Can we run this ourselves? Yes. Here is the sizing.
Datris runs anywhere Docker runs. An evaluation fits on an 8 GB host with a 2 GB heap. Production wants more, and most of the stack is optional. Nobody gets budget approved without a table, so here is the table.
Sizing
| Profile | Host | JVM heap | Notes |
|---|---|---|---|
| Evaluation / demo | 8 GB RAM, 4 vCPU, 40 GB disk | 2 GB | Postgres+pgvector, MinIO, MCP server, Assistant. Kafka, Spark, ActiveMQ, and the bundled embedding model off. |
| Recommended production | 16 GB RAM, 8 vCPU, 200 GB disk | 4 GB | Full stack on one node. Room for the bundled embedding model and concurrent taps. |
| High availability | 3 nodes × 16 GB RAM, 8 vCPU | 4 GB per node | Platform services on the nodes; Postgres, MongoDB, and object storage run externally (managed or your own clusters). |
Figures are starting points. The docs sizing page has per-component detail and tuning.
What is optional
| Component | When you need it |
|---|---|
| Apache Kafka | Only for streaming sources and Kafka destinations. |
| Apache Spark | Only for distributed transformations on large batches. |
| ActiveMQ | Only for the message-queue destination. |
| Vector stores | Qdrant, Weaviate, Milvus, Chroma are optional; pgvector ships in the base Postgres. |
| Snowflake / Databricks | External accounts, not services Datris runs. Only if you land data there; credentials go in Vault. |
| Ollama | Only for local models in air-gapped deployments. Otherwise the AI features use a hosted provider key. |
| Bundled embedding model | The 2.2 GB bge-m3 model is needed only for air-gapped embeddings; otherwise use a hosted embedding provider. |
| Prometheus / Grafana | Only if you want metrics scraped and the shipped dashboard. The platform runs without them. |
Profiles are selected with COMPOSE_PROFILES and per-component enable flags. The
demo profile is the default for the one-command install.
Reference shape 1 · Single-node pilot
One host. Docker Compose. Postgres with pgvector, MinIO, MongoDB, Vault, the platform services, the MCP server, and the Assistant. Everything else off. This is the shape most teams evaluate on and many run in production for a single data team.
curl -fsSL https://get.datris.ai/install.sh | shThen open http://localhost:4200 in your browser.
Reference shape 2 · HA production
Platform services on three nodes behind a load balancer. State moves out: managed or self-run Postgres, MongoDB, and S3-compatible object storage. Vault runs as your existing Vault cluster or a dedicated one. The optional components above, such as Kafka, Spark, ActiveMQ, or a dedicated vector store, join only if the workload needs them.
Operating it
- Metrics. Prometheus endpoints on every service; a Grafana dashboard ships in the repo.
- Audit to SIEM. The audit log mirrors to your SIEM and exports to CSV.
- Orchestration. An Airflow provider triggers and monitors Datris pipelines from your existing DAGs.
- Air-gapped. Local models via Ollama and the bundled embedding model; no telemetry and no required outbound calls. See Security.