--- title: "embedguard vs gateway" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/neerazz-embedguard-vs-trylonai-gateway" tools: ["neerazz-embedguard", "trylonai-gateway"] --- # embedguard vs gateway *GraphCanon updated Sep 20, 2026* ## Verdict Pick embedguard if embedguard is a Python tool designed for detecting and attesting provenance against adversarial embedding attacks in RAG systems, emphasizing AI safety and security; pick gateway if self-hosted firewall for securing AI applications with guardrails and content moderation. [embedguard](https://github.com/neerazz/embedguard) reports 0 GitHub stars, 0 forks, and 0 open issues, last pushed Aug 18, 2026. [gateway](https://www.trylon.ai) has 131 stars, 12 forks, and 0 open issues, last pushed Jun 25, 2025. Figures are from public GitHub metadata via [embedguard's repository](https://github.com/neerazz/embedguard) and [gateway's repository](https://github.com/trylonai/gateway). | | [embedguard](/tools/neerazz-embedguard.md) | [gateway](/tools/trylonai-gateway.md) | | --- | --- | --- | | Tagline | Cross-Layer Detection and Provenance Attestation for Adversarial Embedding Attacks in RAG Systems | Self-hosted firewall for securing AI applications with guardrails and content moderation. | | Stars | 0 | 131 | | Forks | 0 | 12 | | Open issues | 0 | 0 | | Language | Python | Python | | Adopt for | Embedguard is a Python tool designed for detecting and attesting provenance against adversarial embedding attacks in RAG systems, emphasizing AI safety and security. | Self-hosted firewall for securing AI applications with guardrails and content moderation. | | Persona | - | - | | Runtime | - | - | | License | MIT License | Other | | Categories | Evaluation & Observability, LLM Frameworks, Vector Databases | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [embedguard](/tools/neerazz-embedguard.md) | [gateway](/tools/trylonai-gateway.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 30d | 445d | | Stars delta | 0 (30d) | +4 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/neerazz-embedguard/trust.md) | [trust report](/tools/trylonai-gateway/trust.md) | ## Decision facts: embedguard - **Requirements:** Installation requires Python and pip.; Development mode installation is supported. - **Adopt for:** Embedguard is a Python tool designed for detecting and attesting provenance against adversarial embedding attacks in RAG systems, emphasizing AI safety and security. - **License detail:** MIT License ## Decision facts: gateway - **Pricing:** freemium - Open-source with no explicit monetary cost, but requires users to handle infrastructure costs associated with local Docker deployment - **Requirements:** Min 4 GB RAM; Requires Docker; Docker and Docker Compose are required for deployment; Initial launch takes several minutes due to downloading machine learning models (~1.5GB+) for the first time - **Adopt for:** Self-hosted firewall for securing AI applications with guardrails and content moderation. ## Choose when ### Choose embedguard if… - License: embedguard is MIT, gateway is Other. - Requirements: Installation requires Python and pip.; Development mode installation is supported.. - Tags unique to embedguard: ai-safety, embedding-attacks, llm, llm-security. - Also covers Vector Databases. - When you need to ensure the integrity of embeddings in RAG systems against adversarial attacks. ### Choose gateway if… - License: gateway is Other, embedguard is MIT. - Pricing: Open-source with no explicit monetary cost, but requires users to handle infrastructure costs associated with local Docker deployment. - Requirements: Min 4 GB RAM; Requires Docker; Docker and Docker Compose are required for deployment; Initial launch takes several minutes due to downloading machine learning models (~1.5GB+) for the first time. - Tags unique to gateway: content-moderation, docker-compose, guardrails, pii-redaction. - When you need to self-host a secure gateway for LLM-based AI applications that requires local deployment via Docker ## When NOT to use embedguard - If your focus is on general-purpose AI model evaluation without a specific need for adversarial embedding attack detection. - When your application does not involve RAG systems or you are not concerned with embedding security and provenance. ## When NOT to use gateway - Avoid if you are looking for cloud-managed services, as this tool is designed solely for local or self-hosted environments - Not suitable if you require extensive customization beyond the provided policies.yaml file and predefined guardrails for content moderation and security ## Common questions ### What is the difference between embedguard and gateway? embedguard: Cross-Layer Detection and Provenance Attestation for Adversarial Embedding Attacks in RAG Systems. gateway: Self-hosted firewall for securing AI applications with guardrails and content moderation.. See the comparison table for live GitHub stats and shared categories. ### When should I choose embedguard over gateway? Choose embedguard over gateway when License: embedguard is MIT, gateway is Other; Requirements: Installation requires Python and pip.; Development mode installation is supported.; Tags unique to embedguard: ai-safety, embedding-attacks, llm, llm-security; Also covers Vector Databases; When you need to ensure the integrity of embeddings in RAG systems against adversarial attacks. ### When should I choose gateway over embedguard? Choose gateway over embedguard when License: gateway is Other, embedguard is MIT; Pricing: Open-source with no explicit monetary cost, but requires users to handle infrastructure costs associated with local Docker deployment; Requirements: Min 4 GB RAM; Requires Docker; Docker and Docker Compose are required for deployment; Initial launch takes several minutes due to downloading machine learning models (~1.5GB+) for the first time; Tags unique to gateway: content-moderation, docker-compose, guardrails, pii-redaction; When you need to self-host a secure gateway for LLM-based AI applications that requires local deployment via Docker. ### When should I avoid embedguard? If your focus is on general-purpose AI model evaluation without a specific need for adversarial embedding attack detection. When your application does not involve RAG systems or you are not concerned with embedding security and provenance. ### When should I avoid gateway? Avoid if you are looking for cloud-managed services, as this tool is designed solely for local or self-hosted environments Not suitable if you require extensive customization beyond the provided policies.yaml file and predefined guardrails for content moderation and security ### Is embedguard or gateway more popular on GitHub? gateway has more GitHub stars (131 vs 0). Stars measure visibility, not whether either tool fits your constraints. ### Are embedguard and gateway open source? Yes - both are open-source projects on GitHub (embedguard: MIT, gateway: Other). ### Where can I find alternatives to embedguard or gateway? GraphCanon lists graph-backed alternatives at [embedguard alternatives](/tools/neerazz-embedguard/alternatives) and [gateway alternatives](/tools/trylonai-gateway/alternatives) ([embedguard markdown twin](/tools/neerazz-embedguard/alternatives.md), [gateway markdown twin](/tools/trylonai-gateway/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/neerazz-embedguard-vs-trylonai-gateway.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, embedguard or gateway? embedguard: Steady. gateway: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for embedguard and gateway? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [embedguard trust report](/tools/neerazz-embedguard/trust); [gateway trust report](/tools/trylonai-gateway/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=neerazz-embedguard`](/api/graphcanon/graph?tool=neerazz-embedguard) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_