A Postgres backend with auth, storage, and realtime built in.
A fast way to stand up the backend for an AI product.
Check if this matches what you need right now.
Look at price and setup together.
If you want to move quickly, this is a good first tool to try.
Best for teams that want one backend stack instead of stitching services together.
Supabase combines Postgres with the core pieces many product teams need right away. It works well for auth, saved content, relational data, and lightweight realtime features without a lot of backend setup.
A good fit for writing, analysis, and long-context workflows.
Pinecone is the managed vector database teams often choose for production RAG systems.
A fast starting point for teams building AI features.
Use it when front-end speed and developer experience matter.
A practical setup guide for connecting your app to a model API without creating brittle code.
Cost problems usually start quietly. A few simple rules make them much easier to manage.
A good starter stack is small, easy to explain, and tied to a real weekly task instead of internet hype.
Most problems come from rushing: too many tools, not enough review, and no clear rule for what AI should or should not do.
A plain-language guide to telling an AI agent apart from a normal chatbot, and deciding whether you need one now or later.
If you are still learning what AI is useful for, stay with finished apps. API choice only becomes relevant once AI has to fit inside your own system or repeat at scale.