AI Gateway vs helicone
Side-by-side comparison of two AI agent tools
Short answer
- AI Gateway is growing faster: +323 GitHub stars in the last 30 days vs +132 for helicone.
- Pick AI Gateway for: a blazing fast AI Gateway with integrated guardrails. Pick helicone for: open source LLM observability platform.
From GitHub data refreshed daily.
AI Gatewayopen-source
A blazing fast AI Gateway with integrated guardrails. Route to 200+ LLMs, 50+ AI Guardrails with 1 fast & friendly API.
heliconeopen-source
🧊 Open source LLM observability platform. One line of code to monitor, evaluate, and experiment. YC W23 🍓
Metrics
| AI Gateway | helicone | |
|---|---|---|
| Stars | 13.1k | 6.2k |
| Star velocity /mo | 323.36842105263156 | 132.4736842105263 |
| Commits (90d) | 0 | 10 |
| Releases (6m) | 0 | 0 |
| Downloads (30d, npm + PyPI) | 1.9K | 1.3K |
| Overall score | 0.3390945209311889 | 0.4526682642617479 |
Pros
- +支持1600+模型的大规模路由能力，覆盖主流AI提供商
- +超高性能表现：<1ms延迟，122kb轻量级架构，日处理100亿tokens验证
- +企业级可靠性：自动重试、故障转移、负载均衡等生产环境必需功能
- +一行代码集成多个主流 AI 服务商，支持 OpenAI、Anthropic、Gemini 等
- +完整的可观测性套件，包含请求追踪、成本监控、延迟分析和质量评估
- +开源架构提供完全的数据控制权和自定义能力，无厂商锁定风险
Cons
- -企业级功能可能对简单用例过于复杂
- -文档信息不完整，可能存在学习曲线
- -高级配置和企业部署可能需要技术专业知识
- -相对较新的项目，生态系统和第三方集成可能不如成熟的商业解决方案完善
- -自部署需要一定的运维成本和技术能力
- -大规模使用时可能需要额外的性能优化和资源配置
Use Cases
- •多模型AI应用需要统一API接口和智能路由选择
- •企业级AI服务要求高可用性、自动故障转移和负载均衡
- •大规模AI应用需要成本优化和性能监控的网关层
- •AI Agent 系统的全链路监控和调试，追踪多步骤推理过程和工具调用
- •生产环境中的 LLM 成本控制和性能优化，实时监控 API 使用情况
- •多模型 A/B 测试和提示工程，比较不同模型和提示版本的效果
FAQ
- Which is more popular, AI Gateway or helicone?
- AI Gateway has more GitHub stars (13,121 vs 6,196).
- Which is more actively developed, AI Gateway or helicone?
- helicone had more commits in the last 90 days (10 vs 0).
- Should I use AI Gateway or helicone?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.