A self-managed deployment runs the SeekrFlow platform in your Kubernetes cluster and keeps its data in services you provision alongside it. Users and API clients reach it through a single hostname.
How it works
- Installation – Helm installs the SeekrFlow operator. The operator reads your values file, creates the platform services, model servers, agents, and tools, and keeps them matched to your configuration. The operator, not Helm, owns most SeekrFlow pods.
- Access – Users and API clients reach SeekrFlow through the ingress hostname. The authentication service validates every request.
- Capabilities – The platform services provide the web interface and API, agents and custom functions, fine-tuning and dataset preparation, inference, and explainability.
- Inference – The inference router sends each request to a model server on your GPU nodes, or to Amazon Bedrock for externally hosted models. Model servers cache their weights on shared file storage.
- Training and data preparation – Fine-tuning, alignment, and file ingestion run as jobs through Argo Workflows, on GPU and CPU nodes.
- Data – PostgreSQL stores service data. Your object storage holds datasets, model artifacts, and fine-tuning checkpoints. Retrieval-augmented generation uses OpenSearch.
- Licensing – Services don’t report ready until the License service confirms a valid license key. The license also sets a ceiling on the number of GPUs SeekrFlow workloads can request across the cluster.
Services in a deployment
Services you provide
Which services fill each role depends on where you deploy SeekrFlow. Select your environment.
Components the chart deploys
Last modified on October 16, 2026
