Why It Matters
Qdrant delivers purpose-built vector database performance with open-source freedom. Unlike proprietary solutions charging per query, Qdrant charges for resources — so high-traffic apps avoid runaway costs. Built in Rust for speed and safety, it supports complex filtering, multi-vector storage, and deploys anywhere from Docker containers to air-gapped environments.
What Sets Qdrant Apart
The key differentiators that make Qdrant stand out from other vector databases.
Fully Open Source (Apache 2.0)
Complete source code on GitHub. No open-core gatekeeping — every feature available in the cloud is also available self-hosted. Fork it, audit it, contribute to it.
Built in Rust for Speed & Safety
Rust's memory safety guarantees mean no garbage collection pauses, no buffer overflows, and predictable latency. P95 query latency under 5ms for in-memory workloads.
Multi-Vector & Advanced Filtering
Store multiple named vectors per point (e.g., text + image embeddings). Rich payload filtering, full-text search, geo-filtering, and nested conditions — all at query time with HNSW pre-filtering.
No Per-Query Pricing
Pay for resources (CPU/RAM/disk), not operations. Whether you run 100 or 1,000,000 queries per day, the cluster cost stays the same. High-traffic apps avoid cost surprises.
Flexible Storage: RAM, Disk, or Hybrid
Keep hot data in RAM for sub-5ms latency, or use memmap to serve vectors from disk and reduce costs 4-10x. Scalar, product, and binary quantization supported for further compression.
Pricing — No Surprises
Pay for cluster resources (CPU, RAM, disk) — not per query or per vector. Self-hosted is free and open source under Apache 2.0.
- Unlimited vectors, queries, and storage
- Run via Docker, Kubernetes, or binary
- Full feature parity with cloud
- No telemetry or phone-home required
- Community support (Discord, GitHub)
- You manage infrastructure
- 1 GB free forever (no credit card)
- AWS, GCP, Azure — 15+ regions
- Horizontal & vertical scaling
- High availability & auto-healing
- Backup & disaster recovery
- Zero-downtime upgrades
- Unlimited users
- Standard support & uptime SLAs
- All managed cloud benefits
- Run on your own infrastructure
- Central cluster management via Qdrant Cloud
- Any cloud, on-premise, or edge locations
- Data stays in your environment
- Standard or premium support
- All hybrid cloud benefits
- Fully air-gapped deployment option
- Maximum data sovereignty & control
- No connection to Qdrant Cloud required
- Premium support plan included
Pricing Terms Explained
Before you use the pricing calculator, understand what each term means. No more guessing what a "Read Unit" is or how "dimensions" affect your bill.
Cluster
A set of nodes (servers) that run your Qdrant instance. In managed cloud, you choose the node type and count. In self-hosted, you set this up yourself.
A small production cluster might be 1 node with 4 GB RAM and 2 vCPUs, costing around $65–$100/month on managed cloud depending on the provider and region.
Node
A single server in your cluster. Each node has a fixed amount of RAM, CPU, and disk. You pick a node configuration based on your data volume and query load.
RAM
Random Access Memory — where vector indexes and frequently accessed data live for fast retrieval. More vectors = more RAM needed, unless you offload vectors to disk.
Approximate RAM per vector: (dimensions × 4 bytes) + overhead. 1M vectors × 768d ≈ 3–4 GB RAM with HNSW index.
A collection of 1M vectors with 768 dimensions needs roughly 3–4 GB RAM for the HNSW index in default configuration. You can reduce this 4x with scalar quantization.
Disk Storage
Persistent storage for raw vector data, payloads (metadata), and WAL (write-ahead log). Cheaper than RAM. Vectors can be stored on disk with memmap for cost savings at a small latency trade-off.
Quantization
A technique to compress vectors, reducing RAM usage and often speeding up searches. Qdrant supports scalar, product, and binary quantization.
Scalar quantization: ~4x RAM reduction. Binary quantization: ~32x RAM reduction (works best with specific models).
1M vectors × 1,536d normally needs ~7 GB RAM. With scalar quantization, this drops to ~1.75 GB — allowing a smaller, cheaper cluster.
Replication Factor
How many copies of your data exist across nodes. Factor of 2 means each vector is stored on 2 nodes for redundancy. Higher replication = higher availability but more cost.
Memmap (On-Disk Vectors)
Memory-mapped files that store vectors on disk instead of RAM. The OS caches frequently accessed vectors in RAM automatically. This drastically reduces RAM requirements at a small latency cost.
With memmap enabled, a 10 GB vector collection might only need 2–3 GB RAM for the HNSW index graph, rather than 10+ GB for full in-memory storage.
Multi-Vector (Named Vectors)
A unique Qdrant feature where each point can store multiple vectors of different types and dimensions. For example, one point can have a text embedding and an image embedding simultaneously.
Estimate Your Qdrant Costs
Answer a few simple questions about your project and we'll estimate your monthly costs — no technical knowledge required. Fine-tune with advanced options if you want.
What are you building?
Select the use case that best describes your project. This helps us estimate storage, traffic, and recommend the right configuration. See an to understand how costs work before calculating.
Use Case Fit
See how Qdrant aligns with different AI and search use cases.
Technical Deep Dive
Architecture, performance, developer experience, and security — everything you need to evaluate Qdrant for production use.
Architecture
Search Capabilities
Performance
Qdrant publishes open benchmarks comparing against Weaviate, Milvus, Elasticsearch, and others. Performance heavily depends on quantization settings, memmap usage, and hardware.
Deployment Options
Available on AWS, GCP, Azure
Developer Experience
Security & Compliance
Honest Trade-Offs
No technology is perfect. Here are the real limitations of Qdrant — so you make an informed decision, not a surprised one.
| Trade-Off | Impact | Details |
|---|---|---|
| Self-Hosting Requires Infrastructure Expertise | Medium | Running Qdrant in production (backups, monitoring, scaling, HA) requires DevOps knowledge. It's not zero-ops like Pinecone — you're responsible for the infrastructure. |
| No Managed Serverless (Yet) | Medium | Qdrant Cloud uses provisioned clusters, not serverless. You pay for nodes even when idle. This can be wasteful for low-traffic or bursty workloads compared to pay-per-query models. |
| Capacity Planning Needed | Medium | You must estimate RAM, CPU, and disk needs upfront when provisioning a cluster. Under-provisioning leads to OOM (out-of-memory) errors; over-provisioning wastes money. The pricing calculator helps, but it's still your responsibility. |
| Smaller Ecosystem Than Pinecone | Low | While growing rapidly, Qdrant has fewer third-party integrations, tutorials, and enterprise references compared to Pinecone, which has first-mover advantage in the managed vector DB space. |
| No Built-In Embedding or Reranking Models | Low | Unlike Pinecone which offers hosted embedding and reranking models, Qdrant is purely a database. You need a separate service (OpenAI, Cohere, local models) to generate embeddings. |
Running Qdrant in production (backups, monitoring, scaling, HA) requires DevOps knowledge. It's not zero-ops like Pinecone — you're responsible for the infrastructure.
Qdrant Cloud uses provisioned clusters, not serverless. You pay for nodes even when idle. This can be wasteful for low-traffic or bursty workloads compared to pay-per-query models.
You must estimate RAM, CPU, and disk needs upfront when provisioning a cluster. Under-provisioning leads to OOM (out-of-memory) errors; over-provisioning wastes money. The pricing calculator helps, but it's still your responsibility.
While growing rapidly, Qdrant has fewer third-party integrations, tutorials, and enterprise references compared to Pinecone, which has first-mover advantage in the managed vector DB space.
Unlike Pinecone which offers hosted embedding and reranking models, Qdrant is purely a database. You need a separate service (OpenAI, Cohere, local models) to generate embeddings.