> For the complete documentation index, see [llms.txt](https://docs.activeloop.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.activeloop.ai/models-and-pricing.md).

# Models & Pricing

Managed cloud cost is based on consumption for compute and tiered for storage.

### Models

Choose the following models for querying the data.

<table><thead><tr><th>Model name</th><th width="97.26953125">Latency</th><th>Description</th></tr></thead><tbody><tr><td>activeloop-l0-retrieval</td><td>1-5s</td><td>Hybrid visual search. Just returns top_k elements.  </td></tr><tr><td>activeloop-l0</td><td>~40s</td><td>Reasoning iterations with evidence collection and verification including inline citations.</td></tr></tbody></table>

### Pricing

1M input tokens is roughly 1,000 pages of a document. Output tokens include thinking and search results.&#x20;

|               | Input             | Output            | Storage           |
| ------------- | ----------------- | ----------------- | ----------------- |
| activeloop-l0 | $1.00 / 1M tokens | $15.00 / 1M token | $99 / 100GB month |

## **Scenarios**

1. **Financial**: 2,100 pages of documents including quarterly reports, market news video transcripts, and earnings call transcripts.&#x20;
   1. Approximate total cost is **$122/month** | Input 2M tokens → $2, Output 8M tokens → $120, Free Storage
2. **Healthcare**: 8,000 pages of research documents including research papers, clinical trial documents, lab notes, and supplementary data.&#x20;
   1. Approximate total cost is **$707/month** | Input 8M tokens → $8, Output 40M tokens → $600, Storage Tier 100GB -> $99
3. **Insurance**: 120,000 pages of insurance documents including claim forms, incident reports, damage photos/videos, repair estimates, policyholder data, and investigation reports.&#x20;
   1. Approximate total cost is **$2,568/month** | Input 120M tokens → $120, Output 150M tokens → $2,250, Storage Tier 200GB → $198

## **Need Enterprise deployment?**

Activeloop is trusted by F500 including likes of [Bayer](https://www.activeloop.ai/usecase/bayer/), [Flagship Pioneering](https://www.activeloop.ai/usecase/flagshippioneering/), and [Matterport](https://www.activeloop.ai/usecase/matterport/).

* Deploy on your cloud, ensuring data never leaves your infrastructure.
* Integrate custom storage and LLMs.
* Enjoy SOC2 compliance, fine-grained access control, and SSO.

[Book a call](https://www.activeloop.ai/contact/) to discuss enterprise deployment.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.activeloop.ai/models-and-pricing.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
