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Qdrant
Open-source vector database with managed Qdrant Cloud for similarity search, RAG retrieval, and hybrid filtering across AWS, Azure, and GCP. Qdrant raised a $50 million Series B in March 2026.
Engineering teams building RAG or recommendations who want an open-source core with a managed Cloud path and no mandatory monthly floor on the free tier.
Buyers who only want a fully managed, zero-ops index with a published dollar rate card for every production SKU, or teams that will never operate open-source software.
Verdict
Qdrant is a good fit for product and platform teams that want a Rust-based vector engine they can self-host or run on Qdrant Cloud. Cloud pricing starts with a permanent free single-node cluster and moves to usage-based Standard on vCPU, RAM, and disk, with Premium for SSO and private networking. Practitioners like filter performance and open-source control; Cloud sizing still takes a calculator pass for production HA.
Score Breakdown
How Qdrant scores in the categories that matter to its buyers.
Pricing
Qdrant Cloud publishes Free, Standard, Premium, Hybrid, and Private options on qdrant.tech/pricing. Free is forever at fixed small resources; Standard is usage-based on cluster resources billed hourly. As of September 2026.
Model: Free forever (0.5 vCPU / 1 GB RAM / 4 GB disk single node; suspends after inactivity). Standard: dedicated resources, HA, backups, 99.5% SLA, priced from the Cloud calculator on vCPU, RAM, disk, backups, and paid inference tokens. Premium: minimum spend with SSO, private VPC links, and higher SLA. Hybrid and Private Cloud run on your infrastructure with managed control plane. Open-source Qdrant remains free to self-host.
The Field at a Glance
Where Qdrant ranks among Vector / retrieval infra vendors we reviewed, by Overall Score and relative typical engagement cost.
Qdrant scores 7.3 in this four-vendor vector field, behind Pinecone (8.3), Zilliz (8.0), and Weaviate (7.6). Relative cost is in a lower mid band when you stay on Free or modest Standard clusters.
Use-case matrix
| Use case | Fit | Notes |
|---|---|---|
| Managed vector search API | Strong | Cloud clusters with REST/gRPC clients. |
| Self-host / open-source control | Strong | Apache-2.0 core is first-class. |
| Metadata / payload filtering | Strong | Qdrant supports payload filtering alongside vector search. |
| Hybrid keyword + vector | Mixed | Payload filters help; Weaviate offers built-in hybrid search. |
| Zero-ops only (no cluster sizing) | Mixed | Free is easy; production still sizes resources. |
| Document labeling / annotation | Poor | Qdrant does not sell labeling tools. |
Review Excerpts
Representative buyer views drawn from public information about Qdrant. Paid reviews and pay-for-play sites were excluded.
“Filtering on payload fields while searching vectors was the reason we picked it for a RAG service.”
Representative buyer view
“We self-host the open-source build and moved a smaller index to Qdrant Cloud for a second team.”
Representative buyer view
“Sizing a production cluster meant working through resource calculators, because Standard prices are not a flat card.”
Representative buyer view
Where customer reviews couldn't be found, our analysts attempted to give a voice to buyer sentiment, drawing on real information from review sites, forums, and product documentation.
Based on: Qdrant’s website and Qdrant’s Series B announcement
Company Facts
- Founded
- 2021 (CB Insights)
- Headquarters
- Berlin, Germany (CB Insights)
- Total funding raised
- $87.79M (CB Insights)
- Key investors
- 42CAP, IBB Ventures, Unusual Ventures, Spark Capital, Atlantic Vantage Point (CB Insights)
- Customers
- Tripadvisor, HubSpot and &AI (CB Insights)
- Website
- qdrant.tech
Key Leadership
Andrey Vasnetsov
Co-founder and CTO
Methodology
This page is an independent evaluation of Qdrant for buyers comparing options in vector / retrieval infra. AI Industry Reviews accepts no sponsorships, advertising, or pay-for-placement fees. Qdrant did not pay for this review.
What we scored
The headline number is an Overall Score on a 0-10 scale. Eight criteria fall under it in two groups.
- Buyer outcomes (for vector / retrieval infra)
- Vector search & filtering
- Open-source + Cloud path
- RAG / retrieval ops
- Managed ops simplicity
- Company & commercial
- Innovation & product leadership
- Project management & communication
- Pricing
- Contract fairness
Pricing measures whether the price looks fair for the value delivered, including packaging and renewal friction that show up in real buying cycles.
Score Composition
| Input | Weight | What it covers |
|---|---|---|
| Reviews | 40% | A proprietary read of what practitioners say about likes, complaints, and day-to-day use, including public review sites, forums, and private chat rooms. Paid reviews and pay-for-play sites are out of scope. |
| Product | 35% | Hands-on look at screens and workflows. |
| Pricing | 15% | Whether the price looks fair for what you get. |
| Docs & training | 10% | Docs, tutorials, and training material. |
How we balanced the evidence
The Overall Score is the simple average of the eight criteria. Recommendation language follows that score and the use-case matrix above.
Scope
Qdrant is graded here as vector / retrieval infra. Criteria scores can move as more review volume and product checks are added.