LLM Guard vs Straiker Defend AI
LLM Guard
github.com
Straiker Defend AI
straiker.aiSide by side
| Figure | LLM Guard | Straiker Defend AI |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Linux | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · LLM Security Tools | 7.6 (#11) | 6.7 (#26) |
| Integrations | The documentation includes integrations and examples for the OpenAI SDK, LangChain, LiteLLM, and LlamaIndex. | Straiker lists integrations with Amazon Bedrock AgentCore, Google Vertex AI, Microsoft AI Foundry, Microsoft Copilot Studio, Kong AI Gateway, and LiteLLM; it also describes OpenAI and Anthropic partnerships. |
| Intended users | The project describes production deployment and offers tutorials for developers building LLM applications, including guarded RAG and agent workflows. | Straiker says it is built for security-sensitive organizations deploying agents, including financial services, healthcare, technology, media, retail, and ecommerce companies. |
| Purpose | LLM Guard is a security toolkit for LLM interactions that sanitizes inputs, detects harmful language, helps prevent data leakage, and resists prompt injection attacks. | Defend AI provides runtime security for coding, productivity, and custom-built AI agents, detecting and blocking prompt injection, data exfiltration, and agent manipulation. |
Plans
In their own words
LLM Guard
LLM Guard is a self-hosted security toolkit for checking interactions with large language models. It scans prompts and outputs for concerns such as prompt injection, secrets, personally identifiable information, toxicity, bias, malicious URLs, and factual consistency. It can sanitize interactions, detect harmful language, help prevent data leakage, and resist prompt injection attacks. The documentation includes examples for the OpenAI SDK, LangChain, LiteLLM, and LlamaIndex. In the OpenAI examples, prompts and responses are scanned; a streaming example also checks response chunks. LLM Guard can run as an API, and custom scanners can be added. Installation uses `pip install llm-guard` and requires Python 3.9 or higher. It is free and listed under the MIT License, with API, Linux, and self-hosted platforms. The repository was archived on July 9, 2026, is read-only, and is no longer actively developed or maintained. Production deployment assistance is directed to GitHub issues, Slack, and [email protected].
Straiker Defend AI
Straiker Defend AI has no write-up on Specifiction yet; its figures are above.