--- title: "llm-axe vs ai-gateway" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/emirsahin1-llm-axe-vs-ferro-labs-ai-gateway" tools: ["emirsahin1-llm-axe", "ferro-labs-ai-gateway"] --- # llm-axe vs ai-gateway *GraphCanon updated Sep 20, 2026* ## Verdict Pick llm-axe if llm-axe is a Python-based toolkit aiming to facilitate quick applications development with local large language models, focusing on function-calling and compatibility with models like llama3; 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. [llm-axe](https://github.com/emirsahin1/llm-axe) reports 275 GitHub stars, 38 forks, and 0 open issues, last pushed Jan 5, 2025. [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 [llm-axe's repository](https://github.com/emirsahin1/llm-axe) and [ai-gateway's repository](https://github.com/ferro-labs/ai-gateway). | | [llm-axe](/tools/emirsahin1-llm-axe.md) | [ai-gateway](/tools/ferro-labs-ai-gateway.md) | | --- | --- | --- | | Tagline | Toolkit for quick implementation of LLM powered applications | Unified AI Gateway for multiple LLMs with caching, guardrails, A/B testing, and cost controls | | Stars | 275 | 256 | | Forks | 38 | 35 | | Open issues | 0 | 68 | | Language | Python | Go | | Adopt for | llm-axe is a Python-based toolkit aiming to facilitate quick applications development with local large language models, focusing on function-calling and compatibility with models like llama3. | 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 | Apache-2.0 - a permissive free software license | | Categories | LLM Frameworks, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-axe](/tools/emirsahin1-llm-axe.md) | [ai-gateway](/tools/ferro-labs-ai-gateway.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 622d | 2d | | Open issues (now) | 0 | 68 | | Stars delta | 0 (30d) | +37 (30d) | | Open issues delta | 0 (30d) | +5 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/emirsahin1-llm-axe/trust.md) | [trust report](/tools/ferro-labs-ai-gateway/trust.md) | ## Shared compatibility - **Python**: [llm-axe](/tools/emirsahin1-llm-axe.md) - Python runtime; [ai-gateway](/tools/ferro-labs-ai-gateway.md) - Python runtime ## Decision facts: llm-axe - **Adopt for:** llm-axe is a Python-based toolkit aiming to facilitate quick applications development with local large language models, focusing on function-calling and compatibility with models like llama3. ## 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 llm-axe if… - llm-axe is primarily Python; ai-gateway is Go. - License: llm-axe is MIT, ai-gateway is Apache-2.0. - Tags unique to llm-axe: function-calling, llama3, local-llm, ollama. - Also covers LLM Frameworks. - When you need to develop LLM-powered applications quickly using local models, emphasizing simplicity and ease of integration. ### Choose ai-gateway if… - ai-gateway is primarily Go; llm-axe is Python. - License: ai-gateway is Apache-2.0, llm-axe 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 llm-axe - Avoid if your project strictly requires cloud-based LLM resources or seamless model switching across different providers. - Not recommended for scenarios where extensive customization of the training pipeline is a requirement, as it focuses on implementation rather than deep training flexibility. ## 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 llm-axe and ai-gateway? llm-axe: Toolkit for quick implementation of LLM powered applications. 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 llm-axe over ai-gateway? Choose llm-axe over ai-gateway when llm-axe is primarily Python; ai-gateway is Go; License: llm-axe is MIT, ai-gateway is Apache-2.0; Tags unique to llm-axe: function-calling, llama3, local-llm, ollama; Also covers LLM Frameworks; When you need to develop LLM-powered applications quickly using local models, emphasizing simplicity and ease of integration. ### When should I choose ai-gateway over llm-axe? Choose ai-gateway over llm-axe when ai-gateway is primarily Go; llm-axe is Python; License: ai-gateway is Apache-2.0, llm-axe 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 llm-axe? Avoid if your project strictly requires cloud-based LLM resources or seamless model switching across different providers. Not recommended for scenarios where extensive customization of the training pipeline is a requirement, as it focuses on implementation rather than deep training flexibility. ### 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 llm-axe or ai-gateway more popular on GitHub? llm-axe has more GitHub stars (275 vs 256). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-axe and ai-gateway open source? Yes - both are open-source projects on GitHub (llm-axe: MIT, ai-gateway: Apache-2.0). ### Where can I find alternatives to llm-axe or ai-gateway? GraphCanon lists graph-backed alternatives at [llm-axe alternatives](/tools/emirsahin1-llm-axe/alternatives) and [ai-gateway alternatives](/tools/ferro-labs-ai-gateway/alternatives) ([llm-axe markdown twin](/tools/emirsahin1-llm-axe/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/emirsahin1-llm-axe-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, llm-axe or ai-gateway? llm-axe: Dormant. 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 llm-axe and ai-gateway? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-axe trust report](/tools/emirsahin1-llm-axe/trust); [ai-gateway trust report](/tools/ferro-labs-ai-gateway/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=emirsahin1-llm-axe`](/api/graphcanon/graph?tool=emirsahin1-llm-axe) - 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/_