insertion workloads. Components include a proxy layer, query nodes, data nodes, and index nodes.
OpenSearch is a distributed search engine based on the Lucene library. Vector search is provided by the k-NN plugin, which supports HNSW, IVF, and Faiss-based algorithms. The architecture is a shared-nothing cluster where each node stores a portion of the data.
Scale Comparison
| Metric | Milvus | OpenSearch |
|---|---|---|
| Max vectors tested | Billions | Hundreds of millions |
| Distributed scaling | Cloud-native, compute-storage separation | Cluster scaling (add nodes) |
| Index types | IVF_FLAT, IVF_SQ8, IVF_PQ, HNSW, SCANN, DiskANN | HNSW, IVFFlat, IVFPQ, Faiss |
| GPU indexing | Yes | No |
| Disk-based indexing | Yes (DiskANN) | Limited |
Milvus has a clear advantage at very large scale. DiskANN enables billion-scale vector search without requiring all vectors to reside in memory. GPU-accelerated indexing dramatically speeds up index building for large datasets.
Feature Comparison
| Feature | Milvus | OpenSearch |
|---|---|---|
| Vector search | Core capability | Plugin (k-NN) |
| Full-text search | Basic (via integration) | Core capability |
| Hybrid search | Vector + scalar filtering | Vector + text + filtering |
| Analytics | No | Yes (aggregations, dashboards) |
| Schema enforcement | Yes (typed fields) | Flexible (dynamic mapping) |
| Multi-vector | Yes (multiple vector fields per entity) | Yes (multiple k-NN fields) |
| Partitioning | Yes (by partition key) | Yes (by index/shard) |
| Time travel queries | Yes (query historical state) | No |
OpenSearch provides significantly broader functionality beyond vector search. If you need full-text search, log analytics, dashboards, or aggregations alongside vector search, OpenSearch is the more complete platform.
Operational Complexity
Milvus has high operational complexity in its distributed mode. The architecture requires multiple components: etcd for metadata, MinIO/S3 for storage, Pulsar/Kafka for messaging, plus the Milvus components themselves. Managed options (Zilliz Cloud) eliminate this complexity but add cost.
Milvus Lite is an embedded mode for development and small-scale use. It simplifies getting started but is not suitable for production at scale.
OpenSearch Service on AWS is fully managed. AWS handles provisioning, patching, backups, and scaling. The operational burden is moderate and well-documented.
For teams without dedicated infrastructure engineers, OpenSearch Service is significantly easier to operate than self-hosted Milvus.
Performance
At moderate scale (under 10 million vectors), both perform well with proper configuration. At larger scale, Milvus’s purpose-built architecture and GPU support give it advantages in both indexing speed and query latency.
Milvus also supports more index types, allowing fine-tuning of the accuracy-performance tradeoff for specific workloads. DiskANN is particularly valuable for cost-effective search at billion scale.
Cost
Self-hosted Milvus: Infrastructure cost for multiple components. A minimal production cluster needs 3+ nodes plus supporting services. Higher infrastructure cost but no per-query charges.
Zilliz Cloud (managed Milvus): Managed service with usage-based pricing. Simpler operations but premium pricing.
OpenSearch Service: Instance-based pricing. A production cluster (3 data nodes, 3 master nodes) starts at ~$200-300/month. Includes all features (vector search, text search, analytics).
For pure vector search at moderate scale, the costs are comparable. At very large scale (100M+ vectors), Milvus’s efficient indexing and DiskANN can be more cost-effective per vector stored.
When to Choose Milvus
- Billion-scale vector search requirements
- Need GPU-accelerated indexing
- Need disk-based indexing for cost-effective large-scale search
- Building a dedicated similarity search service
- Team has infrastructure expertise to manage the system (or use Zilliz Cloud)
When to Choose OpenSearch
- Need combined vector search, full-text search, and analytics
- Already using OpenSearch for other purposes
- Want managed AWS service with standard operational practices
- Scale requirements are under 100 million vectors
- Need dashboards and visualization alongside vector search
For most enterprise AI applications, OpenSearch provides sufficient vector search capability alongside valuable additional features. Milvus is the right choice when vector search at massive scale is the primary requirement and the operational investment is justified.
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