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NVIDIA NeMo Guardrails vs Sanctum Runtime

NVIDIA NeMo Guardrails

developer.nvidia.com

Sanctum Runtime

sanctumruntime.com

Side by side

FigureNVIDIA NeMo GuardrailsSanctum Runtime
Price fromFree—
Free planYes—
Free trial——
Open sourceYes—
Web—Yes
WindowsYes—
macOSYes—
LinuxYesYes
iOS—Yes
Android—Yes
APIYesYes
Self-hostedYesYes
Plans published30
Spec score · LLM Security Tools8.6 (#4)7.2 (#19)
IntegrationsThe documentation lists integrations with NVIDIA safety models, community models, third-party APIs, Python actions, and LangChain tools.The same verification path is described for OpenAI, Claude/MCP, Gemini-style function calling, DeepSeek, Grok, Bedrock-compatible endpoints, NVIDIA NIM, custom agents, and local runtimes.
Intended usersThe library is presented for developers and teams building LLM applications who need configurable policy enforcement and safety checks.The site presents the product for agent startups, platform and AI engineers, and security or compliance teams.
PurposeNeMo Guardrails is an open-source Python package for adding programmable guardrails to LLM-based applications.Sanctum is a runtime trust layer that evaluates AI actions before they execute.

Plans

NVIDIA NeMo Guardrails

  • Open-source NeMo Guardrails libraryFree
  • Open-source NeMo Guardrails libraryFree
  • NeMo Guardrails microservicePriced on request

NVIDIA NeMo Guardrails pricing →

Sanctum Runtime

No plans published.

Sanctum Runtime pricing →

In their own words

NVIDIA NeMo Guardrails

NVIDIA NeMo Guardrails is an open-source Python package for adding programmable policy and safety checks to applications built with large language models. It can block, alter, or validate inputs and model responses that are unsafe, off-topic, malicious, or against configured policies. Its rails cover input, retrieval, dialog, execution, and output stages. Developers can configure behavior with YAML files, Colang flows, built-in guardrails, and custom actions. The catalog includes content safety, jailbreak protection, topic control, PII detection, agentic security, tool calling, hallucination and fact-checking, and model self-checks. Applications can use the Python SDK or a Guardrails server with HTTP endpoints such as OpenAI-style chat completions. The library supports Windows, Linux, and macOS, and can run in a self-managed Python environment. The installation guide lists Python 3.10 through 3.13 and at least one CPU with 4GB RAM; external models may require separately deployed GPUs. The open-source library costs nothing, though external models and services may need separate setup. NVIDIA cautions that no single rail covers every adversarial prompt and advises defense in depth and evaluation.

Visit developer.nvidia.com

Sanctum Runtime

Sanctum Runtime is middleware that checks actions from AI agents, robots, and automated systems before they are carried out. It evaluates proposed actions against policies and can approve them, ask for verification, or block them. Operators can review, approve, deny, or escalate actions through web and mobile workflows. The product describes behavioral monitoring, audit logs, policy history, and decision trails for runtime visibility and incident review. Its open-core runtime supports local policy checks and offline verification, including local Ollama risk scoring. Documented integrations include OpenAI, Claude/MCP, Gemini-style function calling, DeepSeek, Grok, Bedrock-compatible endpoints, NVIDIA NIM, custom agents, and local runtimes. Developers can use Node and Python SDKs, an MCP wrapper, ROS2-oriented policy packs, and smart-home and embodied-agent templates. The repository is described as MIT-licensed and self-hostable; a hosted console and proxy path are also available. The product identifies itself as embedded runtime middleware, not a standalone consumer app or robot controller. Some enterprise capabilities, including fleet orchestration and organization-wide policy sync, are private.

Visit sanctumruntime.com