Why every AI Engineer needs an AI Gateway (ft Portkey.ai CEO)

we think you should use gateways.

Takeaways

AI gateways serve as operational platforms for connecting to LLMs efficiently.
Routing capabilities are increasingly important for complex AI systems.
Observability allows teams to monitor performance and accuracy effectively.
Guardrails are essential for preventing inaccurate or harmful AI responses.
Human feedback is crucial for improving AI applications but is often underutilized.
MCP simplifies the integration of various services with LLMs.
AI gateways can help manage costs and performance in AI applications.
The future of AI will require more operational efficiency tools.
Best practices for tracing and logging are needed in AI systems.
The adoption of AI gateways is expected to grow as teams move into production.



Chapters

00:00 Introduction to AI Gateways
01:15 Understanding AI Gateways and Their Importance
03:03 Routing and Complex Systems in AI
06:14 The Role of Observability in AI Gateways
10:42 Guardrails: Ensuring Safe AI Interactions
12:26 Practical Applications of Guardrails
15:20 The Future of Observability in AI
19:44 Agentic Logging and Its Challenges
22:29 Human Feedback in AI Systems
25:57 Best Practices for Tracing and Logging
29:18 Future Trends in AI Gateways and MCP


  / 1rohitagarwal  

Keywords

AI gateways, routing, observability, guardrails, LLMs, agentic systems, human feedback, tracing, logging, MCP