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openai.com·13h ago
Web Development
Vercel Blog·May 12·5 min read
AI Gateway production index
TL;DR
AI research and development often faces challenges related to cost and efficiency. The AI Gateway production index was created to measure spending and output in AI projects.
✦ Why It Matters
Engineers can use the AI Gateway production index to optimize their AI project budgets and improve output efficiency.
Key Takeaways
- Utilize multiple AI models to handle diverse workloads effectively.
- Monitor spending and token volume to inform model selection.
- Design routing architecture to allow for quick model switching.
- Plan for fallback mechanisms to ensure uptime and reliability.
Full Summary
Vercel's AI Gateway processes vast amounts of data, serving tens of trillions of tokens across various AI models. The analysis, based on seven months of production traffic from over 200,000 teams, shows that while Anthropic leads in spending, Google dominates in token volume.
For instance, in April 2026, Anthropic accounted for 61% of spending, while Google handled 38% of token traffic. The report emphasizes that different models cater to varying use cases, with high-stakes applications favoring premium models like Claude Opus, while low-stakes tasks utilize cheaper options like Gemini Flash.
This multi-model approach is becoming standard, as teams running over 10 million requests average 35 distinct models, allowing for flexibility and efficiency in production workloads.