Custodian Labs vs Wattfare

from custodian_labs import Custodian model = Custodian( model="gpt-4o", system_prompt="You are a helpful assistant.

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

About Custodian Labs

from custodian_labs import Custodian model = Custodian( model="gpt-4o", system_prompt="You are a helpful assistant. The proprietary Guardian Layer is the guardrail that detects PII and puts you in control of how it's handled before it reaches any model. Give your agent long-term memory and document retrieval without configuring embeddings or vector DBs. Your agent logic stays exactly as it is; only the model underneath changes. No database to provision, no vector store to connect, no hosting to manage.

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.