The same kind of agent that builds your app installs your backend — Amba provisions over hosted MCP, no browser signup required. AI companion apps are LLM front-ends with a memory problem and a billing problem: every conversation needs storing and recalling, every call costs money, and the product is usually subscription-funded. Amba’s AI gateway, vector collections, entitlements, and moderation cover exactly that. What is an agent-native backend?
What a ai companion & chat app backend actually needs.
How does Amba handle memory and LLM costs?
AI companion & chat apps backend — FAQ
What backend does an AI companion app need?
An AI companion app needs per-user conversation and memory storage, an LLM gateway with cost controls, subscriptions, safety moderation, and re-engagement push. Amba provides AI prompts with budgets, vector collections for memory, entitlements, moderation, and push as one backend.
Does Amba control LLM costs?
Yes. Every AI response carries a per-call cost_usd, and you can cap a prompt’s spend with a per-period USD budget that denies further calls once exhausted — plus a project-wide spend ceiling. You bring your own model key, so usage is on your bill with no markup.
Can Amba store and recall conversation memory?
Yes. Amba’s typed collections include vector columns and a findNearest query for semantic recall, per-user isolated by default — a built-in memory store with no separate vector vendor.