GuardionAI vs NVIDIA NeMo Guardrails
GuardionAI
guardion.ai
NVIDIA NeMo Guardrails
developer.nvidia.comSide by side
| Figure | GuardionAI | NVIDIA NeMo Guardrails |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | Yes | — |
| Open source | — | Yes |
| Web | Yes | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 3 |
| Spec score · LLM Security Tools | 8.7 (#3) | 8.6 (#4) |
| Deployment | GuardionAI says it can run inline as a gateway or be called through its API, with no application rewrites. | The library can run in a self-managed Python environment, while the separate NeMo Guardrails microservice uses a container image and Kubernetes with Helm. |
| Documentation depth | Public documentation includes API reference pages, cURL examples, detector descriptions, policy behavior, telemetry schemas and DLP integration guidance. | The documentation covers installation, configuration, Colang, APIs, evaluation, vulnerability scanning, observability, Docker, microservices, troubleshooting and security. |
| Integrations | The site lists connections including Pinecone, Weaviate, Milvus, Wiz, Prisma Cloud, Microsoft Defender, Orca Security, Cloudflare, LiteLLM, Portkey, OpenAI, Claude, Gemini, AWS, LangChain, LlamaIndex and CrewAI. | The documentation lists integrations with NVIDIA safety models, community models, third-party APIs, Python actions, and LangChain tools. |
| Telemetry | Each evaluation can be recorded as sessions, traces and spans, with exportable logs and aggregated findings. | The library emits anonymous usage events and heartbeats by default and provides environment-variable and file-based opt-out controls. |
| What it does | GuardionAI provides runtime visibility, policy enforcement and evidence for customer-facing, autonomous and coding AI agents. | NeMo Guardrails is an open-source Python package that intercepts LLM inputs and outputs and applies programmable policies to block, modify or validate content. |
Plans
NVIDIA NeMo Guardrails
- Open-source NeMo Guardrails libraryFree
- Open-source NeMo Guardrails libraryFree
- NeMo Guardrails microservicePriced on request
In their own words
GuardionAI
GuardionAI provides runtime security and governance for customer-facing AI, autonomous agents, and coding agents. Its gateway evaluates agent and MCP calls inline and can allow, block, or redact actions before they execute. Data protection features identify personal information and secrets, mask or vault-tokenize them before requests leave an organization, and can restore responses for users. Runtime guardrails detect prompt injection, jailbreaks, harmful content, and unsafe tool calls; they can block, redact, or flag actions. A coding-agent plugin monitors tool calls and can stop destructive commands, secret exfiltration, and unauthorized calls before they run. GuardionAI can operate as an inline gateway or through its API, and the maker says this requires no application rewrites. Listed integrations include OpenAI, Claude, Gemini, LangChain, LlamaIndex, LangGraph, Pinecone, AWS, Google Cloud, and Microsoft Azure. The company says its gateway is encrypted, model providers do not see raw data, and customer data is not used to train its models. It describes the service as GDPR-, HIPAA-, and LGPD-ready, with SOC 2 Type II in progress.
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.