Spice.ai Deployment Guide
Spice runs as a single binary, a container, a Kubernetes workload, or a fully managed app on the Spice Cloud Platform. This guide helps choose a target environment and a deployment architecture to match an application's latency, scale, and operational requirements.
Choose a deployment target​
Most users fall into one of three groups:
- Run Spice next to an application — start with Docker for a local container, or follow Getting Started to run the binary directly.
- Operate Spice in production on Kubernetes — use the Spice Helm chart. For automated rollouts, see the CI/CD guide for Helm pipelines and GitOps with Argo CD or Flux.
- Use a managed service — deploy a Spicepod to the Spice Cloud Platform and connect a GitHub repository for continuous delivery.
For production self-hosted clusters, the Spice.ai Enterprise Kubernetes Operator provides per-replica StatefulSets, automatic PVC resizing, configurable update strategies, crashloop protection, and distributed query execution through SpicepodSet and SpicepodCluster custom resources.
Deployment architectures​
Architecture refers to where Spice runs in relation to the application and data sources, and how it scales. Pick an architecture before choosing a guide; the same target environment can host any of these patterns.
- Overview — when to choose each architecture.
- Sidecar — Spice runs alongside the application for the lowest latency.
- Microservice — single or multiple replicas behind a load balancer.
- Tiered — separate read and write tiers for mixed workloads.
- Cluster-Sidecar — combine local and remote Spice instances.
- Hosted — managed on the Spice Cloud Platform.
- Sharded — partition data across multiple Spice instances.
- Cluster — distributed query execution with Spice.ai Enterprise.
Deployment guides​
Step-by-step instructions for each target environment.
| Guide | When to use |
|---|---|
| Kubernetes | Self-hosted production deployments. Covers Helm, Argo CD, and Flux. |
| Docker | Local development, single-host deployments, and container-based pipelines. |
| Spice Cloud | Fully managed deployments without operating infrastructure. |
| AWS | Deployments on AWS using the published CloudFormation template. |
| Azure | Deployments on Azure using ARM/Bicep templates. |
| GCP | Deployments on Google Cloud using GKE, Cloud Run, or Compute Engine. |
| CI/CD | Automating any of the above through pipelines or GitOps. |
| Read/Write Separation | Production pattern that splits ingest from reads using shared snapshots. |
