> ## Documentation Index
> Fetch the complete documentation index at: https://docs.memobase.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Performance and Cost

## Overview

Memobase is designed for high performance and cost-efficiency.

* **Query Performance**: Queries are extremely fast because Memobase returns a pre-compiled user profile, eliminating the need for on-the-fly analysis.
* **Controllable Costs**: You can manage costs by controlling the size of user profiles. This is done by configuring the number of profile slots and the maximum token size for each.
  * Learn to design profile slots [here](/features/profile/profile_config).
  * Learn to control token limits [here](/references/cloud_config).
* **Insertion Efficiency**: New data is added to a buffer and processed in batches. This approach amortizes the cost of AI analysis, making insertions fast and inexpensive.
  * Learn to configure the buffer [here](/references/cloud_config).

## Comparison vs. Other Solutions

#### Memobase vs. [mem0](https://github.com/mem0ai/mem0)

* **Cost**: Memobase is approximately 5x more cost-effective.
* **Performance**: Memobase is roughly 5x faster.
* **Memory Quality**: mem0 provides gist-based memories, while Memobase delivers structured and organized profiles for more predictable recall.

The full technical report is available [here](https://github.com/memodb-io/memobase/tree/docs/docs/experiments/900-chats).