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

# Prerequisites

The main supported installation method is our cloud-native Zerve Helm chart. To install the application, you'll need:

* [Kubernetes](https://kubernetes.io/) (version 1.29+)
* [kubectl](https://kubernetes.io/docs/tasks/tools/#kubectl)
* [Helm](https://helm.sh/docs/intro/install/)

## Zerve's Platform Dependencies

At a minimum, Zerve requires:

* [PostgreSQL](https://www.postgresql.org/)
* [Redis](https://redis.io/)
* [HashiCorp Vault](https://www.hashicorp.com/en/products/vault)
* Object storage

Our Helm chart can install all of the platform dependencies for you for testing and experimentation. However, for a production-grade install we recommend you provide an external Postgres, Redis and Vault.

For object storage, supported options are: S3, GCS, Azure Blob, and VAST.

### Authentication

Zerve requires an [OIDC](https://openid.net/developers/how-connect-works/)-compatible Identity Provider for authentication (Auth0, Okta, Microsoft Entra ID, AWS Cognito, Keycloak, Logto, etc.).

### Notebook Execution and Environment Builds

Zerve runs user code in its Notebooks on the compute infrastructure of your choice. We support a variety of compute backends, including serverless computing services from popular cloud vendors (e.g. AWS Lambda), or k8s-backed execution.

Users may also choose to build custom Environments in Zerve. The builds can be backed by a fully managed cloud service like AWS CodeBuild, or run in-cluster with BuildKit.

Depending on your choice of technology for builds and execution, the platform will require additional dependencies. Refer to [Code Execution](/self-managed/configuration/code-execution.md) and [Builds](/self-managed/configuration/builds.md) pages for more information on supported options.


---

# 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.zerve.ai/self-managed/install/prerequisites.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.
