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SingleStore VS Qdrant

Compare SingleStore VS Qdrant and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Reviews
  3. ยป Alternatives

SingleStore logo SingleStore

SingleStore DB is a high-performance SQL compliant relational database management tool that offers data processing, ingesting, and transaction processing.

Qdrant logo Qdrant

Qdrant is a high-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
  • SingleStore Landing page
    Landing page //
    2022-12-11
  • Qdrant Landing page
    Landing page //
    2023-12-20

Qdrant is a leading open-source high-performance Vector Database written in Rust with extended metadata filtering support and advanced features. It deploys as an API service providing a search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications. Powering vector similarity search solutions of any scale due to a flexible architecture and low-level optimization. Qdrant is trusted and high-rated by Machine Learning and Data Science teams of top-tier companies worldwide.

SingleStore

Website
singlestore.com
Pricing URL
Official SingleStore Pricing
$ Details
-
Platforms
-
Release Date
2011 January
Startup details
Country
United States
State
California
City
San Francisco
Founder(s)
Adam Prout
Employees
250 - 499
Edit details

Qdrant

Website
qdrant.tech
Pricing URL
Official Qdrant Pricing
$ Details
freemium
Platforms
Linux Windows Kubernetes Docker
Release Date
2021 May
Edit details

SingleStore features and specs

  • High Performance
    SingleStore is designed to provide high-speed data processing capabilities, making it suitable for real-time analytics and applications that require fast data retrieval and processing.
  • Scalability
    The platform offers a distributed architecture that allows for horizontal scaling, enabling users to easily add more nodes to handle increased workloads and data volumes.
  • Unified Database
    SingleStore combines transactional and analytical workloads within a single database engine, reducing the need for separate systems and simplifying architecture.
  • Cloud-Native
    SingleStore offers cloud-native features, including seamless integration with public clouds, making it easier for businesses to deploy and manage their databases in cloud environments.
  • Compatibility with SQL
    SingleStore supports standard SQL queries, making it accessible for developers and analysts familiar with SQL, and facilitating integration with existing tools and workflows.

Possible disadvantages of SingleStore

  • Cost
    Licensing and operational costs for SingleStore can be high, especially for smaller organizations or projects with limited budgets.
  • Complexity
    Despite its powerful features, SingleStore's architecture and setup can be complex, potentially requiring specialized knowledge and expertise to optimize and maintain.
  • Limited Use Cases
    While SingleStore performs well for specific workloads like real-time analytics, it may not be the best choice for all use cases, such as those requiring specialized database solutions.
  • Vendor Lock-In
    Relying on SingleStore's proprietary technology could lead to vendor lock-in, making it challenging to migrate to other platforms without significant effort and cost.
  • Evolving Ecosystem
    As SingleStore continues to evolve, users may encounter challenges with backward compatibility or need to adapt to changes in features and functionality.

Qdrant features and specs

  • Advanced Filtering
  • On-disc Storage
  • Scalar Quantization
  • Product Quantization
  • Binary Quantization
  • Sparse Vectors
  • Hybrid Search
  • Discovery API
  • Recommendation API

Analysis of Qdrant

Overall verdict

  • Qdrant is generally well-regarded for its performance and ease of use in managing vector data. Many users find it effective for building applications that require advanced search capabilities, particularly those involving machine learning models. However, its suitability can depend on specific project requirements and constraints, such as the existing tech stack and expected workloads.

Why this product is good

  • Qdrant is a vector database and similarity search engine designed for storing and querying high-dimensional data. It's especially effective for applications like neural search or recommendation systems, due to its ability to efficiently handle large-scale vector embeddings. Qdrant offers features such as real-time updates, seamless integration with existing data pipelines, and high availability, which make it an appealing choice for developers looking for a robust and scalable solution.

Recommended for

  • Developers building AI-powered applications
  • Companies needing efficient similarity search mechanisms
  • Teams implementing recommendation systems
  • Projects requiring real-time data processing
  • Applications dealing with large-scale vector data

Category Popularity

0-100% (relative to SingleStore and Qdrant)

SingleStore

Qdrant

Data Dashboard
100 100%
Data Dashboard
0% 0
Databases
0 0%
Databases
100% 100
Development
100 100%
Development
0% 0
Search Engine
0 0%
Search Engine
100% 100

Questions & Answers

As answered by people managing SingleStore and Qdrant.

Why should a person choose your product over its competitors?

Qdrant's answer:

Advanced Features, Performance, Scalability, Developer Experience, and Resources Saving.

What makes your product unique?

Qdrant's answer:

Highest performance https://qdrant.tech/benchmarks/, scalability and ease of use.

Which are the primary technologies used for building your product?

Qdrant's answer:

Qdrant is written completely in Rust. SDKs available for all popular languages Python, Go, Rust, Java, .NET, etc.

User comments

Share your experience with using SingleStore and Qdrant. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, Qdrant seems to be a lot more popular than SingleStore. While we know about 64 links to Qdrant, we've tracked only 3 mentions of SingleStore. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

SingleStore mentions (3)

  • Ask HN: Who is hiring? (February 2023)
    SingleStoreDB (formerly MemSQL) (https://singlestore.com) | India | Full Time | Remote SingleStoreDB is a database focused on high performance and hybrid workloads (HTAP). Our customers include half of the top 10 US banks, 2 of the top 3 US telcos, and 12% of the Fortune 100. Our product is a distributed, relational database that handles both transactions and real-time analytics at scale. Querying is done through... - Source: Hacker News / over 3 years ago
  • libschema now supports SingleStore
    Libschema now supports SingleStore in addition to PostgreSQL and MySQL. Source: almost 4 years ago
  • Ask HN: Who is hiring? (January 2022)
    SingleStore (formerly MemSQL) (https://singlestore.com) | Lisbon (Portugal), San Francisco, London (UK), Raleigh (NC), and Seattle | Full Time | Remote SingleStore is a database startup focused on high performance and hybrid workloads (HTAP). Our customers include half of the top 10 US banks, 2 of the top 3 US telcos, and 12% of the fortune 100. You can read all about our product here:... - Source: Hacker News / over 4 years ago

Qdrant mentions (64)

  • Kdrant: an idiomatic, coroutine-first Kotlin client for Qdrant
    If you build on the JVM and want to use Qdrant, the official client is io.qdrant:client โ€” and it's built for Java. Every call returns a ListenableFuture, requests are assembled with protobuf builders, and it drags a gRPC/Netty stack onto your classpath. From Kotlin, that means fighting the language:. - Source: dev.to / about 1 month ago
  • How to give Claude Code persistent memory with a self-hosted mem0 MCP server
    The stack runs on Qdrant for vector storage, Ollama for local embeddings, and optional Neo4j for a knowledge graph that I added later. I also set it up to route different operations to the best LLM for each task. It provides eleven tools for your Claude Code instance to manage long-term memory operations, and your memories data never leaves your machine. - Source: dev.to / 6 months ago
  • The Database Zoo: Vector Databases and High-Dimensional Search
    Qdrant: Open-source vector database optimized for hybrid search and easy integration with ML workflows. - Source: dev.to / 9 months ago
  • Java's Agentic Framework Boom is a Code Smell
    Yes, Java SDKs are critical. But you don't need to rebuild entire orchestration engines just to write agents in Java. The ecosystem already has platforms solving the hard problems: memory (Zep, Mem0, LangMem), tools (specialized platforms), vectors (Pinecone, Weaviate, Qdrant), observability (LangSmith, Helicone, Langfuse). Integrate, don't rebuild. - Source: dev.to / 10 months ago
  • What is the Most Effective AI Tool for App Development Today?
    James Allsopp adds, "LangChain or LlamaIndex for managing LLM workflows, especially if you're adding vector search or documents." These tools handle multi-step processes, essential for complex apps. - Source: dev.to / about 1 year ago
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What are some alternatives?

When comparing SingleStore and Qdrant, you can also consider the following products

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

Weaviate - Welcome to Weaviate

MapR Converged Data Platform - An enterprise-grade distributed data platform that you can trust to reliably store and process big and fast data.

Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.

Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost

Vespa.ai - Store, search, rank and organize big data

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