Qdrant
Boost AI with high-performance, scalable vector database technology.. [Paid]
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What is Qdrant?
Qdrant is an open-source, AI-native vector search engine and vector database written in Rust, designed for high-performance similarity search at any scale. It stores vectors with additional metadata payload and enables fast semantic search, filtering, and hybrid retrieval. Qdrant is used for building RAG applications, AI agents, recommendation systems, semantic search, and anomaly detection.
Key features include native hybrid search combining dense and sparse vectors (supporting BM25, SPLADE++, miniCOIL), expansive JSON metadata filtering with nested/geo/text conditions, built-in multivector support for multimodal retrieval, efficient one-stage filtering during HNSW traversal, full-spectrum reranking with score boosting and MMR, real-time indexing without rebuilding, memory-efficient storage with up to 64x quantization, official client libraries for Python/JavaScript/Rust/Go/Java/.NET, REST and gRPC APIs, and a built-in Web UI for visual exploration. Qdrant Edge provides lightweight embedded vector search for offline-capable AI on mobile devices, robots, and kiosks.
Qdrant is for developers and enterprises building production-grade AI search applications who need high RPS, minimal latency, and fine-grained control over search accuracy. It serves teams working on GenAI/RAG, multi-agent platforms, semantic search engines, personalized recommendations, and data analysis. The engine is enterprise-ready with SOC2 and HIPAA compliance, multitenancy with granular RBAC, private networking, zero-downtime upgrades, and backups with point-in-time restore.
Qdrant offers flexible deployment: open-source self-hosted (Apache-2.0 license), fully managed Qdrant Cloud on AWS/GCP/Azure, Hybrid Cloud bringing your own Kubernetes, Private Cloud for air-gapped deployments, and Qdrant Edge (beta) for edge inference. It integrates with leading AI frameworks and offers native cloud inference for generating embeddings directly within Qdrant Cloud.
Qdrant pricing
Pricing model: Freemium
Qdrant uses a freemium, usage-based pricing model. The open-source self-hosted version is free ($0) under Apache-2.0 license, running on your own infrastructure via Docker/Kubernetes. Qdrant Cloud Free Tier is free forever with a single node cluster: 0.5 vCPU, 1GB RAM, 4GB disk, and free cloud inference with selected models - ideal for testing and prototypes without high availability. Standard Tier uses usage-based pricing for production workloads with dedicated resources, vertical/horizontal scaling, highly available setups, backup & disaster recovery, free tokens for paid inference models, and 99.5% uptime SLA. Premium Tier requires minimum spend for enterprises needing SSO, private VPC links, 99.9% uptime SLA, and 24x7 extra support. Hybrid Cloud starts at $0.014/hour for bring-your-own-kubernetes deployments. Private Cloud is custom quote for fully isolated on-premise deployments. Billing is hourly based on actual vCPU, memory (GB), storage (GB), backup storage (GB), and paid inference tokens consumed.
Qdrant pros
- Built entirely in Rust for maximum performance with SIMD acceleration
- Custom storage engine Gridstore with no wrappers or bolt-ons
- Real-time indexing - vectors searchable immediately after insertion
- Memory-efficient storage storing billions of vectors with minimal footprint
- Up to 64x memory reduction via asymmetric, scalar, and binary quantization
- Efficient one-stage filtering applied during HNSW traversal without pre/post-filtering
- Native hybrid search blending dense and sparse vectors in one query
- Expansive metadata filters including nested, text, geo, and has_vector conditions
- Built-in multivector support for expressive multimodal retrieval
- Full-spectrum reranking with score boosting, ColBERT late interaction, and MMR
- Official client libraries for Python, JavaScript, Rust, Go, Java, and .NET
- REST and gRPC APIs with OpenAPI and protobuf definitions
- Built-in Web UI for exploring collections and testing queries visually
- Qdrant Edge for offline-capable vector search on mobile/robots/kiosks
- SOC2 and HIPAA compliant with enterprise-grade security features
- Multitenancy with granular RBAC and vector-scoped API keys
- Zero-downtime upgrades and continuous backups with point-in-time restore
- Private networking and SSO support in Premium tier
- Cloud Inference for generating embeddings without separate pipeline
- 99.5% uptime SLA on Standard tier, 99.9% on Premium tier
Qdrant cons
- Self-hosted deployments not secure by default - require manual authentication setup
- Free tier limited to 1GB RAM and 4GB disk without high availability
- Requires two databases when used alongside pgvector for relational data
- Significant engineering effort needed to set up correctly at scale
- No downgrade support - storage format migration is irreversible
- Only guarantees compatibility between two consecutive minor versions
- Joins not supported in the query DSL
- Collection info API returns approximate point count, not exact
- Results may vary slightly across machines due to hardware/parallel processing differences
- Fine-tuning required to optimize performance depending on application
Frequently asked questions about Qdrant
What is the maximum vector dimension supported by Qdrant?
In dense vectors, Qdrant supports up to 65,535 dimensions.
Can Qdrant generate vector embeddings?
Yes, if you're using Qdrant Cloud, you can generate embeddings with Qdrant Cloud Inference. It lets you embed, store, and index data in a single API call without a separate inference service. Cloud Inference supports dense models for semantic search, sparse models for keyword recall, and multimodal models for image and text search. Several models are available at no cost on all cluster tiers including the free tier. For open-source/self-hosted Qdrant, Cloud Inference isn't available - you can use FastEmbed (Qdrant's lightweight local inference library) or bring your own embedding model/service.
How does Qdrant handle real-time data updates and search?
Qdrant supports live updates for vector data, with newly inserted, updated, and deleted vectors available for immediate search. The system uses full-scan search on unindexed segments during background index updates. Qdrant doesn't always need to index all vectors in the collection - it stores data in segments, and if a segment is small enough, full-scan search is more efficient.
What Happens if I Exceed Free Tier Limits?
If your data size growth exceeds the Free Tier limits (1GB RAM and 4GB disk), you can easily upgrade to a Standard Tier and scale up your cluster. The upgrade provides dedicated resources and high availability.
How does Qdrant handle different vector embeddings from various providers in the same collection?
Qdrant natively supports multiple vectors per data point, allowing different embeddings from various providers to coexist within the same collection. A point can hold any number of dense, sparse, and multi vectors, though each must be configured in the collection's schema. This lets you run hybrid queries over several representations of the same data within one collection.
What's the Difference Between Managed, Hybrid, and Private Cloud?
Managed Cloud (Qdrant Cloud) is fully managed by Qdrant on AWS/GCP/Azure with high availability and auto-sharding. Hybrid Cloud lets you bring your own Kubernetes infrastructure while using Qdrant's managed control plane - data stays in your network but operations are fully managed through Qdrant Cloud. Private Cloud gives you complete control with dedicated, isolated on-premise deployment for strict security or compliance needs, including air-gapped setups, with custom SLAs and full isolation.
Should I create one Qdrant collection per user?
No. Creating one collection per user is considered an antipattern and is more resource intensive. Instead, create a single collection and separate access using payloads. Each Qdrant point can have a payload as metadata - for multitenancy, include a user_id or tenant_id for each point. You can optimize storage further by enabling tenant indexing for payload fields.
Does Qdrant support full-text search or hybrid search?
Qdrant is primarily a vector search engine. It implements full-text support as long as it doesn't compromise vector search use case. Qdrant can do: search with full-text filters, apply full-text filters to vector search, prefix search and semantic search-as-you-type, sparse vectors (SPLADE-like), multi-vectors (ColBERT and other late-interaction models), and combinations of multiple searches. Qdrant doesn't plan to support: non-vector-based retrieval/ranking functions, built-in ontologies/knowledge graphs, or query analyzers and other NLP tools.
Is my Qdrant cluster secure by default?
It depends on your deployment. Qdrant Cloud clusters are always secure by default. Self-hosted deployments are NOT secure by default - they're open to all network interfaces and have no authentication configured until you set it up. You need to configure security manually for self-hosted deployments.
How Is Billing Calculated Month-to-Month?
Billing is calculated based on actual resource usage during the billing period. You're charged for compute (vCPU), memory (GB), storage (GB) consumed by your clusters, storage (GB) consumed by backups, and used inference tokens of paid models. Usage is billed hourly, and you can monitor it through the Qdrant Cloud dashboard.