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PostgreSQL VS Milvus

Compare PostgreSQL VS Milvus and see what are their differences

PostgreSQL

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

Rating
0 reviews
Pricing
Open source
Alternatives Is it good?
Milvus

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

Rating
0 reviews
Pricing
Open source Free
Alternatives Is it good?
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Which is more popular?

Based on our record, Milvus should be more popular than PostgreSQL. It has been mentioned 40 times since March 2021.

social mentions
19 vs 40
Databases popularity
91% vs 9%
alternatives listed
240+ vs 191

Base details

Website, pricing, platforms and company facts side by side.

PostgreSQL
Milvus
Website postgresql.org github.com
Pricing
Open source Official pricing
Open source Free
Company Startup from the United States 2019
Listed in
Databases Relational Databases NoSQL Databases Tool +1
Vector Databases Retrieval Augmented Generation Search Engine Databases Custom Search Engine Utilities +3

About PostgreSQL and Milvus

In their own words, as submitted to SaaSHub.

PostgreSQL
Milvus

No description of PostgreSQL yet.

Milvus is a highly flexible, reliable, and blazing-fast cloud-native, open-source vector database. It powers embedding similarity search and AI applications and strives to make vector databases accessible to every organization. Milvus can store, index, and manage a billion+ embedding vectors...

Read more about Milvus

Features and specs

What each product offers, as listed by its team.

PostgreSQL 7 features
Milvus 6 features
  • Open Source
    PostgreSQL is an open-source database management system, which means it is free to use, modify, and distribute. This reduces the cost of database management for individuals and organizations.
  • ACID Compliance
    PostgreSQL is fully ACID (Atomicity, Consistency, Isolation, Durability) compliant, ensuring reliable transactions and data integrity.
  • Extensible
    PostgreSQL is highly extensible, allowing users to add custom functions, data types, and operators. This enables tailored solutions to specific requirements.
  • Advanced SQL Features
    PostgreSQL supports advanced SQL features like full-text search, JSON and XML data types, and complex queries, providing powerful tools for database operations.
  • Community Support
    There is a strong and active community around PostgreSQL, offering extensive documentation, forums, and collaborative support, which aids troubleshooting and development.
  • Multiple Indexing Techniques
    PostgreSQL offers a variety of indexing techniques such as B-tree, GIN, GiST, and BRIN, allowing for optimized query performance on various data types.
  • Cross-Platform Availability
    PostgreSQL runs on all major operating systems (Windows, MacOS, Linux, Unix), giving flexibility in deployment and development environments.

Possible disadvantages

  • Complex Configuration
    Setting up and configuring PostgreSQL can be complex and time-consuming, especially for beginners, requiring a good understanding of its parameters and best practices.
  • Heavy Resource Consumption
    PostgreSQL can be resource-intensive, consuming significant CPU and memory compared to other database systems, which may affect performance on lower-end hardware.
  • Backup and Restore Process
    The backup and restore process in PostgreSQL is not as straightforward as in some other database systems, requiring more manual intervention and understanding of tools like pg_dump and pg_restore.
  • Replication Complexity
    While PostgreSQL supports replication, setting it up can be more complex than some other databases. Advanced configurations like multi-master replication can be particularly challenging.
  • Steeper Learning Curve
    Due to its advanced features and extensive capabilities, PostgreSQL can have a steeper learning curve, making it harder for new users to get started compared to simpler database systems.
  • Less Third-Party Tool Support
    PostgreSQL has less support from third-party tools compared to more widely adopted databases like MySQL, which can limit options for auxiliary functions like administration, monitoring, and development.
  • High Performance
    Milvus is designed to manage and process large-scale vector data extremely fast, making it suitable for handling real-time processing of massive datasets.
  • Scalability
    Milvus supports horizontal scaling, ensuring that as the data grows, the system can scale out by adding more nodes to maintain performance.
  • Flexible Deployment
    Milvus can be deployed on-premises, on cloud services, or in hybrid environments, providing flexibility for different infrastructure needs.
  • Community and Support
    As an open-source project, Milvus has a strong community and support network, including comprehensive documentation and active community forums.
  • Rich Ecosystem
    Milvus integrates well with various machine learning and data processing tools, such as TensorFlow, PyTorch, and other AI frameworks, facilitating seamless workflows.
  • Built-in Indexing
    Milvus provides built-in indexing capabilities like IVF, HNSW, and ANNOY, which enhance the speed and efficiency of similarity searches on vector data.

Possible disadvantages

  • Steep Learning Curve
    The complexity of vector databases and the need for understanding high-dimensional indexing techniques may pose a challenging learning curve for new users.
  • Resource Intensive
    Milvus can be resource-intensive in terms of CPU and memory, especially for large-scale deployments, which may lead to higher operational costs.
  • Evolving Project
    As a relatively new project, Milvus is rapidly evolving, and users might encounter changing APIs or features that could disrupt ongoing projects.
  • Dependency Management
    Deploying Milvus with its dependencies (such as certain hardware requirements for optimal performance) can be complex, necessitating careful planning and management.
  • Limited Use Cases
    Given its specialization in vector similarity searches, Milvus might not be the best choice for applications needing comprehensive relational database capabilities.

Analysis

An editorial look at what each product does well and who it suits.

PostgreSQL
Milvus

Overall verdict

  • Yes, PostgreSQL is considered a high-quality and reliable database management system, suitable for a wide range of applications, from small-scale personal projects to large enterprise systems.

Why this product is good

  • PostgreSQL is known for its strong support of SQL standards and excellent documentation, making it reliable for complex database requirements.
  • It provides advanced features such as multi-version concurrency control (MVCC), point-in-time recovery, and support for advanced indexing techniques.
  • PostgreSQL offers robust performance optimization options, powerful extensions, and a highly customizable platform.
  • It has a strong open-source community, ensuring ongoing improvements and support.
  • PostgreSQL is compatible with popular development frameworks and languages, enhancing its versatility.

Recommended for

  • Organizations seeking a scalable and stable database solution with strong compliance with SQL standards.
  • Developers who need advanced features like custom data types and indexing capabilities.
  • Projects requiring robust transactional integrity and data consistency.
  • Businesses looking for a cost-effective open-source database solution with active community support.

Overall verdict

  • Milvus is generally regarded as a good option, especially for businesses and developers working in the field of AI and data science. Its open-source nature allows for flexibility and community support, and it is backed by a solid architecture designed for scalability and efficiency.

Why this product is good

  • Milvus is considered a strong choice for handling large-scale vector data due to its high-performance capabilities and ability to manage similarity search effectively. It is particularly well-suited for applications involving AI, machine learning, and deep learning where vector operations are common.

Recommended for

    Milvus is ideal for data scientists, AI researchers, and engineers who require efficient and scalable vector search solutions. It is also recommended for companies and projects dealing with recommendation systems, image and video search, natural language processing, and more.

Videos

Walkthroughs and reviews on video.

PostgreSQL 3 videos + Add
Milvus 2 videos + Add

Comparison of PostgreSQL and MongoDB

More videos

  • - PostgreSQL Review
  • - MySQL vs PostgreSQL - Why you shouldn't use MySQL

End to End Tutorial on Milvus Lite

More videos

  • - An Introduction To the Milvus Open Source Vector Database

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
PostgreSQL
Milvus
91% 91%
Databases
9% 9%
0% 0%
Search Engine
100% 100%
100% 100%
Relational Databases
0% 0%
0% 0%
Vector Databases
100% 100%

User comments

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Reviews and articles

External articles and on-site reviews we used to compare the two products.

PostgreSQL no reviews yet
Milvus no reviews yet

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We have no reviews of Milvus yet. Be the first one to post

Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

PostgreSQL 19 mentions
Milvus 40 mentions

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Alternatives to PostgreSQL and Milvus

When comparing PostgreSQL and Milvus, you can also consider the following products.

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