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Managed Databases

PostgreSQL: Object Relational Database​

PostgreSQL is a powerful open-source object-relational database management system that has evolved to support advanced data types and querying capabilities, including object-oriented data modeling. An object-relational database is a type of database designed to efficiently store and manage complex data relationships, typically used in enterprise applications and data warehousing.

Key Features​

PostgreSQL's object-relational database capabilities offer the following key features:

  1. Object-Oriented Data Types: PostgreSQL supports a range of object-oriented data types, including arrays, composite types, and user-defined types, allowing developers to model complex data relationships.
  2. Inheritance and Polymorphism: PostgreSQL's object-relational model supports inheritance and polymorphism, enabling developers to define hierarchical relationships between tables and objects.
  3. Querying and Indexing: PostgreSQL provides advanced querying and indexing capabilities, including support for SQL and procedural languages, to enable efficient data retrieval and manipulation.
  4. Extensions and Integration: PostgreSQL integrates seamlessly with a range of programming languages and frameworks, including Java, Python, and Ruby, and supports extensions for data analytics, machine learning, and more.

Benefits​

By leveraging PostgreSQL's object-relational database capabilities, developers can:

  1. Improve Data Modeling: Achieve more accurate and flexible data modeling, using object-oriented concepts and complex data types to represent real-world entities and relationships.
  2. Simplify Data Management: Simplify data management and administration, using a unified platform for data storage, querying, and analysis, with support for advanced data types and relationships.
  3. Enhance Scalability and Performance: Build scalable and high-performance data systems, with PostgreSQL's robust and reliable architecture, to support large and complex datasets.

Overall, PostgreSQL's object-relational database capabilities provide a powerful foundation for building innovative applications that rely on complex data relationships and object-oriented data modeling, while simplifying data management and improving scalability and performance.

Available PostgreSQL configurations​

ConfigurationSpecifications
PostgreSQL Node: 2 CPU8 GB RAM | 200 GB Data Disk
PostgreSQL Node: 4 CPU16 GB RAM | 500 GB Data Disk
PostgreSQL Node: 8 CPU32 GB RAM | 1.000 GB Data Disk
PostgreSQL Node: 16 CPU64 GB RAM | 2.000 GB Data Disk
PostgreSQL Node: 32 CPU128 GB RAM | 4.000 GB Data Disk
PostgreSQL Node: 64 CPU256 GB RAM | 12.000 GB Data Disk

Qdrant: Ultra-Fast Vector and Similarity Search Database​

Qdrant is a cutting-edge, open-source database designed for ultra-fast vector and similarity search, enabling developers to build scalable and efficient applications that rely on complex data relationships and vector-based queries. A vector and similarity search database is a type of database optimized for storing and querying large datasets of dense vectors, typically used in machine learning, artificial intelligence, and data science applications.

Key Features​

Qdrant's ultra-fast vector and similarity search database capabilities offer the following key features:

  1. Vector Data Type: Qdrant supports a native vector data type, allowing developers to store and query vectors of varying dimensions, with optimized storage and indexing for fast query performance.
  2. Approximate Nearest Neighbors (ANN) Search: Qdrant provides highly optimized ANN search algorithms, enabling fast and accurate similarity search, with support for various indexing techniques and distance metrics.
  3. Filtering and Indexing: Qdrant offers advanced filtering and indexing capabilities, including support for bitmap indexes and hierarchical indexing, to enable efficient querying and data retrieval.
  4. Scalability and High Performance: Qdrant is designed for scalability and high performance, with support for distributed architecture, parallel querying, and optimized data storage, to handle large and complex datasets.

Benefits​

By leveraging Qdrant's ultra-fast vector and similarity search database capabilities, developers can:

  1. Improve Query Performance: Achieve lightning-fast query performance, with optimized indexing and search algorithms, to enable real-time applications and services.
  2. Simplify Vector Data Management: Simplify vector data management, using a unified platform for data storage, querying, and analysis, with support for advanced vector data types and operations.
  3. Enhance Machine Learning and AI Workflows: Streamline machine learning and AI workflows, with Qdrant's optimized support for vector-based queries and similarity search, to enable faster and more accurate model development and deployment.

Overall, Qdrant's ultra-fast vector and similarity search database capabilities provide a powerful foundation for building innovative applications that rely on complex vector data relationships and similarity search, while simplifying data management and improving query performance.

Available Qdrant configurations​

ConfigurationSpecifications
Qdrant Node: 2 CPU8 GB RAM | 200 GB Data Disk
Qdrant Node: 4 CPU16 GB RAM | 500 GB Data Disk
Qdrant Node: 8 CPU32 GB RAM | 1.000 GB Data Disk
Qdrant Node: 16 CPU64 GB RAM | 2.000 GB Data Disk
Qdrant Node: 32 CPU128 GB RAM | 4.000 GB Data Disk
Qdrant Node: 64 CPU256 GB RAM | 12.000 GB Data Disk

Valkey: In-Memory Caching Database​

Valkey is a high-performance, open-source in-memory caching database designed to accelerate data access and reduce latency, enabling developers to build scalable and responsive applications. An in-memory caching database is a type of database that stores data in RAM, providing fast data access and retrieval, typically used in real-time web applications, gaming, and financial services.

Key Features​

Valkey's in-memory caching database capabilities offer the following key features:

  1. In-Memory Data Storage: Valkey stores data in RAM, providing fast data access and retrieval, with support for various data structures and caching strategies.
  2. High-Performance Caching: Valkey offers advanced caching capabilities, including support for caching hierarchies, cache expiration, and cache invalidation, to optimize data freshness and reduce latency.
  3. Distributed Architecture: Valkey supports a distributed architecture, enabling developers to scale their caching layer horizontally, with support for clustering, replication, and load balancing.
  4. Integration with Existing Databases: Valkey integrates seamlessly with existing databases, including relational and NoSQL databases, providing a caching layer that accelerates data access and reduces the load on underlying databases.

Benefits​

By leveraging Valkey's in-memory caching database capabilities, developers can:

  1. Improve Application Performance: Achieve significant improvements in application performance, with fast data access and retrieval, to enable responsive and interactive user experiences.
  2. Reduce Latency: Reduce latency and improve data freshness, with optimized caching strategies and advanced cache management, to enable real-time data processing and analytics.
  3. Increase Scalability: Increase scalability and reliability, with Valkey's distributed architecture and support for clustering and replication, to handle large and unpredictable workloads.

Overall, Valkey's in-memory caching database capabilities provide a powerful foundation for building innovative applications that require fast data access and low latency, while improving application performance, reducing latency, and increasing scalability.

Available Valkey configurations​

ConfigurationSpecifications
Valkey Node: 2 CPU8 GB RAM | 120 GB Database Disk
Valkey Node: 4 CPU16 GB RAM | 240 GB Database Disk
Valkey Node: 8 CPU32 GB RAM | 480 GB Database Disk
Valkey Node: 16 CPU64 GB RAM | 960 GB Database Disk

MongoDB (FerretDB): MongoDB-Compatible Document Database​

FerretDB is an open-source MongoDB-compatible database that brings the familiar MongoDB wire protocol and query language to a PostgreSQL backend. It enables teams already using MongoDB to migrate to a fully open-source stack without changing their application code, while benefiting from PostgreSQL's reliability, ACID compliance, and mature operational tooling.

Key Features​

FerretDB's MongoDB-compatible document database capabilities offer the following key features:

  1. MongoDB Wire Protocol Compatibility: FerretDB speaks the MongoDB wire protocol natively, allowing existing MongoDB drivers, tools, and applications to connect without modification.
  2. PostgreSQL Backend: FerretDB stores data in PostgreSQL, inheriting its robust transactional guarantees, backup tooling, and operational maturity, while exposing a document-oriented interface.
  3. Open-Source Stack: FerretDB is fully open-source with no proprietary licensing concerns, giving organizations full control and auditability over their database infrastructure.
  4. Familiar Query Language: Developers can use standard MongoDB query syntax, aggregation pipelines, and CRUD operations, reducing the learning curve when migrating from MongoDB.

Benefits​

By leveraging FerretDB's MongoDB-compatible document database capabilities, developers can:

  1. Migrate Without Code Changes: Move from MongoDB to an open-source alternative without rewriting application logic or changing database drivers, minimizing migration risk and effort.
  2. Leverage PostgreSQL Operations: Benefit from PostgreSQL's proven backup, recovery, encryption, and monitoring tooling, applied transparently to MongoDB-style workloads.
  3. Reduce Licensing Risk: Eliminate dependency on MongoDB's Server Side Public License (SSPL) by running a fully open-source stack, reducing legal and compliance exposure.

Overall, FerretDB provides a practical migration path for teams invested in MongoDB tooling who want the operational benefits and open-source guarantees of a PostgreSQL-backed infrastructure.

Available MongoDB (FerretDB) configurations​

ConfigurationSpecifications
MongoDB Node: 2 CPU8 GB RAM | 200 GB Data Disk
MongoDB Node: 4 CPU16 GB RAM | 500 GB Data Disk
MongoDB Node: 8 CPU32 GB RAM | 1.000 GB Data Disk
MongoDB Node: 16 CPU64 GB RAM | 2.000 GB Data Disk
MongoDB Node: 32 CPU128 GB RAM | 4.000 GB Data Disk
MongoDB Node: 64 CPU256 GB RAM | 12.000 GB Data Disk
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