Database & Data Management · head to head
Qdrant vs Dgraph
Qdrant
Database & Data Management
High-performance vector database for similarity search and embedding-based retrieval
- From
- Free
- Rated
- -
Dgraph
Database & Data Management
The only native GraphQL database with graph backend
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Qdrant free tier extremely limited (0.5 vCPU, 1GB RAM, 4GB disk); suitable only for experiments; Dgraph the GitHub repository (dgraph-io/dgraph) is licensed Apache 2.0 with no paid tier, cloud offering, or enterprise edition mentioned anywhere in the README
Where they differ
Only the attributes on which Qdrant and Dgraph actually diverge.
Identical on both: starting price (Free), pricing model (freemium), free tier (Yes), user rating (Not yet rated), category (Database & Data Management).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in Qdrant
Nothing recorded that Dgraph does not also cover.
Only in Dgraph
- Native GraphQL
- Distributed Architecture
- ACID Transactions
- GraphQL Subscriptions
- Full-text Search
- Geolocation Queries
- Horizontal Scaling
- GraphQL
What people use each for
The jobs each tool is most often brought in to do.
Qdrant
- Retrieval-augmented generation (RAG) backends for LLM applicationsnot Dgraph
- Semantic search across large document corporanot Dgraph
- Multimodal retrieval (text, images, video) for recommendation systemsnot Dgraph
- Similarity-based product or content recommendationsnot Dgraph
- Real-time vector indexing for streaming embedding datanot Dgraph
Dgraph
- Knowledge graphsnot Qdrant
- Fraud detectionnot Qdrant
- Recommendation enginesnot Qdrant
- Network analysisnot Qdrant
- Master data managementnot Qdrant
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Qdrant
- Free tier extremely limited (0.5 vCPU, 1GB RAM, 4GB disk); suitable only for experiments
- Standard and Premium pricing usage-based; specific costs not published; requires calculator or quote
- Requires understanding of embeddings and vector search concepts; not suitable for SQL-only teams
- Early-stage serverless offering (coming soon) suggests maturity gaps in that deployment model
Dgraph
- The GitHub repository (dgraph-io/dgraph) is licensed Apache 2.0 with no paid tier, cloud offering, or enterprise edition mentioned anywhere in the README
Pricing, plan by plan
Qdrant
Free- FreeFree
- Single-node cluster
- 0.5 vCPU
- 1GB RAM
- Standard$null/usage-based
- Dedicated resources
- Flexible scaling
- High availability
- Premium$null/minimum spend
- SSO and SAML
- Private VPC links
- 99.9% uptime SLA
Dgraph
Free- CommunityFree
- Native GraphQL
- Graph queries
- Full-text search
- Cloud$39/month
- Managed service
- Auto-scaling
- Enterprise support
Which should you pick?
Choose Qdrant if
- You want to start without paying.
- You work on Cloud (AWS, GCP, Azure), Kubernetes, Self-hosted, Edge (beta), Serverless (coming).
Choose Dgraph if
- You need native graphql.
- You want to start without paying.
- You work on Linux, Mac, Docker, Web.
- You also want distributed architecture.
Questions people ask
- Is Qdrant or Dgraph better?
- Neither clearly leads. Qdrant starts at Free and Dgraph at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Qdrant or Dgraph?
- Qdrant starts at Free and Dgraph at Free.
- Does Qdrant or Dgraph run on more platforms?
- Qdrant runs on Cloud (AWS, GCP, Azure), Kubernetes, Self-hosted, Edge (beta), Serverless (coming). Dgraph runs on Linux, Mac, Docker, Web.
- Can I use Qdrant for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Qdrant best used for?
- Qdrant is most often used for retrieval-augmented generation (rag) backends for llm applications, semantic search across large document corpora, multimodal retrieval (text, images, video) for recommendation systems, similarity-based product or content recommendations. Of those, retrieval-augmented generation (rag) backends for llm applications and semantic search across large document corpora are not what Dgraph is typically brought in for.
- What can Qdrant do that Dgraph cannot?
- Dgraph covers Native GraphQL, Distributed Architecture, ACID Transactions, GraphQL Subscriptions.