I'm always cautious with a one-size-fits-all approach. If a team is working on a small project and SQLite works then great. You can use a SQLite database on som

I'm always cautious with a one-size-fits-all approach. If a team is working on a small project and SQLite works then great. You can use a SQLite database on something like a $4/month DigitalOcean droplet. Can't say the same for Postgres. > AI is a bubble Many say this but Generative AI and LLMs have gotten bunched up with everything else. There is a clear need for vectors and multimodal search. There is no core SQL statement to find concepts within an image for example. Machine learning models support that with arrays of numbers (i.e. vectors). pgvector adds vector storage and similarity search for Postgres. There was a recent post about storing vectors in SQLite (https://github.com/asg017/sqlite-vec https://github.com/asg017/sqlite-vec). > Even if your business is another AI grift, you probably only need to import openai. There's much more than this. There are frameworks such as LangChain, LlamaIndex and txtai (disclaimer I'm the primary author of https://github.com/neuml/txtai https://github.com/neuml/txtai) that handle generating embeddings locally or with APIs and storing them in databases such as Postgres.