Software Alternatives, Accelerators & Startups

Qdrant VS PostgresML

Compare Qdrant VS PostgresML and see what are their differences

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/

PostgresML logo PostgresML

You know Postgres.
  • 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.

  • PostgresML Landing page
    Landing page //
    2023-11-10

Qdrant

$ Details
freemium
Platforms
Linux Windows Kubernetes Docker
Release Date
2021 May

PostgresML

$ Details
Platforms
-
Release Date
-

Qdrant features and specs

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

PostgresML features and specs

No features have been listed yet.

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 Qdrant and PostgresML)
Databases
91 91%
9% 9
AI
0 0%
100% 100
Search Engine
89 89%
11% 11
Developer Tools
100 100%
0% 0

Questions & Answers

As answered by people managing Qdrant and PostgresML.

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 Qdrant and PostgresML. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, Qdrant should be more popular than PostgresML. It has been mentiond 63 times since March 2021. 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.

Qdrant mentions (63)

  • 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 / 5 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 / 7 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 / 8 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 / 11 months ago
  • ๐Ÿ”ฅ Build a RAG Chatbot That Talks to Your Documents Using Python (Gemma + Qdrant + Docling)
    ๐Ÿ“ฆ Qdrant for fast vector search and retrieval. - Source: dev.to / 11 months ago
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PostgresML mentions (7)

  • AI-pipe: Pipeline for generating/storing embeddings from AI models to DB with data scraped from sites using custom scripts
    The web service supports generating embeddings from OpenAI and Ollama AI models. It also provides a fallback for users without access to AI models running on a remote server through PostgresML. - Source: dev.to / over 1 year ago
  • Better RAG Results with Reciprocal Rank Fusion and Hybrid Search
    That's outside of the database, though. This is more like what I had in mind -- I just found it: https://postgresml.org/. - Source: Hacker News / about 2 years ago
  • How Modern SQL Databases Are Changing Web Development - #4 Into the AI Era
    Some excellent tools were created to represent these tasks "naturally" in SQL and even let most of the computation happen inside the database. PostgresML is a great example. It's built above PostgreSQL and provides a set of functions that allow you to train and use machine learning models with SQL. Here's how you can train a classification model for the classic handwritten digit recognition problem:. - Source: dev.to / over 2 years ago
  • A Year of Self-Hosting: 6 Open-Source Projects That Surprised Me in 2023
    PostgresML | You know Postgres. Now you know machine learning โ€“ PostgresML. - Source: dev.to / over 2 years ago
  • OpenAI Switch Kit: Swap OpenAI with any open-source model
    You can swap in almost any open-source model on Huggingface. HuggingFaceH4/zephyr-7b-beta, Gryphe/MythoMax-L2-13b, teknium/OpenHermes-2.5-Mistral-7B and more.If you haven't seen us here before, we're PostgresML, an open-source MLOps platform built on Postgres. We bring ML to the database rather than the other way around. Source: over 2 years ago
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What are some alternatives?

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

Weaviate - Welcome to Weaviate

Talk To Your Data App - Tak to your data in natural language, no technical skills required. PostgreSQL, MySQL, HubSpot, Mailchimp & many more SaaS platforms. Get instant answers, visualizations & insights.

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

Pinecone - Search through billions of items for similar matches to any object, in milliseconds. Itโ€™s the next generation of search, an API call away.

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

ChatWithCloud AI - Chat with your AWS Cloud from Terminal. Talk to your Cloud, literally.