How Simulacrum protects your project data on distributed compute nodes.
Every job on Simulacrum runs in a hardened Docker container with strict isolation.
Containers run with --cap-drop=ALL, --no-new-privileges,
memory limits, and PID limits, providing strong isolation between the workload and the host system.
After every job completes (or fails), all project data, intermediate files, and render output are securely wiped from the operator's node.
Key guarantee: Your project files run in an isolated sandbox. After the job, all data is securely wiped from the operator's system.
All jobs run at a flat rate of $0.75/GPU-hour with hardened container isolation, ClamAV scanning, and Proof of Compute verification included.
Every container runs with --cap-drop=ALL, --no-new-privileges, strict memory limits, and PID limits. Even if a client's code contains a vulnerability, it cannot escalate privileges or escape the container.
Every uploaded project is scanned for malware and zip bombs before dispatch.
The orchestrator periodically verifies that nodes are performing real work using perceptual hashing (rendering) and GPU utilization checks (training).
All project data, scene files, and intermediate artifacts are wiped after every job.
All compute nodes are verified as US-based via IP geolocation and Cloudflare.
All jobs automatically run in hardened Docker containers — no configuration needed.
The security flags (--cap-drop=ALL, --no-new-privileges,
memory limits, PID limits) are applied to every job by default.
Container hardening works with all job types: single renders, parameter sweeps, BYOS (Bring Your Own Scene) UE5 projects, and Docker Only jobs.
All project data is securely wiped regardless of whether the job succeeds or fails. You are not charged for jobs that fail before rendering begins.
Yes. Every job automatically runs in a hardened Docker container with
--cap-drop=ALL, --no-new-privileges, memory limits,
and PID limits. No opt-in is required.
No. The rendering process is identical. The security flags have negligible impact on GPU-heavy workloads.