PostgresML
vs
EDB Postgres AI
PostgresML
PostgresML is a comprehensive platform designed to revolutionize the development of AI-powered applications. It eliminates the need for complex, multi-service architectures by integrating machine learning and artificial intelligence functionalities directly within your PostgreSQL database.
This approach offers several advantages, including significantly faster vector operations, reduced latency, and a more streamlined development process. By colocating data and compute, PostgresML enhances data privacy and security. It supports a wide array of open-source models and provides tools for training, tuning, and deploying machine learning models, including LLMs.
EDB Postgres AI
EDB Postgres AI integrates artificial intelligence capabilities directly into the robust EDB Postgres platform. It is designed to address common enterprise challenges by incorporating native AI vector processing, allowing for advanced AI-driven applications and insights directly from the database.
The platform provides an analytics lakehouse and a unified interface for observability and hybrid data management. This enables organizations to manage transactional, analytical, and AI workloads efficiently across various deployment environments, including cloud and on-premises setups. It supports use cases like sovereign AI, legacy application modernization, and secure open-source software development.
PostgresML
Pricing
EDB Postgres AI
Pricing
PostgresML
Features
- Vector Operations: 10x faster vector operations with fast KNN and ANN search.
- Embedding Generation: Built-in data preprocessors for splitting and chunking text to create vector embeddings.
- Model Training & Deployment: Train, tune, and deploy models for regression, classification, and clustering, including fine-tuning LLMs.
- LLM Support: Utilize open-source models (Mistral, Llama, etc.) for various NLP tasks.
- Data Colocation: Embed, serve, and store data in one process for enhanced data privacy and security.
- Multiple Deployment Options: Offers flexible deployment options, including serverless and dedicated plans.
- Comprehensive Platform: Perform various machine learning & AI tasks using SQL or SDKs in Javascript & Python.
EDB Postgres AI
Features
- Native AI Vector Processing: Integrated capabilities for AI applications directly within the database.
- Analytics Lakehouse Integration: Combines data warehousing and data lake capabilities for comprehensive analytics.
- Unified Observability Platform: Provides tools for monitoring and managing database performance and health.
- Hybrid Data Management: Supports data management across cloud and on-premises environments.
- AI Accelerator: Enhances performance for AI-related tasks.
- Enterprise-Grade Postgres Extensions: Builds upon EDB Postgres Advanced Server and other EDB tools.
PostgresML
Use cases
- Building RAG (Retrieval-Augmented Generation) applications
- Creating AI-powered chatbots
- Implementing fast and efficient search functionalities
- Developing applications requiring real-time, fact-based outputs
- Performing text generation, summarization, and translation
- Training and deploying custom machine learning models
EDB Postgres AI
Use cases
- Sovereign AI
- Legacy App Modernization
- Hybrid DBaaS
- Omni-Data Platform
- Secure Open Source Software
- Geo-Distributed Applications
- Microservices with Kubernetes
- Virtual Expert
PostgresML
Uptime Monitor
Average Uptime
100%
Average Response Time
807.97 ms
Last 30 Days
EDB Postgres AI
Uptime Monitor
Average Uptime
99.85%
Average Response Time
140.78 ms
Last 30 Days
PostgresML
EDB Postgres AI
