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Databases · head to head

Qdrant vs Vespa

Qdrant logo

Qdrant

Databases

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

From
Free
Rated
-
Full reviewWebsite
Vespa logo

Vespa

Databases

Distributed AI search platform for retrieval, ranking, and inference

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Qdrant free tier extremely limited (0.5 vCPU, 1GB RAM, 4GB disk); suitable only for experiments; Vespa pricing not publicly listed, requires contacting sales
  • Prices and features above were last checked on 29 August 2026.

Where they differ

Only the attributes on which Qdrant and Vespa actually diverge.

Attributes where Qdrant and Vespa differ
AttributeQdrantVespa
Pricing modelfreemiumcontact-sales
PlatformsCloud (AWS, GCP, Azure), Kubernetes, Self-hosted, Edge (beta), Serverless (coming)Cloud, Self-hosted
FoundedUnknown2023

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Databases).

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 Vespa does not also cover.

Only in Vespa

  • Vector search
  • Text and structured search
  • Machine-learned ranking
  • Real-time serving
  • SQL interface
  • Automatic scaling
  • Open-source

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

Vespa

  • Build RAG systems with semantic search over documentsnot Qdrant
  • Power e-commerce search with ML rankingnot Qdrant
  • Create recommendation engines for personalizationnot Qdrant
  • Implement real-time search for news or feedsnot Qdrant
  • Deploy private semantic search over sensitive datanot 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

Vespa

  • Pricing not publicly listed, requires contacting sales
  • Steeper learning curve compared to simpler search tools
  • Operational complexity for self-hosted deployments
  • Smaller ecosystem compared to cloud-native alternatives

Pricing, plan by plan

Qdrant

Free
  • FreeFree
    • Single-node cluster
    • 0.5 vCPU
    • 1GB RAM
  • Standard$undefined/usage-based
    • Dedicated resources
    • Flexible scaling
    • High availability
  • Premium$undefined/minimum spend
    • SSO and SAML
    • Private VPC links
    • 99.9% uptime SLA

Vespa

Free

No published plan breakdown. See the Vespa 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 Vespa if

  • You need vector search.
  • You want to start without paying.
  • You work on Cloud, Self-hosted.
  • You also want text and structured search.
Vespa review

Questions people ask

Is Qdrant or Vespa better?
Neither clearly leads. Qdrant starts at Free and Vespa at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Qdrant or Vespa?
Qdrant starts at Free and Vespa at Free.
Does Qdrant or Vespa run on more platforms?
Qdrant runs on Cloud (AWS, GCP, Azure), Kubernetes, Self-hosted, Edge (beta), Serverless (coming). Vespa runs on Cloud, Self-hosted.
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 Vespa is typically brought in for.
What can Qdrant do that Vespa cannot?
Vespa covers Vector search, Text and structured search, Machine-learned ranking, Real-time serving.

Answered from the vendors’ own pages

Qdrant: Can I use Qdrant for free?

Yes, Qdrant offers a free tier that provides a single node cluster with 0.5 vCPU, 1GB RAM, and 4GB disk space. It includes free cloud inference with selected models and is described as free forever for testing and prototypes.

Source
Vespa: Is Vespa open-source?

Yes, Vespa is open-source under the Apache 2.0 license. The code is available on GitHub, and you can self-host or use the managed cloud service.

Source
Qdrant: How is Qdrant Cloud billing calculated?

Billing is calculated based on actual resource usage during each billing period. You are charged hourly for compute (vCPU), memory (GB), storage (GB), backups, and any paid inference tokens used.

Source
Vespa: What latency can Vespa achieve?

Vespa is designed for sub-100 millisecond latencies with thousands of queries per second, suitable for real-time search and recommendation applications.

Source
Qdrant: What happens if I scale beyond the free tier?

The Standard Tier uses the same usage-based billing model as the free tier but adds features like dedicated resources with flexible scaling, highly available setups with backup and disaster recovery, and a 99.5% uptime SLA.

Source
Vespa: Does Vespa support vector search?

Yes, Vespa provides native vector search capabilities alongside text, structured data, and tensor operations for building comprehensive search and AI applications.

Source
Qdrant: What SLA does Qdrant offer?

The Standard Tier provides 99.5% uptime SLA. The Premium Tier, which requires a minimum spend for enterprises, offers 99.9% uptime SLA along with single sign-on and private VPC links.

Source
Vespa: What is the pricing model for Vespa Cloud?

Vespa Cloud pricing is not publicly listed and requires contacting their sales team to discuss your specific use case and scale requirements.

Source
Qdrant: Are there dedicated cloud infrastructure options?

Yes, Qdrant offers Hybrid Cloud (runs on your infrastructure) and Private Cloud (complete isolation) options, both with custom pricing that requires contacting the sales team.

Source
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