On-prem runtime
Why enterprises run Elastra on-prem for data protection and how to install it
A practical article on why on-prem deployment matters for enterprise data boundaries, how Elastra keeps the runtime inside customer infrastructure, and how to install the public runtime with Docker Compose.
For many engineering organizations, the blocker is not whether AI agents are useful. The blocker is whether repository content, operational knowledge, and internal workflows can stay inside the company boundary. Elastra on-prem exists for that case: the runtime runs inside customer infrastructure, the operator keeps control of the stack, and teams still get the same project, repository, namespace, chat, and CLI operating model.
- Audience
- Security-conscious engineering teams, platform teams, CTOs, staff engineers, and enterprise operators evaluating self-hosted AI engineering infrastructure.
- Objective
- Explain why enterprises choose Elastra on-prem for stronger data boundaries and show the shortest reliable installation path using the public Docker Compose runtime.
Key takeaways
- Elastra on-prem keeps the runtime stack inside customer infrastructure instead of pushing teams into a shared hosted control plane.
- The strongest reason to choose on-prem is data boundary control over repositories, knowledge, and internal operational workflows.
- The public runtime can be installed with a small Docker Compose flow and then used through the same local CLI model.
Why on-prem matters for enterprise data protection
Enterprise adoption of AI engineering tools usually fails on trust boundaries before it fails on product quality. Teams may like AI-assisted development, but security and platform leaders still need to answer where repository content lives, where operational knowledge is processed, who controls the infrastructure, and whether the system can be isolated from shared hosted environments.
That is the real role of Elastra on-prem. It lets the customer run the runtime stack inside its own environment, keep ownership of the deployment boundary, and decide how network, storage, secrets, and access controls are managed. For many organizations, that is the difference between a pilot and an approved production rollout.
What stays inside the customer boundary
- The runtime services themselves: API, worker, PostgreSQL, Redis, Qdrant, and MinIO.
- Project and namespace configuration used to organize engineering work.
- Repository-related context and operational usage data that the customer wants to keep inside its infrastructure boundary.
- Bootstrap admin access and local operator flows for the deployment.
Important nuance
Running on-prem does not automatically solve every governance question. Customers still need to define how provider API keys are managed, who can access the stack, how backups are handled, and what data retention rules apply. What on-prem changes is the default control boundary: the stack is yours to operate.
How to install the on-prem runtime
The public runtime is distributed as a Docker image and started through the public Docker Compose file. The shortest path is to create a local deployment folder, download the compose file from the public repository, and start the stack.
After the stack is healthy, the web UI is normally available at `http://localhost:8080`. Then install the Elastra CLI from the public installer endpoint, point it at the local server with `ELASTRA_API_URL`, and authenticate with `elastra auth login` followed by `elastra init`.
Minimum command flow
- `mkdir -p elastra-onprem && cd elastra-onprem`
- `curl -fsSL https://raw.githubusercontent.com/elastraai/elastra/main/docker-compose.yml -o docker-compose.yml`
- `docker compose up -d`
- `curl -fsSL https://api.elastra.ai/v1/downloads/install.sh | bash`
- `export ELASTRA_API_URL=http://localhost:8080`
- `elastra auth login` and `elastra init`
What teams get after installation
The on-prem runtime is not just a container demo. It gives teams a real operating model: projects to segment work, namespaces to control context boundaries, repositories to connect code, chat for active execution, knowledge for direct inspection, and the CLI for repository-local workflows.
That matters because the real enterprise question is not whether a model can answer a prompt. The question is whether the company can give engineers useful AI assistance without giving up control of deployment, access, and operational boundaries. That is the problem the on-prem runtime is meant to solve.
The point of on-prem is not isolation for its own sake. It is giving engineering teams AI assistance inside the data boundary they are actually allowed to operate in.