Sold by: Qdrant
Deployed on AWS
Qdrant is an open-source and fully managed high-performance Vector Database. The vector search engine provides a production-ready service with a convenient API to store, search, and manage vector embeddings.
4.5
Overview
Qdrant is an open-source and fully managed high-performance Vector Database. The vector search engine provides a production-ready service with a convenient API to store, search, and manage vectors with an additional payload Qdrant is tailored to extended filtering support on additional metadata fields, which can be stored as payload along with vector embeddings. With Qdrant, embeddings, and neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more solutions to make the most of unstructured data. It is easy to use, deploy and scale, blazing fast and accurate simultaneously.
Highlights
- Blazing Fast and Accurate
- Advanced Filtering Support
- Flexible Storage Options
Details
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/unit |
|---|---|
Qdrant cloud usage unit according to the cluster deployment. | $0.01 |
Dimensions summary
For Qdrant Cloud on AWS Marketplace, the pricing dimension "Qdrant cloud usage unit" represents the computational resources allocated to your vector database cluster deployment. The pricing is based on the size and configuration of your cluster, which includes factors such as RAM, CPU, and storage capacity. According to Qdrant's official documentation, they offer different tiers of deployment options to accommodate varying workload requirements, from development environments to production-scale implementations.
Top-of-mind questions for buyers like you
How is the Qdrant cloud usage unit calculated for billing purposes?
The Qdrant cloud usage unit is calculated based on your cluster's configuration, including RAM, CPU cores, and storage capacity. The pricing scales with your resource allocation, where larger clusters with more computational resources consume more usage units per hour of operation.
What is the minimum deployment size available on AWS Marketplace?
Qdrant offers flexible deployment options starting from development-sized clusters suitable for testing and small workloads. The exact specifications and pricing can be determined during the deployment process through the AWS Marketplace interface.
Does the usage unit pricing include high availability and backup features?
The Qdrant cloud usage unit includes high availability features with automatic failover capabilities and data replication across nodes. Additional features such as automatic backups and monitoring are included in the base pricing, though storage costs for backups may be charged separately.
Vendor refund policy
Contact support@qdrant.com
Custom pricing options
Request a private offer to receive a custom quote.
Legal
Vendor terms and conditions
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor resources
Support
Vendor support
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Updated weekly
By Qdrant
By Pinecone
By SingleStore
Top
10
In Embeddings
Top
10
In Embeddings
Top
10
In Analytic Platforms, Databases & Analytics Platforms, Databases
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Vector Search Engine
High-performance vector search engine for storing, searching, and managing vector embeddings with production-ready service capabilities
Advanced Filtering Support
Extended filtering capabilities on additional metadata fields that can be stored as payload along with vector embeddings
Flexible Storage Options
Multiple storage configuration options to support various deployment and scalability requirements
API Interface
Convenient API for storing, searching, and managing vectors with payload support
Unstructured Data Processing
Support for neural network encoders and embeddings to enable matching, searching, and recommendation applications on unstructured data
Vector Search Performance
Ultra-low query latency for vector search operations supporting billions of items with state-of-the-art vector search libraries
Real-time Index Updates
Live index updates enabling immediate data modifications when adding, editing, or deleting vector data
Metadata Filtering
Advanced filtering capabilities allowing combination of vector search with metadata filters for refined and accelerated results
Security and Compliance
SOC 2 Type II certification and GDPR compliance with enterprise-grade security measures for data protection
Serverless Infrastructure
Fully managed serverless vector database eliminating requirements for infrastructure maintenance, service monitoring, and algorithm tuning
Distributed SQL Database Architecture
Fully managed, distributed SQL database with lock-free cloud-native architecture designed for transactional (OLTP) and analytical (OLAP) workloads
High-Throughput Data Ingestion
Parallel, distributed lock-free ingestion capable of processing millions of events per second using Pipelines
Vector Search Capabilities
Indexed vector search with full-text search capabilities for generative AI applications with elastic scale-out architecture
Real-Time Query Processing
Low-latency SQL query execution on billions of rows of data with support for tens or hundreds of thousands of concurrent users
Unified Workload Engine
Single engine supporting transactional (OLTP), analytical (OLAP), and vector (GenAI) workloads without requiring data movement between systems
Standard contract
No
No
No
Customer reviews
AllanTan1
Hybrid search has improved legal and educational AI retrieval and supports fast model iteration
Reviewed on Feb 13, 2026
Review provided by PeerSpot
What is our primary use case?
My primary use cases for Qdrant are legal and educational.
What is most valuable?
The most valuable feature I have found in Qdrant is the sample code. I think they have good examples that make it developer-friendly.
Using Qdrant's hybrid search capability has improved my search results. The ability of Qdrant to handle high-dimensional vectors for my AI projects is pretty fast, and I think it's the best we have used so far. That's why we continue using it and did not check other options anymore.
The configuration of Qdrant is okay. For a developer, it was easy to set the product up and to use it.
What needs improvement?
I should check if real-time data updates in Qdrant have helped improve my models, as I don't even know they have that feature.
A lot of our work is agentic right now, and we have also segmented the content to be logical, so there's not a lot of vector search anymore. I haven't really thought of any additional features that would make Qdrant closer to a perfect score.
For how long have I used the solution?
I have been using Qdrant for two years.
How are customer service and support?
I would rate Qdrant's technical support as community-driven. There's community support since we're not paying anything, and it's more the community support for it. It's open source, so we house it on our server.
I think they provide enough information on the internet, and I am satisfied with it. They explain it well.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
I switched from Faiss because it's open source and there's not a lot of support. We were worried that moving forward, maybe no one will maintain it, so it's just good for experimenting.
How was the initial setup?
The configuration of Qdrant is okay. For a developer, it was easy to set the product up and to use it.
What's my experience with pricing, setup cost, and licensing?
Using Qdrant is free. We house it and have a VM where we just installed it on the VM.
Which other solutions did I evaluate?
Before finally choosing Qdrant, I did evaluate other options, but that was a long time ago, and I don't know what the state of vector databases is now.
What other advice do I have?
Currently, we are using a vector database called Qdrant, but most of our tasks are agentic, and we don't have it anymore. I can answer a few questions about Qdrant.
I have used Qdrant's hybrid search capability. The use of multiple query languages has impacted my data query processes mostly as Q&A.
We use the Ragas metrics to evaluate Qdrant's performance in indexing and retrieving vectors. All the metrics I consider in Ragas are useful.
In my company, we have around eight or nine people using Qdrant. I think Qdrant is popular enough in my region, but they can probably promote it more.
I rate this review a 9 out of 10.
Kawalpreet J.
A quick and easy to setup vector database for RAG needs
Reviewed on Dec 05, 2024
Review provided by G2
What do you like best about the product?
In our organization, we developed an RAG application and needed a way to store embeddings. I looked after many open-source tools like Pinecone and Superduperdb. Qdrant worked the best. The setup on our server was super easy, and their documentation is very elaborate. I also think the embedding search is more accurate than the other platforms I piloted with. We are still currently using Qdrant for our RAG application and are happy with it.
What do you dislike about the product?
Inability to perform rich operations from UI without writing code/query. For example, if I want to delete all collections or collections matching a name pattern, or even if I want to select multiple collections and delete, that is not possible through UI.
What problems is the product solving and how is that benefiting you?
Enable storing and searching of embeddings for AI applications.
Rishi K.
scalability & availability
Reviewed on Nov 28, 2024
Review provided by G2
What do you like best about the product?
fully manage in all resource ,available on AWS , Google and azure plaform help with vector search technolgy
What do you dislike about the product?
non build in visualiztion ,significantly slower searching time in result.
What problems is the product solving and how is that benefiting you?
text searching is not enough , Qdrant vector database to find the similar image its detect duplicates ,including picture by text description
Aarav M.
Self-hosted Qdrant Vector DB
Reviewed on Nov 28, 2024
Review provided by G2
What do you like best about the product?
Self-hosting Qdrant on a host is really simple and does not takes a lot of time to setup or troubleshoot issues. The documentation is also up to date. I prefer to install it using Docker to avoid installing dependencies.
What do you dislike about the product?
The initial learning curve is high but the documentation and resources makes up for it.
What problems is the product solving and how is that benefiting you?
I mainly use Qdrant for searches and building applications where I need to store vectors
Akhil G.
depth review of Qdrant.Ai
Reviewed on Sep 11, 2024
Review provided by G2
What do you like best about the product?
desparate data sources makes easier to consolidate and analyze data from various sources,scaling data,data quality and governance.
What do you dislike about the product?
Learning might be quite difficult for who are not familiar with advanved data analytics.
pricing plans are high.
pricing plans are high.
What problems is the product solving and how is that benefiting you?
using this we can unify data from different sources,with its analyzing customer data we can gain clear insight of customer behaviour