Announcing our lead investment in PostgresML’s $4.7M seed round to bring machine learning to everyone’s Postgres database and make it easier to build data-driven applications.
Natalie Vais and Sarah Catanzaro
Today more than ever, everyone wants to build AI-powered applications. However, in conversations with developers and data practitioners, we’ve consistently heard the same thing about AI infrastructure — it’s hard. Setting up machine learning workflows (which may include data preparation, training, fine-tuning, and inference) creates a ton of overhead for teams. Many companies address this by hiring experienced ML engineers to build custom infrastructure like feature stores, model stores, and inference layers. Even then, companies are caught with the maintenance burden of these solutions which become more complex (and bespoke) over time. Here are some of the biggest challenges we heard from developers when it comes to managing AI infrastructure:
Many companies we spoke to don’t consider themselves to have “fancy ML workloads”. Instead, they frequently have a single SQL database sitting around (usually Postgres). For these folks, machine learning can be an aspirational roadmap item. While the recent surge of LLM APIs makes AI more accessible to developers without classical ML training, most LLM APIs do not fit nicely into existing developer workflows and technical stacks, especially for online training.
What if you could bring AI tasks directly to the database and make it dead simple to deploy AI applications from a single platform?
Last year, we met a team of experienced ML and infrastructure engineers solving exactly this problem. Montana Low and Lev Kokotov are the creators and founders of PostgresML, an open-source extension for Postgres written in Rust, that allows developers to easily train and deploy ML models using SQL. The pair met during their time at Instacart, where they worked on the ML and platform teams respectively during one of the highest growth periods at Instacart ever.
PostgresML is at the forefront of a trend wherein widely adopted developer tools and databases are being adapted and extended to an AI-first world. PostgresML allows developers to prototype and deploy AI applications quickly with their end-to-end platform on Postgres by bringing common AI tasks directly into the database.
Some of the most exciting features of their current platform are:
By allowing companies to run ML models directly on a Postgres database (and bringing the “code to the data”), PostgresML removes the need for a separate feature store and reduces the data management overhead associated with training and deploying ML models. This is huge, especially for those smaller companies who view machine learning as a walled garden.
During our due diligence process, we spoke to numerous developers who expressed their excitement about the potential of PostgresML to address their pain points in ML development. This feedback resonated with our own experience, as we've seen firsthand how the complexity of ML infrastructure can hinder the adoption of AI-driven applications. Here are the things that excite us most about PostgresML:
Today, we’re excited to announce our lead investment in PostgresML’s $4.7M seed round to make machine learning more accessible in the era of AI-driven applications. Welcome to the Amplify family!