# Honest question: at this price and performance, would you use this memory API?

> Source: <https://dev.to/woochan/honest-question-at-this-price-and-performance-would-you-use-this-memory-api-2bll>
> Published: 2026-10-16 07:31:38+00:00

I work at Wontopos, so I'm biased. About three months ago we launched a long-term memory API for AI agents, and honestly almost nobody has looked at it. I'm not asking you to sign up. I'd like a blunt answer: at this price and performance, would you use it?

You store a user's memories once. For each query, `recall` returns only the relevant ones, and you pass them into your model's prompt. Memory lives per user, so each end-user or agent gets an isolated space.

The core calls are `store`, `recall`, `search`, `supersede` (replace an outdated memory) and `forget`. SDKs for Python, TypeScript and Rust, plain HTTP works too, and there is an MCP server (beta) for Claude Code and Cursor.

|  | Input / 1M tokens | Output / 1M tokens | 
|---|---|---|
| Tablet | $2 | $3 | 
| Scroll | $4 | $8 | 

Each request adds a flat $0.0001. No subscription, no storage rent, no memory caps. You add a card and top up at least $5, and there is a hard spend cap. If you bring your own LLM keys, the provider bills those.

The harness and per-question records behind the BEAM-1M number are public, and the Tablet 2 paper reports three findings that run against us.

You don't need to sign up, you can just read the information. Knowing what I'm talking about should let you give a more accurate answer than my summary, so please take a look if you want the details: [wontopos.com](https://wontopos.com) and [the research page](https://wontopos.com/research).

You can answer here in the comments. Even better, there is a short survey on the site: click the Survey button in the top menu of the main page. No sign-up is needed for that either.

*I wrote the first draft with an AI assistant and edited it myself.*