Vector Databases

Amazon OpenSearch Service vs Qdrant

Compare Amazon OpenSearch Service and Qdrant side-by-side. See feature differences and pricing in the Vector Databases category.

Amazon OpenSearch Service

by Amazon

No reviews yet

Pricing: Usage-based in US East (Ohio): clusters from $0.036 per instance-hour plus storage; Serverless $0.24 per OCU-hour plus $0.30 per GB-month. Rates vary by Region.

Amazon OpenSearch Service is an AWS-managed search, log analytics and vector database, run as sized clusters or as usage-billed OpenSearch Serverless.

15 /18 features Full profile

Qdrant

by Qdrant

No reviews yet

Pricing: Free Tier on a single-node cluster; Standard is usage-based, billed hourly for compute, memory and storage; Premium, Hybrid Cloud and Private Cloud are priced on request.

Qdrant is an open source vector database with managed, hybrid and private cloud editions, for teams building RAG, semantic search and agent memory.

16 /18 features Full profile

Feature Comparison

Vector Databases features side-by-side

Feature	Amazon OpenSearch Service 15/18 features	Qdrant 16/18 features
Approximate nearest-neighbor index An HNSW, IVF or similar index serves top-k similarity queries in milliseconds over millions of vectors.	Yes	Yes
Filtered vector search on metadata Structured filters on payload fields applied inside the similarity search, not after it.	Yes	Yes
Hybrid keyword and vector search Sparse or keyword signals combined with dense embeddings in one ranked query.	Yes	Yes
Choice of similarity metrics Cosine, dot product and Euclidean distance selectable per collection or index.	Yes	Yes
Quantized index compression Scalar, product or binary quantization shrinks index memory with a tunable recall trade-off.	Yes	Yes
Multi-tenant namespaces per index Tenant data isolated inside one index so a per-customer query never scans other tenants.	No	Yes
Integrated embedding models Text or images embedded by a hosted model at write and query time, with no separate pipeline.	Yes	Yes
Built-in reranking stage A second-stage reranker model reorders the top candidates before results are returned.	No	Yes
Multiple named vectors per record Several vectors per record, such as text and image embeddings, searchable together.	No	Yes
Live upserts visible in seconds New or updated vectors become searchable within seconds while the index stays online.	Yes	Yes
Sharded vector collections Collections split across nodes for datasets beyond one machine's memory.	Yes	Yes
Replicated vector shards Each shard is copied to another node so queries keep answering through a node loss.	Yes	Yes
Disk-backed index beyond RAM Indexes served from SSD rather than memory so billions of vectors fit on modest hardware.	Yes	Yes
Collection snapshots and restore Point-in-time snapshots of a collection that can be restored or copied to another cluster.	Yes	Yes
Role-based keys per collection API keys or roles scoped to specific collections and to read or write operations.	Yes	Yes
Bulk import from Parquet files Vectors and metadata loaded in bulk from Parquet or JSON files in cloud storage.	Yes	No
Serverless usage-based pricing Billing by reads, writes and stored vectors, with no cluster to size in advance.	Yes	No
Open source self-hosted engine The engine's source is public and can be self-hosted without a commercial license.	Yes	Yes
Overall Rating	No reviews yet	No reviews yet

Amazon OpenSearch Service 15/18 features

Approximate nearest-neighbor index: Yes
Filtered vector search on metadata: Yes
Hybrid keyword and vector search: Yes
Choice of similarity metrics: Yes
Quantized index compression: Yes
Multi-tenant namespaces per index: No
Integrated embedding models: Yes
Built-in reranking stage: No
Multiple named vectors per record: No
Live upserts visible in seconds: Yes
Sharded vector collections: Yes
Replicated vector shards: Yes
Disk-backed index beyond RAM: Yes
Collection snapshots and restore: Yes
Role-based keys per collection: Yes
Bulk import from Parquet files: Yes
Serverless usage-based pricing: Yes
Open source self-hosted engine: Yes

No reviews yet

Qdrant 16/18 features

Approximate nearest-neighbor index: Yes
Filtered vector search on metadata: Yes
Hybrid keyword and vector search: Yes
Choice of similarity metrics: Yes
Quantized index compression: Yes
Multi-tenant namespaces per index: Yes
Integrated embedding models: Yes
Built-in reranking stage: Yes
Multiple named vectors per record: Yes
Live upserts visible in seconds: Yes
Sharded vector collections: Yes
Replicated vector shards: Yes
Disk-backed index beyond RAM: Yes
Collection snapshots and restore: Yes
Role-based keys per collection: Yes
Bulk import from Parquet files: No
Serverless usage-based pricing: No
Open source self-hosted engine: Yes

No reviews yet

Pricing

Amazon OpenSearch Service

Usage-based in US East (Ohio): clusters from $0.036 per instance-hour plus storage; Serverless $0.24 per OCU-hour plus $0.30 per GB-month. Rates vary by Region.

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Qdrant

Free Tier on a single-node cluster; Standard is usage-based, billed hourly for compute, memory and storage; Premium, Hybrid Cloud and Private Cloud are priced on request.

See all plans

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