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Qdrant

  • What it is:Qdrant is an open-source vector database and vector search engine written in Rust, designed to provide fast and scalable vector similarity search for high-dimensional data.
  • Best for:AI/ML engineering teams, Cost-conscious startups, Multi-cloud enterprises
  • Pricing:Free tier available, paid plans from Usage-based starting at $0.01/unit
  • Rating:82/100Very Good
  • Expert's conclusion:If your organization is looking for an open-source vector database that can provide high-performance, advanced payload filtering and flexible deployment options (both self-hosted and cloud-managed), then Qdrant is likely the best choice for your production vector search workload.
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Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

What Is Qdrant and What Does It Do?

Qdrant is a vector database and search engine developed in Rust, as an open-source solution allowing companies to store, index and search large numbers of vector embeddings quickly and efficiently. The company was established in Berlin in 2021 and offers high-performance and scalable vector similarity search infrastructures for AI-related applications such as semantic search, recommendations, and retrieval of generative AI tasks.

Active
📍Berlin, Germany
📅Founded 2021
🏢Private
TARGET SEGMENTS
AI DevelopersEnterprise AI TeamsMachine Learning EngineersStartups

What Are Qdrant's Key Business Metrics?

📊
23,000+
GitHub Stars
📊
10 million+
Downloads
📊
7,500+
Community Members
🏢
20+ countries
Team Presence
📊
$9.7 million
Total Funding

How Credible and Trustworthy Is Qdrant?

82/100
Good

Qdrant has a solid technical basis with a mature open-source project, significant community acceptance, and strong financial backing. It has a solid market position with reliable enterprise-level features.

BREAKDOWN
Product Maturity85/100
Company Stability80/100
Security & Compliance85/100
User Reviews80/100
Transparency85/100
Support Quality75/100
TRUST SIGNALS
Open-source with 23K+ GitHub stars10M+ downloads demonstrating broad adoptionBacked by established investors including Unusual VenturesUsed by ambitious startups and enterprise-scale deploymentsWritten in Rust for reliability and performanceEnterprise-ready managed cloud service

What is the history of Qdrant and its key milestones?

2021

Company Founded

The founders of Qdrant are Andrei Zayarni (CEO), and Andrey Vasnetsov (CTO) who created the first version of the software available on Github; it attracted immediate attention from developers due to its open-source nature.

2022

Pre-Seed Funding

In 2021, the company raised €2 million ($2.2 million) in a pre-seed round of funding to continue developing their vector search engine.

2023

Seed Funding & Cloud Launch

Qdrant announced they had secured $7.5 million in a Series A round of funding led by Unusual Ventures along with participation from 42cap and IBB Ventures. They also launched a managed cloud service with one-click deployments and automatic upgrades.

2024-2025

Enterprise Expansion

The number of employees at Qdrant increased to over 75, across more than 20 different countries. They also released their enterprise product which can be used for on-premises and private-cloud deployments. Their cloud infrastructure continues to expand into AWS, GCP, and Azure.

What Are the Key Features of Qdrant?

✨
High-Performance Vector Similarity Search
Qdrant delivers up to 4x RPS (requests per second) with virtually no latency, providing very fast and efficient vector similarity searches at scale.
📊
Advanced Compression
In addition to supporting other forms of compression, Qdrant supports three types of quantization (scalar, product, binary) to reduce memory requirements by as much as 40 times without reducing search performance.
💬
Multi-Vector Data Support
Qdrant supports the following types of vectors: dense vectors, sparse vectors, multivectors, and named vectors, providing support for various AI and machine learning use-cases.
✨
Distributed Cloud-Native Architecture
Qdrant provides both vertical and horizontal scalability with no downtime required when upgrading, using managed cloud services from AWS, GCP, and Azure.
🔗
Easy-to-Use API
Qdrant offers an OpenAPI v3 specification, so clients can generate libraries in almost any programming language; and they can deploy them easily using Docker.
📊
Enterprise-Grade Security
Offers enterprise level access control, back-up options, disaster recovery, and fully customized enterprise solutions for critical business deployments.
✨
Open-Source Foundation
Open source software, offering both enterprise ready, managed cloud and self-deployment options, allowing maximum flexibility and potential cost savings when deploying your application.

What Technology Stack and Infrastructure Does Qdrant Use?

Infrastructure

Cloud-native architecture with managed services on AWS, GCP, and Azure supporting vertical and horizontal scaling with zero-downtime upgrades

Technologies

RustOpenAPI v3

Integrations

Docker deploymentAWS, GCP, Azure cloud platformsLanguage-agnostic API clients

AI/ML Capabilities

Specialized vector similarity search engine with support for multiple vector types (dense, sparse, multivectors, named vectors), quantization for efficient rescoring, and optimized performance for semantic search, recommendation systems, retrieval-augmented generation (RAG), and AI-driven applications

Based on official product documentation and company website

What Are the Best Use Cases for Qdrant?

AI/ML Developers Building LLM Applications
Use Qdrant to build production grade AI apps utilizing vector search to perform retrieval augmented generation (RAG), enabling you to integrate unstructured data in real time into large language models.
Enterprise Search and Recommendations Teams
Utilize Qdrant to power semantic search engines and recommendation systems providing personalized user experience and increasing revenue through accurate vector similarity matching on billions of vectors.
Data Science Teams in Startups
Take advantage of open source infrastructure with enterprise level features at minimum costs and deploy and prototype AI apps quickly without worrying about being locked into a specific vendor.
E-Commerce and Content Platforms
Build advanced recommendation engines and low latency/high accuracy product/image search functionality to increase customer engagement and conversion rates.
NLP and Image Analysis Applications
Store and perform high dimensional embeddings searches from NLP and computer vision models to enable real-time semantic understanding over text and image based data.
NOT FORTraditional Relational Database Users Requiring ACID Compliance
Unsuitable - Qdrant is an optimized vector search engine and will not provide the strong transactional guarantees or relational model support found in traditional enterprise class databases.
NOT FORLow-Latency Trading Systems
Sub-optimal - While Qdrant provides fast search performance, its primary design focus is on AI/ML workload type applications and not on the ultra-low millisecond response times required in financial trading operations.
NOT FORUnstructured Data Ingestion at Massive Scale Without ML Context
Not Recommended - Data must be first converted to a vector format before it can be stored/searched in Qdrant; therefore, it is not suited for raw file storage and/or data that is not represented in vector form.

How Much Does Qdrant Cost and What Plans Are Available?

Pricing information with service tiers, costs, and details
☐Service$CostℹDetails🔗Source
Managed Cloud Free Tier$01GB free cluster forever, no credit card required, fully managed with central cluster management—
Managed Cloud PaidUsage-based starting at $0.01/unitBilled on CPU, memory, disk usage. Multiple cloud providers (AWS, GCP, Azure), horizontal/vertical scaling, HA, backups, standard supportOfficial pricing page
Hybrid CloudCustom quoteBring your own cluster (cloud, on-prem, edge), connect to managed cloud control plane, standard supportOfficial pricing page
Private CloudCustom quoteFully on-premise deployment for data sovereignty, premium support requiredOfficial pricing page
Managed Cloud Free Tier$0
1GB free cluster forever, no credit card required, fully managed with central cluster management
Managed Cloud PaidUsage-based starting at $0.01/unit
Billed on CPU, memory, disk usage. Multiple cloud providers (AWS, GCP, Azure), horizontal/vertical scaling, HA, backups, standard support
Official pricing page
Hybrid CloudCustom quote
Bring your own cluster (cloud, on-prem, edge), connect to managed cloud control plane, standard support
Official pricing page
Private CloudCustom quote
Fully on-premise deployment for data sovereignty, premium support required
Official pricing page

How Does Qdrant Compare to Competitors?

FeatureQdrantPineconeWeaviateMilvus
Core Vector SearchYesYesYesYes
Advanced FilteringYes (Payload)YesYesLimited
Horizontal ScalingYesYesYesYes
Multi-Cloud SupportYes (AWS/GCP/Azure)AWS onlyYesSelf-managed
Managed CloudYesYesYesLimited
On-PremiseYesNoYesYes
Starting Price$0 (1GB free)$0.096/hr$25/moSelf-hosted free
Free TierYes (1GB)Yes (limited)YesOpen source
Enterprise SSOYes (Custom)YesYesYes
API AvailabilityREST/gRPC/PythonREST/PythonGraphQL/RESTREST/gRPC
Security CertificationsSOC2 (planned)SOC2ISO27001Enterprise custom
Support OptionsStandard/PremiumEnterpriseCommunity/ProCommunity/Enterprise
Core Vector Search
QdrantYes
PineconeYes
WeaviateYes
MilvusYes
Advanced Filtering
QdrantYes (Payload)
PineconeYes
WeaviateYes
MilvusLimited
Horizontal Scaling
QdrantYes
PineconeYes
WeaviateYes
MilvusYes
Multi-Cloud Support
QdrantYes (AWS/GCP/Azure)
PineconeAWS only
WeaviateYes
MilvusSelf-managed
Managed Cloud
QdrantYes
PineconeYes
WeaviateYes
MilvusLimited
On-Premise
QdrantYes
PineconeNo
WeaviateYes
MilvusYes
Starting Price
Qdrant$0 (1GB free)
Pinecone$0.096/hr
Weaviate$25/mo
MilvusSelf-hosted free
Free Tier
QdrantYes (1GB)
PineconeYes (limited)
WeaviateYes
MilvusOpen source
Enterprise SSO
QdrantYes (Custom)
PineconeYes
WeaviateYes
MilvusYes
API Availability
QdrantREST/gRPC/Python
PineconeREST/Python
WeaviateGraphQL/REST
MilvusREST/gRPC
Security Certifications
QdrantSOC2 (planned)
PineconeSOC2
WeaviateISO27001
MilvusEnterprise custom
Support Options
QdrantStandard/Premium
PineconeEnterprise
WeaviateCommunity/Pro
MilvusCommunity/Enterprise

How Does Qdrant Compare to Competitors?

vs Pinecone

Qdrant has greater deployment flexibility (Multi-Cloud, Hybrid and On-Prem) compared to Pinecones AWS Only Managed Service Option; Qdrants Free Tier Has Greater Storage Capabilities (1GB Forever) But Pinecones Cloud Serverless Deployment Model Is More Simplified For Pure Cloud Needs.

Therefore, while Qdrant would be best suited for developers that want flexibility in their deployment options and those looking for cost-effective solutions, Pinecone would be better suited for customers looking for a completely managed solution.

vs Weaviate

The two are both open-source and both have excellent filtering. However, Qdrant has an edge when it comes to pure vector performance because it is a Rust-based product. On the other hand, Weaviate performs better for hybrid search + knowledge graph applications. The two also have similar managed cloud pricing.

Based upon your specific needs, if you need to perform high-speed vector similarity searches, then Qdrant would be a good fit for you. If you need to perform semantic searches along with vector searches, then Weaviate would be the better choice.

vs Milvus

Both products are open-source and can be scaled natively using Kubernetes. In addition, Qdrant has a much simpler API as well as better default payload filtering than does Milvus. Although Milvus is more mature at massive scale, it has significantly more operational complexity.

Therefore, if you are a developer that wants to create AI applications, but still want ease of development and want to take advantage of high-performance vector similarity searching, then Qdrant would be the best choice. However, if you plan to deploy applications that exceed petabytes in size, then Milvus would likely be the better fit due to its ability to scale beyond what most competitors offer.

vs Chroma

While Qdrant is ready for production and has all of the typical features you would find in an enterprise product, Chroma is focused on creating an extremely lightweight embedding database product. As such, Qdrant will always scale better horizontally.

Therefore, if you are deploying AI applications into production, then Qdrant would be the better choice. On the other hand, if you are only doing proof of concept or small-scale testing, then Chroma would be a good fit.

What are the strengths and limitations of Qdrant?

Pros

  • Extremely Fast Performance – Because of its Rust based architecture, Qdrant is able to handle billions of vectors at incredibly fast speeds.
  • Extensive Free Tier Options – With a free tier that includes a 1 GB cluster available forever, as well as the fact that no credit card is required to sign up for this tier, it makes it very simple for new users to try out the application.
  • High Levels Of Flexibility – Because Qdrant supports multiple types of deployment configurations, including cloud, hybrid, on-premise and multi-cloud environments, it offers its users the highest levels of flexibility.
  • Advanced Filtering Capabilities – Due to the fact that Qdrant allows for powerful payload/metadata filtering, its users can filter down large amounts of data quickly and easily.
  • Simple Local Development Environment – For users that want to test and develop locally before moving into the cloud, Qdrant makes it very easy by allowing them to simply place one Docker container onto their local environment and begin developing immediately.
  • Ready For Production – With Qdrant’s production-ready features, including HA, backups, and zero-downtime upgrades in the cloud, it provides its users with a great way to deploy their applications to the cloud.
  • Open Source Core – With its open-source core, users of Qdrant do not have to worry about being locked into any particular vendor.

Cons

  • Pricing Model Opaque – Due to the fact that Qdrant uses a usage-based pricing model, it can sometimes be difficult for users to determine how much they will be charged each month until they have run the pricing calculator.
  • Pure Consumption-Based Pricing Model – Because Qdrant charges its users solely based upon how much of the application they consume, it may sometimes be difficult for users to budget for these costs.
  • Custom Enterprise Features Require Sales Contact – While many of Qdrant’s features can be purchased separately, some of the company’s premium features, including SSO and advanced technical support, require users to make contact with a sales representative before they can purchase.
  • Young Ecosystem – Although Qdrant has a growing ecosystem of users, it still lags behind many of the more established databases in terms of the number of third-party integrations available.
  • Cloud-Only Management – When users choose to host Qdrant themselves, they lose access to the centralized monitoring and alerting functionality that is available to cloud-based users.
  • Learning Curve For Rust Programming Language – Users who wish to customize the Qdrant product for their own use will often find that they need to have a significant amount of knowledge of the Rust programming language in order to accomplish this.
  • Limited User Interface – While Qdrant provides its users with a simple-to-use web-based console for managing their applications, the majority of the time users will be interacting directly with the application via its API.

Who Is Qdrant Best For?

Best For

  • AI/ML engineering teams — High-Performance Vector Search With Easy Docker Deployment For Prototyping To Production
  • Cost-conscious startups — Qdrant has a generous 1GB free tier that can be scaled based on usage (no commitment) similar to how you would use a paid option.
  • Multi-cloud enterprises — Qdrant is available through all of the major marketplaces (AWS/GCP/Azure) and also allows hybrid/on-prem options if needed.
  • RAG application developers — Qdrant uses excellent filtering + vector search, which are both highly optimized for use in LLM (large language model) retrieval pipeline workflows.
  • Teams needing data sovereignty — When you deploy Qdrant into your own private cloud, you will maintain complete control over the underlying infrastructure.

Not Suitable For

  • Budget fixed-price seekers — The cost of using AWS/GCP/Azure (usage-based) will vary from month-to-month compared to the predictable monthly costs of Pinecone's usage-based pod pricing. If you plan to self-host Chroma, consider its lower costs.
  • Non-technical business users — While Qdrant is designed as a API-centric solution and does not have a visual query builder like Weaviate (GraphQL UI), this should not prevent you from using it.
  • Simple prototype-only projects — Qdrant may be overkill when compared to other lightweight options such as Chroma or FAISS.
  • Immediate enterprise compliance needs — At the time of writing, Qdrant’s SOC2/ISO compliance was still pending. As of the writing of this comparison document, Pinecone already had SOC2 compliance.

Are There Usage Limits or Geographic Restrictions for Qdrant?

Free Tier Storage
1GB cluster forever
Billing Model
CPU + memory + disk usage-based, $0.01/unit via marketplaces
Cluster Scaling
Horizontal and vertical, limits depend on cloud provider quotas
Deployment Regions
Multiple AWS/GCP/Azure regions
Self-Hosted
No cloud management features, standard support only
API Rate Limits
Not publicly documented, contact support for high-throughput
Hybrid Cloud
Requires customer infrastructure + Qdrant Cloud control plane
Private Cloud
Premium support required, fully air-gapped capable

Is Qdrant Secure and Compliant?

High AvailabilityAuto-healing clusters, multi-region redundancy, zero-downtime upgrades
Data IsolationDedicated clusters per customer, security isolation in hybrid/private cloud
EncryptionStandard TLS encryption in transit, customer-managed infra controls at-rest
Access ControlUnlimited users with role management in cloud console
Monitoring & LoggingCentral monitoring, log management, alerting included
Backup & RecoveryAutomated backups and disaster recovery standard
Compliance CertificationsSOC2 Type II and ISO27001 in process, contact sales for status
SSO/SAML SupportEnterprise feature, available in custom deployments

What Customer Support Options Does Qdrant Offer?

Channels
support@qdrant.techCommunity support for open sourceActive developer communitySelf-service guides and tutorials
Hours
Community support 24/7, Enterprise support business hours
Response Time
Community: best effort, Enterprise: SLA guaranteed
Satisfaction
Positive developer feedback on GitHub and forums
Specialized
Dedicated support for Qdrant Cloud Enterprise customers
Business Tier
Priority support, high availability SLAs, dedicated account managers
Support Limitations
•Open source version limited to community support
•No phone support
•Enterprise features require paid Cloud plan

What APIs and Integrations Does Qdrant Support?

API Type
REST API, gRPC, OpenAPI specification
Authentication
API Key, Service Account tokens
Webhooks
Not natively supported, use collection watch API
SDKs
Python, JavaScript/TypeScript, Rust, Go, Java, .NET, PHP
Documentation
Comprehensive at qdrant.tech/documentation with interactive examples
Sandbox
Qdrant Cloud free tier available for testing
SLA
99.95% uptime for Cloud, sub-20ms p95 query latency
Rate Limits
Configurable per cluster, defaults prevent overload
Use Cases
RAG systems, semantic search, recommendation engines, AI agents

What Are Common Questions About Qdrant?

Qdrant is an open-source vector database developed with Rust for indexing and searching large amounts of high-dimensional vectors along with their payloads. In terms of features, Qdrant performs better than most databases at performing similarity searches with the added capability of advanced filtering for production AI applications.

Both Qdrant and Pinecone offer both self-hosted open-source versions and cloud-managed versions; however, the only fully managed version offered by Qdrant is its Cloud version. Qdrant has superior filtering functionality with payload indexes, but runs on your infrastructure providing complete control of your data.

Yes. Qdrant supports encrypting both at rest and in transit. Qdrant also provides role-based access control. Additionally, running on your infrastructure, Qdrant also supports running within your VPC. The Cloud offering includes SOC2 compliance and also private networking options.

Since Qdrant is an open-source product, there is no charge to download or use it. However, Qdrant Cloud is available on a pay-as-you-go basis, with costs beginning at $0.05/hour per pod and Enterprise pricing for production workloads. A free tier is provided for testing purposes.

Yes. Qdrant can be deployed via Docker, Kubernetes, or any cloud provider. For developers, single node deployments provide a simple way to test and develop new applications. For production environments, Qdrant can scale to many nodes with high availability configurations.

Any number of dimensions less than 65,000. Typical size ranges include 768 (used by OpenAI), 384 (used by MiniLM), and 1536 (used by others). There is no required number of dimensions.

Yes. Qdrant combines both vector similarity searches and full-text searches along with metadata filtering in a single query. This makes Qdrant ideal for RAG (retrieve-and-generate) applications that require both semantic and keyword relevance.

Distributed horizontally to support vector search on billions of vectors across multiple cluster nodes; The cloud service will automatically scale as needed, while the community-supported open source can be used for shard-based scaling or replication for production workloads.

Is Qdrant Worth It?

Qdrant stands out among other open-source vector databases because it has production-quality performance, advanced filtering capabilities, and flexible deployment options; With its Rust foundation, Qdrant has a level of speed and reliability that no other open-source vector database currently offers, and Qdrant’s Cloud Service provides enterprise-level SLA guarantees. It is ideal for teams that need both development agility and production-level scalability.

Recommended For

  • AI/ML engineering teams developing RAG and semantic search applications
  • Companies that need data sovereignty and/or self-hosted deployment options
  • Production environments that process billions of vectors, and have filtering requirements
  • Teams looking to transition from Pinecone due to cost concerns, and wanting greater control over their vector search environment

!
Use With Caution

  • Teams that do not have experience with containerized/k8s deployments for self-hosted Qdrant deployments
  • Small projects that require simple vector search solutions
  • Organizations that want to take advantage of a fully integrated managed services ecosystem as quickly as possible

Not Recommended For

  • Non-technical teams that cannot manage their own infrastructure
  • Teams are doing only prototyping, and therefore do not need to use heavy-weight vector search solutions
  • Budget-constrained startups who are unwilling to make investments in operational infrastructure
Expert's Conclusion

If your organization is looking for an open-source vector database that can provide high-performance, advanced payload filtering and flexible deployment options (both self-hosted and cloud-managed), then Qdrant is likely the best choice for your production vector search workload.

Best For
AI/ML engineering teams developing RAG and semantic search applicationsCompanies that need data sovereignty and/or self-hosted deployment optionsProduction environments that process billions of vectors, and have filtering requirements

What do expert reviews and research say about Qdrant?

Key Findings

For organizations with large-scale production RAG (Relevance-Aware Graph) systems that require sub-20ms latency at scale (i.e., millions/billions of vectors), and require advanced filtering capabilities, Qdrant is the current leader in the open-source vector database space. In addition to its Rust-based performance advantages, Qdrant also includes mature SDKs, and has reached production-ready levels for both self-hosted and cloud-managed deployments.

Data Quality

Excellent - comprehensive documentation, official website, GitHub activity, and third-party benchmarks confirm technical capabilities and adoption.

Risk Factors

!
Self-hosted deployments of Qdrant will require DevOps-level expertise.
!
Vector DB technology is rapidly evolving, and there is significant activity occurring in this area today.
!
Features typically available to enterprise customers are behind a paywall in Qdrant's cloud offering.
Last updated: January 2026

What Additional Information Is Available for Qdrant?

Open Source Community

An active GitHub repository with > 15k stars, and a large presence in Discord and Slack communities exist for Qdrant. Regular contributor events and office hour sessions with the core team members occur frequently.

Deployment Flexibility

Runs everywhere: Docker, Kubernetes (OCI certified), AWS Marketplace, serverless. Upgrades to new versions of Qdrant Cloud are available with zero downtime.

Performance Benchmarks

Benchmark tests from independent organizations show that Qdrant is better than competitors when it comes to Quality Per Second (QPS) and latency. Qdrant can index 100k + vectors per second.

Ecosystem Integrations

Native integration for LangChain, LlamaIndex, Haystack; works with most popular embedding models and LLM frameworks.

Rust Foundation

Written in Rust so that it can be fast and safe with memory. Billion-vectors scales without pausing due to garbage collection because of "zero-cost" abstractions.

What Are the Best Alternatives to Qdrant?

  • •
    Pinecone: A fully-managed vector database offering a good developer experience but not allowing you to self-host. Less complicated to operate at a cost of less control and greater cost. For those that value ease-of-use over flexibility. (pinecone.io)
  • •
    Weaviate: An open-source vector DB using a GraphQL API with built-in machine learning modules. More involved to set up, stronger knowledge-graph capabilities. Good for teams needing both structured and unstructured search. (weaviate.io)
  • •
    Milvus: An open-source vector DB optimized for use on very large scales with multiple index types. Higher resource utilization, more difficult to learn. Good for massive petabyte-scale computer vision applications. (milvus.io)
  • •
    Chroma: A lightweight open-source embedding database for prototyping. Much easier to set up and much smaller, however it does not have enough functionality or scalability to be used in a production environment. Good for local development and small apps. (chroma.run)
  • •
    PGVector: A PostgreSQL extension for performing vector searches. Uses an existing SQL infrastructure as well as having slower performance and fewer options for filtering. Good for teams that prefer to use SQL and do not want to create additional databases. (supabase.com)

What Are Qdrant's Vector Db Performance?

sub-20ms
Query Latency
>100,000 vectors/sec
Indexing Throughput
Highest RPS
Search Speed
40x
Search Performance Improvement

What Is Qdrant's Vector Db Scalability?

Max Vectors
Billions
Horizontal Scaling
Supported with distributed architecture
Sharding Support
Automatic partitioning
Replication
Multi-replica with consistency options

What Vector Db Index Types Does Qdrant Support?

HNSW

Custom HNSW algorithm modification for fast and precise approximate nearest neighbor search

What Vector Db Features Does Qdrant Offer?

Advanced Compression

Scalar, Product, Binary Quantization (40x faster)

Multitenancy Support

Isolation and privacy through segment collections

Payloads & Advanced Filtering

String matching, numerical ranges, geographic locations

Sparse Vector Support

Fastest way to perform text retrieval

Memory Maps & IO Uring

Storage on disk has been optimized

Hybrid Search

Vector and Keyword through Cloud Inference

Multi-Modal Support

Text and Image Embeddings

What Is Qdrant's Vector Db Deployment?

Cloud Managed
Qdrant Cloud (fully managed with Inference)
Self Hosted
Docker, standalone binary (open source)
Kubernetes
Helm charts and operators
Serverless
Pay-per-query via Qdrant Cloud

What Vector Db Distance Metrics Does Qdrant Support?

CosineEuclidean (L2)Dot Product

What Vector Db Integrations Does Qdrant Offer?

LangChain

Native vector-store integration

LlamaIndex

Python SDK

REST API (OpenAPI v3)

gRPC

Rust Client

Various Language Clients

Expert Reviews

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