--- title: "arthur-engine vs ai-gateway" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/arthur-ai-arthur-engine-vs-ferro-labs-ai-gateway" tools: ["arthur-ai-arthur-engine", "ferro-labs-ai-gateway"] --- # arthur-engine vs ai-gateway *GraphCanon updated Sep 20, 2026* ## Verdict Pick arthur-engine if the Arthur Engine monitors AI/ML workloads with a focus on guardrails for LLM applications, evaluation of agentic systems, extensive model monitoring metrics, and extensible API support; pick ai-gateway if ai-gateway from Ferro Labs supports over 30 LLMs with integrated caching, guardrails, A/B testing, and cost controls, making it ideal for managing multiple language models in a production. [arthur-engine](https://arthur.ai) reports 89 GitHub stars, 16 forks, and 16 open issues, last pushed Sep 12, 2026. [ai-gateway](https://docs.ferrolabs.ai) has 256 stars, 35 forks, and 68 open issues, last pushed Sep 10, 2026. Figures are from public GitHub metadata via [arthur-engine's repository](https://github.com/arthur-ai/arthur-engine) and [ai-gateway's repository](https://github.com/ferro-labs/ai-gateway). | | [arthur-engine](/tools/arthur-ai-arthur-engine.md) | [ai-gateway](/tools/ferro-labs-ai-gateway.md) | | --- | --- | --- | | Tagline | Monitoring and governing for your AI/ML | Unified AI Gateway for multiple LLMs with caching, guardrails, A/B testing, and cost controls | | Stars | 89 | 256 | | Forks | 16 | 35 | | Open issues | 16 | 68 | | Language | Python | Go | | Adopt for | The Arthur Engine monitors AI/ML workloads with a focus on guardrails for LLM applications, evaluation of agentic systems, extensive model monitoring metrics, and extensible API support. | ai-gateway from Ferro Labs supports over 30 LLMs with integrated caching, guardrails, A/B testing, and cost controls, making it ideal for managing multiple language models in a production environment. | | Persona | - | - | | Runtime | - | - | | License | MIT License, allowing free use and modification of the tool's codebase under the terms of this license. | Apache-2.0 - a permissive free software license | | Categories | Evaluation & Observability, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [arthur-engine](/tools/arthur-ai-arthur-engine.md) | [ai-gateway](/tools/ferro-labs-ai-gateway.md) | | --- | --- | --- | | Days since push | 0d | 2d | | Open issues (now) | 16 | 68 | | Stars delta | +3 (30d) | +37 (30d) | | Open issues delta | -16 (30d) | +5 (30d) | | Full report | [trust report](/tools/arthur-ai-arthur-engine/trust.md) | [trust report](/tools/ferro-labs-ai-gateway/trust.md) | ## Decision facts: arthur-engine - **Adopt for:** The Arthur Engine monitors AI/ML workloads with a focus on guardrails for LLM applications, evaluation of agentic systems, extensive model monitoring metrics, and extensible API support. - **License detail:** MIT License, allowing free use and modification of the tool's codebase under the terms of this license. ## Decision facts: ai-gateway - **Adopt for:** ai-gateway from Ferro Labs supports over 30 LLMs with integrated caching, guardrails, A/B testing, and cost controls, making it ideal for managing multiple language models in a production environment. - **License detail:** Apache-2.0 - a permissive free software license ## Choose when ### Choose arthur-engine if… - arthur-engine is primarily Python; ai-gateway is Go. - License: arthur-engine is MIT, ai-gateway is Apache-2.0. - Tags unique to arthur-engine: agentic, benchmarking, evaluation, genai. - Also covers Evaluation & Observability. - When developing or managing large language models that require real-time detection of sensitive data leakage, hallucination, or prompt injection. ### Choose ai-gateway if… - ai-gateway is primarily Go; arthur-engine is Python. - License: ai-gateway is Apache-2.0, arthur-engine is MIT. - Tags unique to ai-gateway: ai-gateway, litellm, llm-cost, llm-proxy. - Also covers Inference & Serving. - When you need to integrate more than 30 different LLM services including OpenAI and Anthropic ## When NOT to use arthur-engine - Avoid if the project does not require real-time monitoring and evaluation on live data streams. - Not suitable for teams that prefer minimalistic setups over comprehensive services with wide-ranging capabilities. - It may be overkill for organizations focused exclusively on model training without subsequent need for ongoing monitoring or governance. ## When NOT to use ai-gateway - If your project only involves one or two LLMs which does not necessitate the gateway's broad compatibility features - For small-scale projects that do not require comprehensive cost analysis tools - When custom integration for specific guardrails is required, as ai-gateway offers generalized settings ## Common questions ### What is the difference between arthur-engine and ai-gateway? arthur-engine: Monitoring and governing for your AI/ML. ai-gateway: Unified AI Gateway for multiple LLMs with caching, guardrails, A/B testing, and cost controls. See the comparison table for live GitHub stats and shared categories. ### When should I choose arthur-engine over ai-gateway? Choose arthur-engine over ai-gateway when arthur-engine is primarily Python; ai-gateway is Go; License: arthur-engine is MIT, ai-gateway is Apache-2.0; Tags unique to arthur-engine: agentic, benchmarking, evaluation, genai; Also covers Evaluation & Observability; When developing or managing large language models that require real-time detection of sensitive data leakage, hallucination, or prompt injection. ### When should I choose ai-gateway over arthur-engine? Choose ai-gateway over arthur-engine when ai-gateway is primarily Go; arthur-engine is Python; License: ai-gateway is Apache-2.0, arthur-engine is MIT; Tags unique to ai-gateway: ai-gateway, litellm, llm-cost, llm-proxy; Also covers Inference & Serving; When you need to integrate more than 30 different LLM services including OpenAI and Anthropic. ### When should I avoid arthur-engine? Avoid if the project does not require real-time monitoring and evaluation on live data streams. Not suitable for teams that prefer minimalistic setups over comprehensive services with wide-ranging capabilities. It may be overkill for organizations focused exclusively on model training without subsequent need for ongoing monitoring or governance. ### When should I avoid ai-gateway? If your project only involves one or two LLMs which does not necessitate the gateway's broad compatibility features For small-scale projects that do not require comprehensive cost analysis tools When custom integration for specific guardrails is required, as ai-gateway offers generalized settings ### Is arthur-engine or ai-gateway more popular on GitHub? ai-gateway has more GitHub stars (256 vs 89). Stars measure visibility, not whether either tool fits your constraints. ### Are arthur-engine and ai-gateway open source? Yes - both are open-source projects on GitHub (arthur-engine: MIT, ai-gateway: Apache-2.0). ### Where can I find alternatives to arthur-engine or ai-gateway? GraphCanon lists graph-backed alternatives at [arthur-engine alternatives](/tools/arthur-ai-arthur-engine/alternatives) and [ai-gateway alternatives](/tools/ferro-labs-ai-gateway/alternatives) ([arthur-engine markdown twin](/tools/arthur-ai-arthur-engine/alternatives.md), [ai-gateway markdown twin](/tools/ferro-labs-ai-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/arthur-ai-arthur-engine-vs-ferro-labs-ai-gateway.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, arthur-engine or ai-gateway? arthur-engine: Very active. ai-gateway: Very active. 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 arthur-engine and ai-gateway? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [arthur-engine trust report](/tools/arthur-ai-arthur-engine/trust); [ai-gateway trust report](/tools/ferro-labs-ai-gateway/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=arthur-ai-arthur-engine`](/api/graphcanon/graph?tool=arthur-ai-arthur-engine) - 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/_