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Qdrant vs Convex

Qdrant logo

Qdrant

Software

High-performance vector database for similarity search and embedding-based retrieval

From
Free
Rated
-
Full reviewWebsite
C

Convex

Software

The reactive backend platform that keeps up with you and your agents

From
On request
Rated
-
Full reviewWebsite

The short version

  • Only Qdrant has a free tier, so it costs nothing to try first.
  • Each has a real cost: Qdrant free tier extremely limited (0.5 vCPU, 1GB RAM, 4GB disk); suitable only for experiments; Convex the Free and Starter tier includes only 1 million function calls and 0.5 GB of database storage per month before per-unit overage charges of $2.20 per million calls apply, as of August 2026.

Where they differ

Only the attributes on which Qdrant and Convex actually diverge.

Attributes where Qdrant and Convex differ
AttributeQdrantConvex
Starting priceFreeOn request
Pricing modelfreemiumusage-based
Free tierYesNo
PlatformsCloud (AWS, GCP, Azure), Kubernetes, Self-hosted, Edge (beta), Serverless (coming)Web

Identical on both: user rating (Not yet rated), category (Unknown).

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 Convex
  • Semantic search across large document corporanot Convex
  • Multimodal retrieval (text, images, video) for recommendation systemsnot Convex
  • Similarity-based product or content recommendationsnot Convex
  • Real-time vector indexing for streaming embedding datanot Convex

Convex

No use cases recorded yet. See the Convex review.

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

Convex

  • The Free and Starter tier includes only 1 million function calls and 0.5 GB of database storage per month before per-unit overage charges of $2.20 per million calls apply, as of August 2026.

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

Convex

On request

No published plan breakdown. See the Convex review.

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).
Qdrant review

Choose Convex if

Nothing in the data separates Convex from Qdrant on the points above - pick on price and on how each one feels to use.

Convex review

Questions people ask

Is Qdrant or Convex better?
Neither clearly leads. Qdrant starts at Free and Convex at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Qdrant or Convex?
Qdrant has a free tier; the other does not. Paid plans start at Free for Qdrant and On request for Convex.
Does Qdrant or Convex run on more platforms?
Qdrant runs on Cloud (AWS, GCP, Azure), Kubernetes, Self-hosted, Edge (beta), Serverless (coming). Convex runs on Web.
Can I use Qdrant for free?
Yes. Qdrant has a free tier, so you can try it without paying. Convex starts at On request.
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 Convex is typically brought in for.
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