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Deploying Weaviate

Weaviate is available as a hosted service, Weaviate Cloud (WCD), or as a self managed instance. If you manage your own instance, you can host it locally or with a cloud provider. Self-managed instances use the same Weaviate Database as WCD.

If you are upgrading from a previous version of Weaviate, see the Migration guide for any changes that may affect your installation.

Weaviate offers multiple deployment options to satisfy your specific use case in production.

This section hosts common deployment topics, including Kubernetes, cloud providers, and best practices, along with detailed tutorials and how-to guides. Weaviate is designed for:

  • Scalability – Handle billions of vector data points efficiently.
  • High-Performance Search – Power AI applications with real-time vector retrieval.
  • Flexible Integrations – Connect with various machine learning models and data sources.
  • Cloud & On-Prem Deployment – Deploy on Weaviate Cloud, Kubernetes, or managed cloud services.

Choose the best deployment method based on your needs:

In addition to the above deployment methods, we also have the following:

Docker Compose and Kubernetes use yaml files to configure Weaviate instances. Docker uses the docker-compose.yml file. Kubernetes relies on Helm charts and the values.yaml file. The Weaviate documentation also calls these files configuration yaml files.

If you are self-hosting, consider experimenting on a small scale with Docker and then transferring your configuration to Kubernetes Helm charts when you are more familiar with Weaviate.

To run an unreleased version of Weaviate, edit your configuration file to use the unreleased image instead of a generally available image. The GitHub releases page lists generally available and release candidate builds.

For example, to run a Docker image for a release candidate, edit your docker-config.yaml to import the release candidate image.

YAML
image: cr.weaviate.io/semitechnologies/weaviate:1.34.0-rc.1

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