PostgresML is an AI tool that allows users to perform various machine learning tasks, including indexing, filtering, and ranking vectors; creating embeddings; and generating real-time, fact-based outputs. It offers a comprehensive platform with multiple deployment options and supports various AI and machine learning tasks using SQL or SDKs in JavaScript and Python. The tool is built on PostgreSQL and is designed for efficient GPU memory management. PostgresML also provides various open-source tools like Korvus, PGML, and PgCat.
• built-in data preprocessors for splitting and chunking
• choose from state-of-the-art models
• index embeddings with hnsw or ivfflat
• perform fast knn and ann search
• 10x faster vector operations
• generate real-time, fact-based outputs
• create embeddings
• index, filter & rank vectors
On PostgresML you can build and scale Postgres without having to manage servers or GPUs. Your database will respond to your application’s demand automatically, and scale up or down as needed. Your charges will be based purely on your usage, and measured down to the millisecond.
PostgresML does not charge per token. We charge by the amount of time a query runs. Queries that generate or process more tokens will often run longer, but queries that use smaller models will run more quickly. You’re only charged for the resources you use.
PostgresML charges $0.25 per gigabyte per month for storage. This includes fault tolerant RAID configurations for high availability as well as backups for disaster recovery.
Our approach to GPU memory management is inherently more efficient because at PostgresML, we move full AI capability to the database rather than moving the data to the models.
PostgresML estimates costs based on typical workloads and real world benchmarks. Workload prediction is difficult which can make future cost estimation even harder. Please contact our team if you would like help estimating the size of your workload and the associated costs. We’re happy to help if you have any questions.
Anything you want with PostgresML. We’ll send you an email when your free credits expire as a reminder that you may start incurring charges in the future.
By default, you will be billed monthly based on your usage. You will receive an invoice with total charges three days before your elected payment method is automatically billed. If you incur significantly increased utilization before your normal billing cycle, we will notify you with an off cycle invoice to help you control costs and maintain service.
Serverless plans have access to our community Discord. Dedicated plans offer a private Slack or MS teams channel for direct communication with our team. PostgresML provides custom SLAs for enterprise plans. Contact us for details.
• Pay-per-use
• Burst GPU capacity
• Access curated models
• Support on Discord
• Committed use discounts
• Dedicated hardware
• Use any model on HuggingFace
• Deploy on major cloud providers in any region
• Dedicated support on private Slack or MS Teams
• Pay as you go or committed use pricing
• VPC deployments on major cloud providers in any region
• Multiple GPUs
• Custom SLAs
• Premium support and onboarding
• Dedicated support on Slack or MS Teams
• Priority feature requests
Full Stack Engineer
PostgresML is a cloud-based AI application database built on PostgreSQL that enables efficient machine learning tasks using GPUs. It offers serverless and dedicated plans with various features.
Benefits:
Remote-first
Relocate if you want
Platinum-tier insurance
Stipends
Unlimited PTO
Experience Requirements:
5+ years of professional programming experience including vanilla HTML/JS/CSS in a browser
Other Requirements:
Experience creating and maintaining front end infrastructure and tooling such as build pipelines
Keen attention to detail and able to work with a designer to iterate toward responsive and scalable designs
Interest in machine learning, statistics and algorithms for dealing with large datasets
Responsibilities:
Build infrastructure as a service with a web app implemented in Rust
Work with a small team to implement a fast, modern and efficient web front end with client and server side components
Implement advanced visualizations using D3, WASM, SVG and CSS to provide insight into complex data sets and models
Build end-to-end ML applications with the support of a Data Scientist on the platform
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Machine Learning Engineer
PostgresML is a cloud-based AI application database built on PostgreSQL that enables efficient machine learning tasks using GPUs. It offers serverless and dedicated plans with various features.
Benefits:
Remote-first
Relocate if you want
Platinum-tier insurance
Stipends
Unlimited PTO
Experience Requirements:
5+ years of relevant experience in roles that require a strong statistical background day to day
Other Requirements:
Experience designing machine learning models (data analysis, feature engineering, algorithm selection), and working with them in a production environment, using a language like R, Python or C++
Understanding of SQL concepts for data retrieval, cleaning and feature engineering
Presentation and communication skills, diagrams as well as documents to explain deep concepts
Responsibilities:
Work with an engineering team to implement ML solutions at scale
Build an infrastructure platform across multiple cloud providers
Integrate the latest models and frameworks w/ vector and tabular database operations
Diagnose customer scale and performance issues
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Data Scientist
PostgresML is a cloud-based AI application database built on PostgreSQL that enables efficient machine learning tasks using GPUs. It offers serverless and dedicated plans with various features.
Benefits:
Remote-first
Relocate if you want
Platinum-tier insurance
Stipends
Unlimited PTO
Education Requirements:
Bachelor's degree or equivalent experience in a quantitative field (Statistics, Mathematics, Computer Science, Engineering, etc.)
Experience Requirements:
5+ years of relevant experience in roles that require a strong statistical background day to day
Other Requirements:
Experience designing machine learning models (data analysis, feature engineering, algorithm selection), and working with them in a production environment, using a language like R, Python or C++
Excellent understanding of SQL concepts for data retrieval, cleaning and feature engineering
Presentation and communication skills, diagrams as well as documents to explain deep concepts
Responsibilities:
Create documentation for classical Machine Learning use cases on a brand new platform
Educate early customers and help them formulate business problems as supervised learning problems
Design statistical visualizations that provide insight across multiple data sets
Guide UX to create interpretable model outputs, as well as avenues for model improvement
Implement concepts in SQL, Python and Rust rather than Powerpoint
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