--- title: "ai-gateway vs Rapid-MLX" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ferro-labs-ai-gateway-vs-raullenchai-rapid-mlx" tools: ["ferro-labs-ai-gateway", "raullenchai-rapid-mlx"] --- # ai-gateway vs Rapid-MLX *GraphCanon updated Aug 9, 2026* ## Verdict 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 environment; pick Rapid-MLX if rapid-MLX is a high-speed local AI engine for Apple Silicon devices that supports OpenAI-compatible APIs and multiple models optimized based on system RAM size. [ai-gateway](https://docs.ferrolabs.ai) reports 219 GitHub stars, 33 forks, and 63 open issues, last pushed Aug 7, 2026. [Rapid-MLX](https://pypi.org/project/rapid-mlx) has 3.4k stars, 388 forks, and 48 open issues, last pushed Aug 1, 2026. Figures are from public GitHub metadata via [ai-gateway's repository](https://github.com/ferro-labs/ai-gateway) and [Rapid-MLX's repository](https://github.com/raullenchai/Rapid-MLX). | | [ai-gateway](/tools/ferro-labs-ai-gateway.md) | [Rapid-MLX](/tools/raullenchai-rapid-mlx.md) | | --- | --- | --- | | Tagline | Unified AI Gateway for multiple LLMs with caching, guardrails, A/B testing, and cost controls | Fast local AI engine for Apple Silicon | | Stars | 219 | 3,391 | | Forks | 33 | 388 | | Open issues | 63 | 48 | | Language | Go | Python | | 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. | Rapid-MLX is a high-speed local AI engine for Apple Silicon devices that supports OpenAI-compatible APIs and multiple models optimized based on system RAM size. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 - a permissive free software license | Apache-2.0 | | Categories | Inference & Serving, Model Training | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ai-gateway](/tools/ferro-labs-ai-gateway.md) | [Rapid-MLX](/tools/raullenchai-rapid-mlx.md) | | --- | --- | --- | | Days since push | 2d | 0d | | Open issues (now) | 63 | 48 | | Owner type | Organization | User | | Full report | [trust report](/tools/ferro-labs-ai-gateway/trust.md) | [trust report](/tools/raullenchai-rapid-mlx/trust.md) | ## 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 ## Decision facts: Rapid-MLX - **Pricing:** freemium - Rapid-MLX is free to install and use, but some advanced features may require additional configuration or payment. - **Requirements:** Min 8 GB RAM - **Adopt for:** Rapid-MLX is a high-speed local AI engine for Apple Silicon devices that supports OpenAI-compatible APIs and multiple models optimized based on system RAM size. ## Choose when ### Choose ai-gateway if… - ai-gateway is primarily Go; Rapid-MLX is Python. - Tags unique to ai-gateway: ai-gateway, litellm, llm-cost, llm-proxy. - Also covers Model Training. - When you need to integrate more than 30 different LLM services including OpenAI and Anthropic ### Choose Rapid-MLX if… - Rapid-MLX is primarily Python; ai-gateway is Go. - Pricing: Rapid-MLX is free to install and use, but some advanced features may require additional configuration or payment.. - Requirements: Min 8 GB RAM. - Tags unique to Rapid-MLX: apple-silicon, local-llm, openai-replacement, tool-calling. - Use Rapid-MLX when you need an ultra-fast local inference solution specifically tailored for Apple's M1, M2, or M3 chips, as it is up to 4.2 times faster than Ollama. ## 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 ## When NOT to use Rapid-MLX - Avoid Rapid-MLX if you do not have an Apple Silicon device, as its performance optimizations and support are exclusively for Apple's M1, M2, or M3 processors. - Do not use this tool if your project requires complex vision or audio models out of the box; these extras must be installed separately. ## Common questions ### What is the difference between ai-gateway and Rapid-MLX? ai-gateway: Unified AI Gateway for multiple LLMs with caching, guardrails, A/B testing, and cost controls. Rapid-MLX: Fast local AI engine for Apple Silicon. See the comparison table for live GitHub stats and shared categories. ### When should I choose ai-gateway over Rapid-MLX? Choose ai-gateway over Rapid-MLX when ai-gateway is primarily Go; Rapid-MLX is Python; Tags unique to ai-gateway: ai-gateway, litellm, llm-cost, llm-proxy; Also covers Model Training; When you need to integrate more than 30 different LLM services including OpenAI and Anthropic. ### When should I choose Rapid-MLX over ai-gateway? Choose Rapid-MLX over ai-gateway when Rapid-MLX is primarily Python; ai-gateway is Go; Pricing: Rapid-MLX is free to install and use, but some advanced features may require additional configuration or payment.; Requirements: Min 8 GB RAM; Tags unique to Rapid-MLX: apple-silicon, local-llm, openai-replacement, tool-calling; Use Rapid-MLX when you need an ultra-fast local inference solution specifically tailored for Apple's M1, M2, or M3 chips, as it is up to 4.2 times faster than Ollama. ### 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 ### When should I avoid Rapid-MLX? Avoid Rapid-MLX if you do not have an Apple Silicon device, as its performance optimizations and support are exclusively for Apple's M1, M2, or M3 processors. Do not use this tool if your project requires complex vision or audio models out of the box; these extras must be installed separately. ### Is ai-gateway or Rapid-MLX more popular on GitHub? Rapid-MLX has more GitHub stars (3,391 vs 219). Stars measure visibility, not whether either tool fits your constraints. ### Are ai-gateway and Rapid-MLX open source? Yes - both are open-source projects on GitHub (ai-gateway: Apache-2.0, Rapid-MLX: Apache-2.0). ### Where can I find alternatives to ai-gateway or Rapid-MLX? GraphCanon lists graph-backed alternatives at [ai-gateway alternatives](/tools/ferro-labs-ai-gateway/alternatives) and [Rapid-MLX alternatives](/tools/raullenchai-rapid-mlx/alternatives) ([ai-gateway markdown twin](/tools/ferro-labs-ai-gateway/alternatives.md), [Rapid-MLX markdown twin](/tools/raullenchai-rapid-mlx/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/ferro-labs-ai-gateway-vs-raullenchai-rapid-mlx.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ai-gateway or Rapid-MLX? ai-gateway: Very active. Rapid-MLX: 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 ai-gateway and Rapid-MLX? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ai-gateway trust report](/tools/ferro-labs-ai-gateway/trust); [Rapid-MLX trust report](/tools/raullenchai-rapid-mlx/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ferro-labs-ai-gateway`](/api/graphcanon/graph?tool=ferro-labs-ai-gateway) - 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/_