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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.
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, between Pinecone (8.3) and Weaviate (7.6), with Zilliz nearby on the open-core commercial path. 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 | Frequent win in RAG stacks. |
| Hybrid keyword + vector | Mixed | Payload filters help; not Weaviate-module shaped. |
| Zero-ops only (no cluster sizing) | Mixed | Free is easy; production still sizes resources. |
| Document labeling / annotation | Poor | Wrong category. |
Who it’s for
Good fit
- RAG and recommendation teams that may start OSS then buy Cloud
- Buyers who want quantization, multivector, and rich payload filters
- Startups avoiding a paid monthly floor while prototyping
Poor fit
- Teams that refuse any cluster sizing or calculator-based bills
- Catalog or labeling platform buys
- Buyers needing only managed embeddings without a vector store
Review Excerpts
Below are excerpts from public reviews. Paid reviews and pay-for-play sites such as Clutch were excluded.
“Qdrant’s filtering and open-source deployment options let us keep latency low on RAG without locking every index into a proprietary managed API.”
“The free Cloud cluster was enough to prove retrieval quality before we moved the same collections onto a Standard HA setup.”
“Production sizing still means learning vCPU/RAM/disk tradeoffs; teams expecting a single all-in seat price find Standard less obvious than a flat SaaS card.”
“We run hybrid payload filters on product metadata in front of the LLM so retrieval stays precise without a separate keyword engine.”
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 such as Clutch 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 fit pattern described above.
Scope
Qdrant is graded here as vector / retrieval infra. Criteria scores can move as more review volume and product checks are added.