# Backend API

## 2025 Cloud Services Comparison & Usage

| Source / Server                                                                                 | Usage Relative to AI Agent Stack                                                                                                                                                                                                            | Usage Relative to All Other                                      |
| ----------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------- |
| Ollama                                                                                          | <ul><li>Main Server to communicate with large and compressed models.</li></ul>                                                                                                                                                              | <ul><li>AWS Sagemaker AI</li></ul>                               |
| Google Stack [BigQuery](https://github.com/googleapis/python-bigquery-dataframes) and Vertex AI | This serves as Gaby’s database and memory **Gatekeeper**. Its main responsibilities include assisting with service workflows, storing analytics from his observations, and enabling fast knowledge retrieval, such as entity relationships. | <ul><li>AWS Sagemaker AI</li></ul>                               |
| Google Vertex AI                                                                                | <ul><li>Used to serve Vision/LLMS.</li></ul>                                                                                                                                                                                                | <ul><li>AWS Bedrock AgentCore</li><li>Lightning Studio</li></ul> |
| Lightning Studio                                                                                | <ul><li>Agent's Coding Sandbox  / Playground for programming and data cleaning </li><li>ML/AI Tunings </li></ul>                                                                                                                            | <ul><li>Google Vertex AI</li><li>AWS Bedrock AgentCore</li></ul> |
| Hugging Face Space                                                                              | <ul><li>Fallback for Lightning Studio hosting Agent's Sandbox </li><li>Datasets Collections behind prompt chains</li></ul>                                                                                                                  | <ul><li>Google Firebase Studio</li><li></li></ul>                |
| Google: Firestore Database                                                                      | Used this for my Tarot Reading Agent so this is my go-to at the moment but BigQuery AI is a more scalable solution (i.e. will not have to worry that much about database setups / runs)                                                     |                                                                  |
| Google API keys to workspace e.g. Google Sheets                                                 | These keys are used for users to view their cleaned dataset in tabular form. This is also handy in connecting IP subnets to users' database e.g. MongoDB.                                                                                   |                                                                  |


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# Agent Instructions: Querying This Documentation

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

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://whoamimi.gitbook.io/blog/projects/readme-1/backend-api.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
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.