Software Alternatives, Accelerators & Startups

Google Cloud SQL VS Qdrant

Compare Google Cloud SQL VS Qdrant and see what are their differences

Google Cloud SQL logo Google Cloud SQL

Google Cloud SQL is a fully-managed database service that makes it easy to set-up, maintain, manage and administer your MySQL database.

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/
  • Google Cloud SQL Landing page
    Landing page //
    2023-09-18
  • 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.

Google Cloud SQL

Pricing URL
-
$ Details
-
Platforms
-
Release Date
-

Qdrant

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

Google Cloud SQL features and specs

No features have been listed yet.

Qdrant features and specs

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

Google Cloud SQL videos

GCP | Google Cloud SQL | Cloud SQL Features , Read Replicas & High Availability | DEMO

Qdrant videos

No Qdrant videos yet. You could help us improve this page by suggesting one.

+ Add video

Category Popularity

0-100% (relative to Google Cloud SQL and Qdrant)
Databases
44 44%
56% 56
Search Engine
0 0%
100% 100
Relational Databases
100 100%
0% 0
NoSQL Databases
100 100%
0% 0

Questions and Answers

As answered by people managing Google Cloud SQL 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 Google Cloud SQL 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 should be more popular than Google Cloud SQL. It has been mentiond 39 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.

Google Cloud SQL mentions (15)

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Qdrant mentions (39)

  • How to Build a Chat App with Your Postgres Data using Agent Cloud
    AgentCloud uses Qdrant as the vector store to efficiently store and manage large sets of vector embeddings. For a given user query the RAG application fetches relevant documents from vector store by analyzing how similar their vector representation is compared to the query vector. - Source: dev.to / 11 days ago
  • Hindi-Language AI Chatbot for Enterprises Using Qdrant, MLFlow, and LangChain
    Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker. - Source: dev.to / 22 days ago
  • Boost Your Code's Efficiency: Introducing Semantic Cache with Qdrant
    I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours. - Source: dev.to / 29 days ago
  • Ask HN: Has Anyone Trained a personal LLM using their personal notes?
    I'm currently looking to implement locally, using QDrant [1] for instance. I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2]. [1]. https://qdrant.tech/. - Source: Hacker News / about 2 months ago
  • Open-source Rust-based RAG
    There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb. - Source: Hacker News / 2 months ago
View more

What are some alternatives?

When comparing Google Cloud SQL and Qdrant, you can also consider the following products

PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.

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

MySQL - The world's most popular open source database

Weaviate - Welcome to Weaviate

Oracle DBaaS - See how Oracle Database 12c enables businesses to plug into the cloud and power the real-time enterprise.

pgvecto.rs - Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs