Quick Start

Self-Hosting Documentation Access Granted

Before You Get Started

Welcome to LlamaCloud! Before you get started, please make sure you have the following prerequisites:

LlamaCloud License Key. To obtain a LlamaCloud License Key, please contact us at support@llamaindex.ai.
Kubernetes cluster >=1.28.0 and a working installation of kubectl.
- We are largely aligned with the versions supported in EKS, AKS, and GKE.
Helm v3.7.0+
- To install Helm, please refer to the official Helm Documentation.
OpenAI API Key or Azure OpenAI Credentials. Configuring OpenAI credentials is the easiest way to get started with your deployment.
- LlamaCloud tries to meet you at your organization’s needs and supports configuring more than OpenAI LLMs in including Anthropic, Bedrock, Vertex AI, and more.
- Please refer to the docs in the Configuration section of the sidebar to learn more about configuring other LLMs.
File Storage: LlamaCloud must leverage your cloud provider’s object storage to store files.
- Please follow the File Storage documentation to configure your deployment.
Authentication Settings:
- OIDC. OIDC is our recommended authentication mode for production deployments.
- Basic Auth (email/password): As of July 24th, 2025 (v0.5.0), we support both oidc and basic authentication methods. This is a simpler authentication mode more suitable for staging deployments.
- For more information, please refer to the Authentication Modes documentation.
Credentials to External Services (See below).

External Services

LLamaCloud requires the following external services to be available: Postgres, MongoDB, RabbitMQ and Redis.

Please follow the Database and Queues documentation to configure these services for your deployment.

Hardware Requirements

Linux Instances running x86 CPUs
- We currently only linux/amd64 images. arm64 is not supported at this moment.
Ubuntu >=22.04
>=12 vCPUs
>=80Gbi Memory

Warning #1: LlamaParse, LlamaIndex’s proprietary document parser, can be a very resource-intensive deployment to run, especially if you want to maximize performance.

Warning #2: The base CPU/memory requirements may increase if you are running containerized deployments of LlamaCloud dependencies. (More information in the following section)

Configure and Install Your Deployment

This section will walk you through the steps to configure a minimal LlamaCloud deployment.

Minimal `values.yaml` configuration

To get a minimal LlamaCloud deployment up and running, you can create a values.yaml file with the following content:

  license:
    key: <LLAMACLOUD-LICENSE-KEY>

  postgresql:
    host: "postgresql"
    port: "5432"
    database: "llamacloud"
    username: <POSTGRES-USERNAME>
    password: <POSTGRES-PASSWORD>

  mongodb:
    host: "mongodb"
    port: "27017"
    username: <MONGODB-USERNAME>
    password: <MONGODB-PASSWORD>

  rabbitmq:
    scheme: "amqp"
    host: "rabbitmq"
    port: "5672"
    username: <RABBITMQ-USERNAME>
    password: <RABBITMQ-PASSWORD>

  redis:
    scheme: "redis"
    host: "redis-master"
    port: "6379"
    db: 0

  config:
    llms:
      openAi:
        apiKey: <OPENAI-APIKEY>

    frontend:
      enabled: true
    parseOcr:
      gpu: true

    authentication:
      oidc:
        enabled: true
        discoveryUrl: "https://login.microsoftonline.com/<TENANT-ID>/v2.0/.well-known/openid-configuration"
        clientId: <CLIENT-ID>
        clientSecret: <CLIENT-SECRET>

  license:
    key: <LLAMACLOUD-LICENSE-KEY>

  postgresql:
    host: "postgresql"
    port: "5432"
    database: "llamacloud"
    username: <POSTGRES-USERNAME>
    password: <POSTGRES-PASSWORD>

  mongodb:
    host: "mongodb"
    port: "27017"
    username: <MONGODB-USERNAME>
    password: <MONGODB-PASSWORD>

  rabbitmq:
    scheme: "amqp"
    host: "rabbitmq"
    port: "5672"
    username: <RABBITMQ-USERNAME>
    password: <RABBITMQ-PASSWORD>

  redis:
    scheme: "redis"
    host: "redis-master"
    port: "6379"
    db: 0

  config:
    llms:
      azureOpenAi:
        secret: ""
        deployments: []

    frontend:
      enabled: true
    parseOcr:
      gpu: true

    authentication:
      oidc:
        enabled: true
        discoveryUrl: "https://login.microsoftonline.com/<TENANT-ID>/v2.0/.well-known/openid-configuration"
        clientId: <CLIENT-ID>
        clientSecret: <CLIENT-SECRET>

  license:
    key: <LLAMACLOUD-LICENSE-KEY>

  postgresql:
    host: "postgresql"
    port: "5432"
    database: "llamacloud"
    username: <POSTGRES-USERNAME>
    password: <POSTGRES-PASSWORD>

  mongodb:
    host: "mongodb"
    port: "27017"
    username: <MONGODB-USERNAME>
    password: <MONGODB-PASSWORD>

  rabbitmq:
    scheme: "amqp"
    host: "rabbitmq"
    port: "5672"
    username: <RABBITMQ-USERNAME>
    password: <RABBITMQ-PASSWORD>

  redis:
    scheme: "redis"
    host: "redis-master"
    port: "6379"
    db: 0

  config:
    llms:
      openAi:
        apiKey: <OPENAI-APIKEY>

    frontend:
      enabled: true
    parseOcr:
      gpu: true

    authentication:
      basicAuth:
        enabled: true
          validEmailDomain: "llamaindex.ai" # this is optional, but recommended for production deployments
          jwtSecret: <YOUR-JWT-SECRET>

  license:
    key: <LLAMACLOUD-LICENSE-KEY>

  postgresql:
    host: "postgresql"
    port: "5432"
    database: "llamacloud"
    username: <POSTGRES-USERNAME>
    password: <POSTGRES-PASSWORD>

  mongodb:
    host: "mongodb"
    port: "27017"
    username: <MONGODB-USERNAME>
    password: <MONGODB-PASSWORD>

  rabbitmq:
    scheme: "amqp"
    host: "rabbitmq"
    port: "5672"
    username: <RABBITMQ-USERNAME>
    password: <RABBITMQ-PASSWORD>

  redis:
    scheme: "redis"
    host: "redis-master"
    port: "6379"
    db: 0

  config:
    llms:
      azureOpenAi:
        secret: ""
        deployments: []

    frontend:
      enabled: true
    parseOcr:
      gpu: true

    authentication:
      basicAuth:
        enabled: true
        validEmailDomain: "llamaindex.ai" # this is optional, but recommended for production deployments
        jwtSecret: <YOUR-JWT-SECRET>

Install the Helm chart

# Add the Helm repository
helm repo add llamaindex https://run-llama.github.io/helm-charts

# Update your local Helm chart cache
helm repo update

# Create the llamacloud namespace
kubectl create ns llamacloud

# Install the Helm chart
helm install llamacloud llamaindex/llamacloud -f values.yaml --namespace llamacloud

If you want to install a specific version of the Helm chart, you can specify the version:

helm install llamacloud llamaindex/llamacloud --version x.y.z -f values.yaml --namespace llamacloud

Validate the installation

After installation, you will see the following output:

NNAME: llamacloud
LAST DEPLOYED: Tue Nov 18 10:12:03 2025
NAMESPACE: llamacloud
STATUS: deployed
REVISION: 1
TEST SUITE: None
NOTES:
Welcome to LlamaCloud!

View your deployment with the following:

  kubectl --namespace default get pods -n llamacloud

To view LlamaCloud UI in your browser:
  Run the following command:

  kubectl --namespace llamacloud port-forward svc/llamacloud-web 3000:80

If you list the pods with kubectl get pods -n llamacloud, you should see the following pods:

NAME                                        READY   STATUS      RESTARTS     AGE
llamacloud-64f468d5cf-sqjq6                 1/1     Running     0            2m56s
llamacloud-layout-6d97b84c58-rld8x          1/1     Running     0            2m56s
llamacloud-ocr-5cc459bdd-99xgt              1/1     Running     0            2m56s
llamacloud-operator-5d4c58b854-dwnjk        1/1     Running     0            2m56s
llamacloud-parse-7ffbc786b5-r98w2           1/1     Running     0            2m56s
llamacloud-telemetry-5fc9ff8c67-fv8xj       1/1     Running     0            2m56s
llamacloud-web-b88d95588-rprhc              1/1     Running     0            2m56s
llamacloud-worker-58b95ccc6f-vqmgx          1/1     Running     0            2m56s

Port forward the frontend service to access the LlamaCloud UI:

kubectl --namespace llamacloud port-forward svc/llamacloud-web 3000:80

Open your web browser and navigate to http://localhost:3000. You should see the LlamaCloud UI.

Next Steps

Choose your deployment approach based on your needs:

🌩️ Cloud-Specific Deployment Guides

Recommended for most users - Complete, opinionated guides for major cloud providers:

📋 Choose Your Cloud Provider →

Azure: AKS + Azure-native services with Microsoft Entra ID
AWS: EKS + AWS-native services (coming soon)
GCP: GKE + GCP-native services (coming soon)

These guides provide end-to-end instructions using cloud-native services and enterprise authentication.

⚙️ Custom Configuration Guides

For advanced users with specific requirements or non-standard setups:

Authentication Modes - Configure OIDC, basic auth, or custom authentication
File Storage - Set up S3, Azure Blob, GCS, or other storage
Database and Queues - Configure external databases and message queues
LLM Integrations - Set up OpenAI, Azure OpenAI, Bedrock, or other LLMs
Ingress Configuration - Load balancers, SSL, and networking
Autoscaling Configuration - HPA and KEDA-based scaling for services
Service Tuning - Performance and scaling configurations

Use these guides if you need custom integrations, have specific compliance requirements, or want to mix and match different services.

🚰 Data Sink Configuration

Configure at least one Data Sink to store the vector embeddings of your documents.

More Examples and Guides

there are many more configuration options available for each component. to see the full values.yaml specification, please refer to the values.yaml file in the helm chart repository.
To see how common scenarios are configured, please refer to the values.yaml examples directory in the Helm chart repository.
Similarly, we have other configuration docs available there too for more advanced configurations.