Open-source vector database for AI applications and semantic search
Qdrant is a vector database designed for storing and searching high-dimensional vectors used in AI and machine learning applications.
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AI Editor ApprovedApproved and published by our AI Editor-in-Chief after full panel analysis.Qdrant is an open-source vector database built specifically for handling high-dimensional vector data used in modern AI and machine learning applications. It provides efficient storage, indexing, and similarity search capabilities for vector embeddings generated by neural networks, making it suitable for applications like semantic search, recommendation engines, and retrieval-augmented generation (RAG) systems.
The database offers both exact and approximate nearest neighbor search algorithms, with support for filtering and payload-based queries. Qdrant can handle various distance metrics including cosine similarity, dot product, and Euclidean distance. It features horizontal scaling capabilities, real-time indexing, and ACID compliance for production workloads.
Qdrant targets developers, data scientists, and organizations building AI-powered applications that require fast vector similarity search. It competes with other vector databases like Pinecone, Weaviate, and Milvus in the growing vector database market. The platform offers both self-hosted open-source deployment options and managed cloud services.
Key technical features include support for multiple vector types, payload filtering, distributed deployment, and integration with popular machine learning frameworks. Qdrant provides REST and gRPC APIs, along with client libraries for Python, Rust, Go, and other programming languages, making it accessible to developers across different technology stacks.
Provides metrics, logs, and alerts for Qdrant Cloud clusters to enable performance monitoring and observability.
Offers a hosted managed cloud deployment so users can run Qdrant without managing their own infrastructure.
Provides filtering capabilities that can be applied during vector searches to enable precise retrieval of matching results.
Supports running Qdrant with GPU acceleration to enhance indexing and search performance.
Combines dense and sparse vector support to enable hybrid search capabilities through the Query API for enhanced retrieval quality.
Allows use of multiple vector representations per document to improve document retrieval precision and quality.
Supports creation and restoration of collection snapshots for data protection and backup management.
Supports sparse vectors alongside dense vectors to enable efficient vector-based hybrid search.
Stores, indexes, and retrieves high-dimensional vectors to perform fast and scalable vector similarity search operations.
Supports hybrid cloud cluster creation and operator configuration for deploying Qdrant across mixed infrastructure environments.
Offers integrations with popular platforms and frameworks to enable seamless embedding of Qdrant into existing AI and data pipelines.
Provides cloud RBAC with a permission reference system for managing access control and securing cloud resources.
For testing and prototypes
For production workloads and scaling applications
For enterprises with additional security and compliance needs
Run managed Qdrant clusters on your own infrastructure using your compute, network and storage
Dedicated, isolated deployment for strict security or compliance needs
Qdrant doubled its raise on Bosch and HubSpot running in production — the open-source bet has a moat.
“AVP led a $50M Series B in March 2026, bringing Qdrant to $87.8M total raised. The harder question is whether to standardize before Pinecone's managed convenience locks the org in elsewhere.”
Canva, HubSpot, Bosch, Tripadvisor, and OpenTable run Qdrant in production. That's the logo wall a CIO can defend without a memo. Spark Capital and Bosch Ventures both wrote checks in the March 2026 round — that tells you what the strategic buyers think.
The substance is the Rust core and Hybrid Cloud. Andrey Vasnetsov built the engine in Rust from scratch — that's a performance moat Pinecone can't copy without a rewrite. Hybrid Cloud keeps vectors on your own infrastructure under Qdrant's managed plane, which is the regulated-workload play your CISO will actually approve.
The catch is the pricing surface. Standard and Premium tiers ship without published prices — you're in sales-led quotes once you outgrow the free 1GB cluster. Pilot on the free tier for 60 days. Get Standard quoted in writing before you commit.
Pinecone still owns the managed-only buyer, but Qdrant's open-source plus Hybrid Cloud is the stronger board story.
Canva, HubSpot, Bosch, Tripadvisor, and OpenTable in production make this an easy slide for any board.
Free 1GB cluster plus REST, gRPC, and Python and JavaScript clients lets engineers prototype before procurement.
RAG and semantic search are direction-of-travel, and Hybrid Cloud is the right shape for regulated AI workloads.
$87.8M raised across two rounds with Spark Capital, AVP, and Bosch Ventures — vendor existence is settled.
Engineering teams who run RAG and semantic search in regulated environments.
Solo developers who just need a local prototype with sqlite-vss.
Rust substrate plus a self-hostable cloud is the architectural bet most AI platform teams should defend.
“Qdrant runs the same engine in OSS, Managed, Hybrid, and Private Cloud — that optionality is the architectural hedge against Pinecone-style lock-in. The Rust core gives you 30-40ms p99 latency at 100M vectors and a Query API that fuses dense, sparse, and multivector retrieval in one call.”
Berlin-based, founded 2021, Rust core. That stack choice isn't decorative — it's why Qdrant lands p99 in the 30-40ms band on 100M-vector workloads where Weaviate's JVM tax shows. Andrey Vasnetsov and Andre Zayarni built the engine before they built the cloud.
The Query API fuses dense, sparse, and multivector retrieval in one request — the shape RAG actually wants. Pinecone gives you a polished managed surface and no exit ramp. Qdrant's Hybrid Cloud tier keeps the data plane on your VPC while the control plane stays managed — the right answer for regulated AI workloads.
The catch is the Standard Tier line that reads 'no automated shard rebalancing.' That's the operational tax below the Premium SKU, and it bites whenever you reshape collections at scale. Spark Capital led a $28M Series A in January 2024, but the open-core model means strategic features land in the cloud first.
Sits at the top of the vector DB pack against Pinecone, Weaviate, and Milvus per public benchmarks.
Hybrid Cloud and Private Cloud match how regulated AI platform teams actually need to deploy.
REST and gRPC plus Python, JavaScript, Rust, and Go clients cover the realistic stack surface.
Open-core means strategic features land in cloud first, but OSS exit ramp keeps optionality intact.
Rust core, HNSW, multivector, and the Query API show engine-first craft above category norm.
AI platform teams who need a vector substrate they can self-host today and lift to managed cloud later.
Solo developers who just want a hosted endpoint and zero ops surface.
Qdrant Cloud bills hourly on vCPU and RAM — open-source escape hatch keeps the lock-in math honest.
“Managed Cloud starts at $25/month with usage-based hourly billing across vCPU, RAM, storage, and backups. The catch is SSO and Private VPC sit behind Premium, with no published rate.”
Qdrant Cloud bills by the hour. vCPU, RAM, storage, backup volume — line items, not seats. No per-query charge, no per-vector tax. Compare to Pinecone's pod-hour shape: different floor, same procurement question.
Free tier runs 0.5 vCPU and 1GB RAM — useful for prototypes, not load. Standard starts at $25/month per cluster with a 99.5% SLA. Premium goes to 99.9% and bundles SSO and Private VPC Links. A 50-engineer team across three Standard clusters at $200 each lands near $7,200/year.
The catch is Premium pricing isn't published. Hybrid Cloud and Private Cloud sit behind sales. Standard also omits automated shard rebalancing per the docs. However, the Apache 2.0 self-host path is the real negotiation lever, and Spark Capital's $28M Series A in January 2024 makes the OSS commitment look durable.
Hourly invoicing line-items compute, memory, and storage; inference tokens billed separately for paid models.
Apache 2.0 self-host option keeps every renewal honest and the OSS migration tool is documented.
Standard usage-based and Free tier are public; Premium, Hybrid, and Private Cloud are sales-gated.
Cluster-level metrics, logs, and alerts in the dashboard make per-workload cost attribution measurable.
Hourly resource billing avoids per-vector or per-query overage surprises common with Pinecone.
Engineering teams who want usage-based vector search billing without seat math.
Procurement teams who need every tier published before signing.
Qdrant's Query API folds dense, sparse, and ColBERT reranking into one round trip — RAG plumbing stops sprawling.
“Built on Rust with HNSW under the hood, Qdrant ships hybrid retrieval and RRF fusion in a single Query API call. The Standard cluster floor at $25/month and Spark Capital's $28M Series A make it a defensible bet for a RAG backend.”
The retrieval layer is where every RAG project bleeds time. Qdrant's Query API collapses dense + sparse + RRF fusion + ColBERT reranking into one round trip — no orchestration code stitched across BM25 and a vector store. Compare Pinecone where reranking still means a second client call.
Multivector support handles ColBERT-style late interaction without forcing you to flatten 32 token vectors into one. Payload filters apply pre-search, not post — the docs are explicit about indexed payload fields and the gRPC schema matches the REST schema field-for-field. Rust under the hood means the Free tier's 1GB RAM goes further than Python-stack rivals.
The catch is the Standard tier billing model — compute, RAM, storage, and inference tokens metered hourly from $25/month, but no automated shard rebalancing, so horizontal scale-out still means manual operator work. Backed by Spark Capital's $28M Series A from January 2024.
Query API collapses hybrid retrieval into one call, removing a real daily friction in RAG plumbing.
Docs explicitly cover indexed payload fields, schema parity, and OSS-to-Cloud migration tooling.
No automated shard rebalancing on Standard tier and hourly metered billing add cognitive overhead.
Multivector, ColBERT reranking, sparse vectors, GPU acceleration, and Hybrid Cloud span beginner to advanced.
REST plus gRPC with Python, JS, Rust, and Go clients fit existing stacks without retooling.
ML engineers who build RAG pipelines on managed infrastructure.
Teams who need automated shard rebalancing on the Standard tier.
Qdrant's free tier runs forever on 1GB, and that small choice tells you who they built it for.
“The free 1GB cluster fits roughly 250,000 vectors and never expires, which is rare in this category. The catch is the Standard tier scales horizontally but skips automated shard rebalancing, so growth still wants planning.”
The free tier tells you a lot. Most vector databases throttle you into a paid plan inside two weeks — Pinecone's starter is generous but pushes you toward pods fast. Qdrant's free 1GB cluster sits there indefinitely, fits about 250,000 vectors at 768 dimensions, and asks for no credit card.
Day thirty is when you find the Query API. Hybrid Search through one endpoint — dense and sparse vectors fused at the database, not stitched in your app code. Multivector Representations show up in the same call. Weaviate covers similar ground but the docs make you work harder.
The catch is the rough edges around scale. Standard tier scales horizontally but the pricing page admits there's no automated shard rebalancing — your team plans the resharding. Premium adds SSO and Private VPC Links with a 99.9% SLA, but pricing is on request.
Pricing page lists every tier on one screen and the Rust core shows in response feel.
REST, gRPC, Python and JavaScript clients are easy day one, but advanced Hybrid Search needs reading.
Backend vector database — mobile is not the use case, scored neutral per category norm.
Free 1GB cluster with no credit card and a published quick-start lowers the cost of trying.
Snapshots and ACID compliance ship, but Standard tier explicitly lacks automated shard rebalancing.
Developers who want a self-hostable vector store with a generous free tier.
Teams who need automated shard rebalancing without manual planning.
Qdrant closed $50M Series B in March 2026 while Pinecone was reportedly weighing a sale.
“Andrey Vasnetsov and Andre Zayarni built Qdrant in Rust from Berlin in 2021; the cadence and the $87.8M raise hold up. The yellow flag isn't Qdrant — it's the category consolidating into Postgres extensions before vector DB winners are crowned.”
Berlin, 2021. Andrey Vasnetsov and Andre Zayarni built this in Rust. Series B closed at $50M in March 2026, led by AVP — total raise sits at $87.8M. The team is real and the cadence held.
The product evidence is real. Hybrid Cloud lets you run managed Qdrant on your own VPC — that's the exit hatch most vector DBs don't ship. Multivector Representations and Query API hybrid fusion are concrete, not category words. Free tier starts at $0, managed cloud from $25/month.
But the category itself is the worry. Vector DBs are consolidating into Postgres extensions like pgvector and into OLTP stores. Pinecone was reportedly weighing a sale by August 2025. Milvus is open-source too. Qdrant's moat is Rust performance and that Hybrid Cloud option. Worth a 12-month bet, not a five-year one.
Hybrid Cloud and Rust perf are real, but Pinecone, Weaviate, Milvus, and pgvector make this a crowded space.
Apache 2.0 self-hosted option, REST and gRPC APIs, plus Hybrid Cloud means migration off is clean if direction shifts.
$50M Series B in March 2026 led by AVP is fresh runway, but category consolidation into Postgres extensions is the real watch.
Landing page leads with "vector search engine in Rust" — grounded, not aspirational, and the docs back it up.
Rust plus open-source plus steady changelog matches surviving infrastructure patterns, but the vector DB cohort itself is shaky.
Engineering teams who run RAG and similarity search at production scale.
Solo developers who already use pgvector for small workloads.
Common questions answered by our AI research team
The Standard Tier supports horizontal up and downscaling, but the pricing page notes there is "no automated shard rebalancing" for this tier. So while horizontal scaling is available, automated shard rebalancing is not included.
Qdrant Cloud billing is based on actual resource usage — specifically compute (vCPU), memory (GB), storage (GB) consumed by clusters, storage (GB) consumed by backups, and inference tokens used for paid models. Usage is billed hourly and can be monitored through the Qdrant Cloud dashboard. It is not per query or per vector stored.
Yes, the Premium Tier includes both Private VPC Links and Enterprise SSO Authentication. The homepage also lists SSO (SAML/OIDC) as part of the enterprise-ready tooling, confirming support for those protocols.
Yes, you can migrate from an existing Qdrant OSS deployment to Qdrant Cloud. Qdrant provides a migration tool and documentation to help with the transition, as stated in the FAQ section.
Yes, Qdrant supports official clients for Python and JavaScript, and provides both REST and gRPC APIs. The homepage states developers can "start with a single API call — scale to advanced control over HNSW, hybrid fusion, reranking, and multi-vector retrieval, all via REST, gRPC, or official clients (Python, JavaScript, etc.)."
Company
QdrantFounded
2021Pricing
From $25/moFree Trial
AvailableFree Plan
AvailableQdrant is an open-source vector database and similarity search engine written in Rust, headquartered in Berlin. It provides a service for storing, searching, and managing high-dimensional vectors with a REST and gRPC API.
