Qdrant
Vector search engine for AI retrieval
Recommended for
- Solo / Indie
- Idea-Stage
- Pre-Seed
- Seed
- Series A
- Scaleup
- Late-Stage
- Public / Enterprise
SSIE
Stage Fit Recommendations
Following data shows why Qdrant is recommended for listed teams. The Fit Score represents the percentage fit of Qdrant to each company stages, as calculated by the SSIE.
Stage
Fit Score
Fit Reasons
Solo / Indie
$0–$2,000 MRR
51- Free tier available, no upfront cost
- Entry pricing sized for this stage
- Open-source codebase, independently reviewable
- Documented REST API for integration
- Multiple support channels (chat, email)
Idea-Stage
$0
53- Pre-built third-party integrations
- Data encryption at rest and in transit
Pre-Seed
$0–$10k MRR
61- GDPR - baseline compliance certification
- Automated data backup with retention policies
- Documented disaster recovery procedures
Seed
$10k–$100k MRR
69- SOC 2 - core enterprise certification
- Single sign-on (SSO) via SAML 2.0
- Role-based access control (RBAC)
- Published uptime SLA with availability guarantees
- Active bug bounty program
- Dedicated enterprise plan with custom pricing
Series A
$100k–$500k MRR
74- HIPAA - regulated-industry certification
- Configurable regional data residency
Scaleup
$500k–$2M+ MRR
75- SOC 2 - core enterprise certification
Late-Stage
$2M+ MRR
74- SOC 2 - core enterprise certification
Public / Enterprise
$100M+ ARR
73- SOC 2 - core enterprise certification
Description
Qdrant is an open-source vector search engine written in Rust, providing fast and scalable vector similarity search services with convenient API. It is designed for production-grade AI search and is engineered for real-time retrieval with speed, accuracy, and scale. Qdrant offers expansive metadata filters, including nested, text, geo, has_vector, and more. It is used by companies to make sense of their unstructured data through vector searches. Qdrant has a strong community with over 25k GitHub stars and 60k community members.
Pricing
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Reviewed by a human. SSIE Fit Scoring.
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