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Qdrant vs Vespa

Two fact sheets from our own research, side by side — pricing, platforms, API access and status re-checked by us rather than quoted from the vendors.

The short answer

Free plan
Both have one
Public API
Both have one
Higher Trust score
Vespa — 93 vs 88 for Qdrant
Best fit
Qdrant: Teams building RAG or semantic search who want an open-source, self-hostable store · Vespa: Teams building large-scale search, recommendation or RAG systems that need hybrid ranking
Platforms
Qdrant: Web · Vespa: Linux, macOS

From our own records as of each tool's last check. Anything we have not confirmed is left out rather than guessed. How we verify

QdrantOpen-source vector search engine written in Rust, available self-hosted or as a managed cloud for RAG and recommendation apps.VespaOpen-source search and vector engine that combines text, vectors, ranking and model inference for RAG, recommendations and agents.

Side by side

FactQdrantVespa
CategoryVector Databases & RAGVector Databases & RAG
Pricing modelFreemiumFreemium
Public APIYesYes
Trust score88 / 10093 / 100
PlatformsWeb, APIAPI, Linux, macOS
CompanyQdrant Solutions GmbHVespa.ai AS
Launched20212017
Last verifiedOct 2, 2026Oct 2, 2026

What our research says

Qdrant

A mature, well-documented vector database that is easy to start with locally and scales to managed or private deployments. It is a strong pick when you want open-source control and fine-grained metadata filtering. Paid cost is usage-based and not quoted as a simple number, so estimate it with the vendor's calculator before committing.

Pros

  • Apache 2.0 engine with a free forever cloud tier
  • Native hybrid search with dense and sparse vectors
  • Deployable on your own servers, in Qdrant Cloud, or in your own cloud
  • SOC 2 and HIPAA compliance listed on the homepage

Cons

  • Standard and Premium cloud pricing is usage-based with no quoted starting price
  • The free tier is a single small node suited to prototypes only
  • Self-hosting means you handle scaling, backups and upgrades

Vespa

Vespa is built for search and recommendation workloads where relevance, hybrid retrieval and low latency at scale matter more than a quick prototype. That depth makes it heavier to learn than a simple vector store, so it pays off mainly for production-grade retrieval.

Pros

  • Open source under Apache-2.0, with a managed Vespa Cloud option
  • Combines vector search, text search, tensors and model inference in one platform
  • Supports real-time updates alongside queries
  • Case studies include Perplexity, according to the vendor

Cons

  • Steeper learning curve than lightweight vector databases
  • Cloud rates are not shown as a plain price table, so cost estimation takes a trial or sales contact
  • Operating a self-hosted cluster is a real infrastructure commitment

Which one fits

Choose Qdrant if you need

  • Teams building RAG or semantic search who want an open-source, self-hostable store
  • Applications that need heavy metadata filtering alongside vector similarity
  • Regulated companies that need to run managed clusters in their own cloud

Choose Vespa if you need

  • Teams building large-scale search, recommendation or RAG systems that need hybrid ranking
  • Engineers who want one engine for text, vectors and structured filters
  • Organisations that want to start self-hosted and move to a managed cloud later

Qdrant vs Vespa: the questions people ask

Does Qdrant or Vespa have a free tier?

Both: Qdrant has a free tier, and Vespa has a free tier.

Which has a public API, Qdrant or Vespa?

Both ship a public API, so either can be built on; the fact table above shows the platforms each one runs on.

More comparisons

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