Qdrant vs Amazon OpenSearch Service

Qdrant8.5Visit Amazon OpenSearch Service8.2Visit

Higher score Qdrant #5 in Ai Databases 8.5/10 Free plan Free plan✓ 2 of 3 features Visit Qdrant

vs

Amazon OpenSearch Service #8 in Ai Databases 8.2/10 Paid ✓ 2 of 3 features Visit AWS

• Qdrant fits best

  Teams needing cloud or self-hosted vector search

• Amazon OpenSearch Service fits best

  AWS users building managed vector search and RAG

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Side by side

Feature	Qdrant 8.5/10 Visit ↗	Amazon OpenSearch Service 8.2/10 Visit ↗
At a glance
Editor score	8.5	8.2
Ranking	#5 in Ai Databases	#8 in Ai Databases
Best for	Teams needing cloud or self-hosted vector search	AWS users building managed vector search and RAG
Pricing model	Free plan + paid	Paid
Starting price	—	Not checked yet
Free plan	✓ (best)	—
Free trial	—	—
Deployment	Cloud, Self-hosted	Cloud
Platforms	Web, Windows, macOS, Linux	Web
Support	Email, Tickets, Community, Docs	Live chat, Tickets, Docs
Compliance	SOC 2, HIPAA, GDPR	SOC 2, ISO 27001, HIPAA, PCI DSS, SSO/SAML
Built for	Small business, Mid-market, Enterprise	Small business, Mid-market, Enterprise
Features Qdrant 2/3 · Amazon OpenSearch Service 2/3
Metadata filtering	✓	✓
Hybrid search	✓	✓
Managed backups	Not checked yet	Not checked yet
Specs
Deployment	Not checked yet	Not checked yet
Index types	HNSW; sparse vector index; payload index; filterable HNSW	Not checked yet
Max vector dimensions	65535 (best)	16000
Our review
Pros	Supports dense, sparse, and hybrid vector search with metadata filtering Offers open-source self-hosting and managed cloud deployment Includes a free tier with cloud inference for selected models	Combines vector and lexical search, with metadata filtering. Offers serverless collections with automatic scaling and scale-to-zero. Connects with AWS services including S3, Kinesis, and Bedrock.
Cons	The free tier is limited to a single-node cluster with 1 GB RAM Standard pricing is usage-based rather than a fixed published amount Higher availability and SSO are reserved for paid tiers	Paid, usage-based pricing varies by deployment model and Region. Requires AWS ecosystem fit for its broadest integration value. Managed domains require choices for compute and storage configuration.
Our verdict	Qdrant is an open-source vector search engine and database for semantic search, retrieval-augmented generation, recommendations, and other AI applications. It suits teams that need to store vector representations alongside metadata and… Read the review →	For teams building search, analytics, observability, or AI applications on AWS, Amazon OpenSearch Service provides managed OpenSearch domains and serverless collections. Its vector features support k-nearest-neighbor retrieval, semantic… Read the review →

Strengths and trade-offs

• Qdrant — where it wins

  • Supports dense, sparse, and hybrid vector search with metadata filtering
  • Offers open-source self-hosting and managed cloud deployment
  • Includes a free tier with cloud inference for selected models

  Where it doesn't

  • The free tier is limited to a single-node cluster with 1 GB RAM
  • Standard pricing is usage-based rather than a fixed published amount
  • Higher availability and SSO are reserved for paid tiers

• Amazon OpenSearch Service — where it wins

  • Combines vector and lexical search, with metadata filtering.
  • Offers serverless collections with automatic scaling and scale-to-zero.
  • Connects with AWS services including S3, Kinesis, and Bedrock.

  Where it doesn't

  • Paid, usage-based pricing varies by deployment model and Region.
  • Requires AWS ecosystem fit for its broadest integration value.
  • Managed domains require choices for compute and storage configuration.

• Qdrant8.5/10 · Free plan

  A flexible vector database for teams building semantic search and AI retrieval.

  Visit QdrantFull verdict →
• Amazon OpenSearch Service8.2/10 · Paid

  A managed AWS option for teams combining vector retrieval with lexical search.

  Visit AWSFull verdict →