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InferenceWall supports three deployment paths: a Python package installed directly on the host, a Docker container, or a Kubernetes deployment via Helm. All three paths expose the same API server and SDK. Choose based on your existing infrastructure.

Installation

Pre-built wheels are available for Linux x86_64, Linux aarch64, macOS arm64, and Windows x86_64. Requires Python >= 3.10.

Deployment profiles

Post-install setup

1

Generate API keys

This generates a scan key (iwk_scan_…) and an admin key (iwk_admin_…) and writes them to .env.local.
2

Set environment variables

Export the generated keys before starting the server:
Or source the generated file directly:
3

Start the server

The server listens on 0.0.0.0:8000 by default.
4

Install ML models (Standard and Full only)

If you installed the standard or full profile, download the ML models:
Models are cached in ~/.cache/inferwall/models/ and downloaded from HuggingFace (~730 MB for Standard).
5

Run a health check

Confirm the server is up and signatures are loaded:
In development, you can skip API key setup entirely. Run inferwall serve without setting IW_API_KEY or IW_ADMIN_KEY and scan without any Authorization header. Dev mode is not suitable for production.

Environment variables

TLS modes

Redis for distributed sessions

Set IW_REDIS_URL to enable distributed rate limiting and session state across multiple InferenceWall instances:
When unset, InferenceWall uses in-process state, which is scoped to a single instance.

Health check endpoints

Further reading

Environment variables reference

Complete list of all environment variables with types, defaults, and valid values.

Health API

Response schemas for the liveness, readiness, and full health endpoints.