Actian VectorAI DB vs Qdrant

Actian VectorAI DB9.0Visit Qdrant8.7Visit

Higher score Actian VectorAI DB #4 in Vector Databases 9.0/10 Free plan · 30-day trial Free planFree trial✓ 2 of 3 features Visit Actian

vs

Qdrant #7 in Vector Databases 8.7/10 Free plan Free plan✓ 3 of 3 features Visit Qdrant

• Actian VectorAI DB fits best

  Teams needing broad deployment and multimodal inputs

• Qdrant fits best

  Modern teams wanting open hybrid search

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Side by side

Feature	Actian VectorAI DB 9.0/10 Visit ↗	Qdrant 8.7/10 Visit ↗
At a glance
Editor score	9.0	8.7
Ranking	#4 in Vector Databases	#7 in Vector Databases
Best for	Teams needing broad deployment and multimodal inputs	Modern teams wanting open hybrid search
Pricing model	Free plan + paid	Free plan + paid
Starting price	$417/mo	—
Free plan	✓	✓
Free trial	30 days	—
Deployment	Desktop, Self-hosted	Cloud, Self-hosted
Platforms	Linux, Windows, macOS	Web, Windows, macOS, Linux
Features Actian VectorAI DB 2/3 · Qdrant 3/3
Hybrid search	✓	✓
Metadata filtering	✓	✓
Sparse vectors	Not checked yet	✓ (best)
Specs
Deployment	Both	Both
Maximum dimensions	Not checked yet	65535 dimensions
Index types	HNSW, flat	HNSW; sparse vector index; payload index; filterable HNSW
SDK languages	Python, JavaScript, C++	Python, JavaScript/TypeScript, Rust, Go, .NET, Java
Our review
Pros	Dense, hybrid, filtered, and multimodal retrieval support Docker, Kubernetes, Helm, and Terraform deployment options Free Community plan plus a 30-day trial	Hybrid dense and sparse search with metadata filtering Open-source self-hosting plus managed cloud deployment Multivectors, named vectors, REST, and gRPC APIs
Cons	Paid pricing is listed for only Starter and Growth Community capacity is limited to 5K vectors Sparse BM25 scoring is client-side rather than native storage	The Free Tier is limited to a single node Free resources are limited to 0.5 vCPU, 1 GB RAM, and 4 GB disk Paid cloud tiers use usage-based pricing rather than fixed plan amounts
Our verdict	Actian VectorAI DB is a self-hosted vector database for semantic search, retrieval-augmented generation, recommendations, and AI agent memory. It supports local machines, servers, virtual machines, on-premises infrastructure, self-managed… Read the review →	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 supports self-hosted deployment and Qdrant Cloud,… Read the review →

Strengths and trade-offs

• Actian VectorAI DB — where it wins

  • Dense, hybrid, filtered, and multimodal retrieval support
  • Docker, Kubernetes, Helm, and Terraform deployment options
  • Free Community plan plus a 30-day trial

  Where it doesn't

  • Paid pricing is listed for only Starter and Growth
  • Community capacity is limited to 5K vectors
  • Sparse BM25 scoring is client-side rather than native storage

• Qdrant — where it wins

  • Hybrid dense and sparse search with metadata filtering
  • Open-source self-hosting plus managed cloud deployment
  • Multivectors, named vectors, REST, and gRPC APIs

  Where it doesn't

  • The Free Tier is limited to a single node
  • Free resources are limited to 0.5 vCPU, 1 GB RAM, and 4 GB disk
  • Paid cloud tiers use usage-based pricing rather than fixed plan amounts

• Actian VectorAI DB9.0/10 · Free plan · 30-day trial

  A flexible self-hosted vector database for multimodal and hybrid retrieval.

  Visit ActianFull verdict →
• Qdrant8.7/10 · Free plan

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

  Visit QdrantFull verdict →