Qdrant vs Vespa

Qdrant9.3Visit Vespa8.8Visit

Higher score Qdrant #2 in Vector Database Hosting 9.3/10 Free plan Free plan✓ 2 of 3 features Visit Qdrant

vs

Vespa #6 in Vector Database Hosting 8.8/10 Paid ✓ 2 of 3 features Visit Vespa

• Qdrant fits best

  Open-source vector search at scale

• Vespa fits best

  Feature-rich open-source search platforms

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Side by side

Feature	Qdrant 9.3/10 Visit ↗	Vespa 8.8/10 Visit ↗
At a glance
Editor score	9.3	8.8
Ranking	#2 in Vector Database Hosting	#6 in Vector Database Hosting
Best for	Open-source vector search at scale	Feature-rich open-source search platforms
Pricing model	Free plan + paid	Paid
Starting price	—	$0.05/unit usage-based
Free plan	✓ (best)	—
Free trial	—	—
Deployment	Cloud, Self-hosted	Cloud, Self-hosted
Platforms	Web, Windows, macOS, Linux	Linux
Support	Email, Tickets, Community, Docs	Email, Community, Docs
Built for	Small business, Mid-market, Enterprise	Small business, Mid-market, Enterprise
Features Qdrant 2/3 · Vespa 2/3
Hybrid search	✓	✓
Metadata filtering	✓	✓
Serverless scaling	Not checked yet	Not checked yet
Specs
Included storage	Not checked yet	Not checked yet
Deployment regions	Not checked yet	Not checked yet
Maximum dimensions	65535 dimensions	Not checked yet
Our review
Pros	Supports dense, sparse, hybrid, and multivector retrieval Offers self-hosted and managed cloud deployment Includes REST, gRPC, and multiple language SDKs	Combines dense, lexical, hybrid, and structured search Supports real-time updates, ranking, inference, and distributed scaling Available through Vespa Cloud or self-managed deployment
Cons	The Free Tier is limited to a single-node cluster Free resources are limited to 0.5 vCPU, 1 GB RAM, and 4 GB disk SSO and private VPC links are reserved for the Premium Tier	No free plan is available Startup uses shared resources and has no autoscaling Paid pricing is resource-based rather than a fixed monthly fee
Our verdict	Qdrant is an open-source vector search engine and vector database for developers building semantic search, recommendations, retrieval-augmented generation, and other AI applications. It stores dense, sparse, and multivector representations… Read the review →	Vespa is a distributed AI search and serving platform for teams building search, recommendation, personalization, navigation, and retrieval-augmented generation applications. It brings vector and tensor search, lexical text search with… Read the review →

Strengths and trade-offs

• Qdrant — where it wins

  • Supports dense, sparse, hybrid, and multivector retrieval
  • Offers self-hosted and managed cloud deployment
  • Includes REST, gRPC, and multiple language SDKs

  Where it doesn't

  • The Free Tier is limited to a single-node cluster
  • Free resources are limited to 0.5 vCPU, 1 GB RAM, and 4 GB disk
  • SSO and private VPC links are reserved for the Premium Tier

• Vespa — where it wins

  • Combines dense, lexical, hybrid, and structured search
  • Supports real-time updates, ranking, inference, and distributed scaling
  • Available through Vespa Cloud or self-managed deployment

  Where it doesn't

  • No free plan is available
  • Startup uses shared resources and has no autoscaling
  • Paid pricing is resource-based rather than a fixed monthly fee

• Qdrant9.3/10 · Free plan

  A flexible vector database for semantic, hybrid, and filtered search at scale.

  Visit QdrantFull verdict →
• Vespa8.8/10 · Paid

  A broad open-source platform for teams building advanced search and retrieval systems.

  Visit VespaFull verdict →