Wattfare vs Wolbarg

Users connect, set a spending cap, and you call any model through one SDK, charged to them, not you.

Why most local multi-agent workflows don't need PostgreSQL for semantic memory, and when they actually do.

About Wattfare

Users connect, set a spending cap, and you call any model through one SDK, charged to them, not you. OpenAI-compatible Works with the Vercel AI SDK ~5-min integration. One button connects a user's inference budget to your app, metered, capped, and revocable. The problem AI costs are the one line item you can't predict. Every AI app makes the same uncomfortable bet: price high enough to survive your power users, ration usage so nobody hurts you, or quietly lose money on the heavy ones.

About Wolbarg

Why most local multi-agent workflows don't need PostgreSQL for semantic memory, and when they actually do. When people build an AI application, the database choice looks obvious: PostgreSQL, maybe Redis for caching, maybe pgvector for semantic search. This post is about that decision, and why SQLite is often the better fit for local multi-agent workflows. Local agents usually run on one machine, one process, maybe a handful of agents sharing context. Strip away the marketing and the list is short: low latency, simplicity (no migrations, users, or roles to wrestle with), local execution (offline works, no cloud bill), reliability (transactions, not half-written memories), and persistence (a file you can copy, back up, or delete).