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Important Set the correct Weaviate version

Make sure to set your desired Weaviate version.

This can be done through either explicitly setting it as part of the values.yaml or through overwriting the default as outlined in the deployment step below.


  • A recent Kubernetes Cluster (at least version 1.23).
    • If you are in a development environment, consider using the kubernetes cluster that is built into Docker desktop. For more information, see the Docker documentation.
  • The cluster needs to be able to provision PersistentVolumes through PersistentVolumeClaims.
  • No special file systems are required. Any file system that has a ReadWriteOnce access mode is sufficient.
  • Helm. To use Helm chart version "v16.8.8", you must have Helm v3 or higher.

Weaviate Helm chart

To obtain and install the Weaviate chart on your Kubernetes cluster, take the following steps:

Verify tool setup and cluster access

# Check if helm is installed
helm version
# Make sure `kubectl` is configured correctly and you can access the cluster.
# For example, try listing the pods in the currently configured namespace.
kubectl get pods

Obtain the Helm Chart

Add the Weaviate helm repo that contains the Weaviate helm chart

helm repo add weaviate

Get the default values.yaml configuration file from the Weaviate helm chart:

helm show values weaviate/weaviate > values.yaml

Modify values.yaml (as necessary)

May not be needed

The default values in values.yaml may be sufficient. However, we recommend reviewing:

  • The Weaviate version
  • Modules to enable
  • gRPC service configuration

In the values.yaml file you can tweak the configuration to align it with your setup. The yaml file is extensively documented to help you align the configuration with your setup.

Out of the box, the configuration file is setup for:

  • 1 Weaviate replica.
  • The text2vec-contextionary module is enabled and running with 1 replica. (This can be adjusted based on the expected load).
  • Other modules, such as text2vec-transformers, qna-transformers or img2vec-neural are disabled by default. They can be enabled by setting the respective enabled flag to true.
  • grpcService is disabled by default. If you want to use the gRPC API, set the enabled flag to true and the type to the required type, such as LoadBalancer. This decision to disable the gRPC service by default is made for backward compatibility, and as different setups might require different configurations.

See the resource requests and limits in the example values.yaml. You can adjust them based on your expected load and the resources available on the cluster.

Authentication and authorization

An example configuration for authentication is shown below.

enabled: true
- readonly-key
- secr3tk3y
enabled: false
enabled: true
username_claim: email
groups_claim: groups
client_id: wcs
enabled: true

In this example, the key readonly-key will authenticate a user as the identity, and secr3tk3y will authenticate a user as

OIDC authentication is also enabled, with WCS as the token issuer/identity provider. Thus, users with WCS accounts could be authenticated. This configuration sets as an admin user, so if were to authenticate, they will be given full (read and write) privileges.

For further, general documentation on authentication and authorization configuration, see:

Deploy (install the Helm chart)

You can deploy the helm charts as follows:

# Create a Weaviate namespace
kubectl create namespace weaviate

# Deploy
helm upgrade --install \
"weaviate" \
weaviate/weaviate \
--namespace "weaviate" \
--values ./values.yaml

The above assumes that you have permissions to create a new namespace. If you have only namespace-level permissions, you can skip creating a new namespace and adjust the namespace argument on helm upgrade according to the name of your pre-configured namespace.

Optionally, you can provide the --create-namespace parameter which will create the namespace if not present.

Updating the installation after the initial deployment

The above command (helm upgrade...) is idempotent, you can run it again, for example after adjusting your desired configuration.

Additional Configuration Help

Using EFS with Weaviate

In some circumstances, you may wish, or need, to use EFS (Amazon Elastic File System) with Weaviate. And we note in the case of AWS Fargate, you must create the PV (persistent volume) manually, as the PVC will NOT create a PV for you.

To use EFS with Weaviate, you need to:

  • Create an EFS file system.
  • Create an EFS access point for every Weaviate replica.
    • All of the Access Points must have a different root-directory so that Pods do not share the data, otherwise it will fail.
  • Create EFS mount targets for each subnet of the VPC where Weaviate is deployed.
  • Create StorageClass in Kubernetes using EFS.
  • Create Weaviate Volumes, where each volume has a different AccessPoint for VolumeHandle(as mentioned above).
  • Deploy Weaviate.

This code is an example of a PV for weaviate-0 Pod:

apiVersion: v1
kind: PersistentVolume
name: weaviate-0
storage: 8Gi
volumeMode: Filesystem
- ReadWriteOnce
persistentVolumeReclaimPolicy: Delete
storageClassName: "efs-sc"
volumeHandle: <FileSystemId>::<AccessPointId-for-weaviate-0-Pod>
namespace: <namespace where Weaviate is/going to be deployed>
name: weaviate-data-weaviate-0

For more, general information on running EFS with Fargate, we recommend reading this AWS blog.


  • If you see No private IP address found, and explicit IP not provided, set the pod subnet to be in an valid ip address range of the following:

Questions and feedback

If you have any questions or feedback, please let us know on our forum. For example, you can: