MCP
Building AI agent workflows with the help of an MCP gateway
Discover how an MCP gateway simplifies agentic AI workflows by unifying frameworks, models, and tools, with built-in security, observability, and enterprise-ready infrastructure.
Drishti Shah
MCP
Using an MCP (Model Context Protocol) gateway to unify context across multi-step LLM workflows
Learn how an MCP gateway can solve security, observability, and integration challenges in multi-step LLM workflows, and why it’s essential for scaling MCP in production.
LLM
How to implement budget limits and alerts in LLM applications
Learn how to implement budget limits and alerts in LLM applications to control costs, enforce usage boundaries, and build a scalable LLMOps strategy.
Azure
Build resilient Azure AI applications with an AI Gateway
Learn how to make your Azure AI applications production-ready by adding resilience with an AI Gateway. Handle fallbacks, retries, routing, and caching using Portkey.
observability
Using metadata for better LLM observability and debugging
Learn how metadata can improve LLM observability, speed up debugging, and help you track, filter, and analyze every AI request with precision.
LLM
What is AI interoperability, and why does it matter in the age of LLMs
Learn what AI interoperability means, why it's critical in the age of LLMs, and how to build a flexible, multi-model AI stack that avoids lock-in and scales with change.
Azure
How to scale GenAI apps built on Azure AI services
Discover how to scale genAI applications built on Microsoft Azure. Learn practical strategies for managing costs, handling prompt engineering, and scaling your AI solutions in enterprise environments.
MCP
MCP vs A2A
Explore the differences between MCP (and A2A, how they address distinct challenges in AI systems, and why combining them could power the next generation of intelligent, interoperable agents.
LLMOps
How LLM tracing helps you debug and optimize GenAI apps
Learn how LLM tracing helps you debug and optimize AI workflows, and discover best practices to implement it effectively using tools like Portkey.
Cost reduction
LLM cost attribution: Tracking and optimizing spend for GenAI apps
Learn how to track and optimize LLM costs across teams and use cases. This blog covers challenges, best practices, and how LLMOps platforms like Portkey enable cost attribution at scale.
The hidden technical debt in LLM apps
Discover where hidden technical debt builds up in LLM apps—from prompts to pipelines—and how LLMOps practices can help you scale GenAI systems without breaking them.
LLMOps
Scaling and managing LLM applications: The essential guide to LLMOps tools
Learn how to scale your AI applications with proven LLMOps strategies. This practical guide covers observability, cost management, prompt versioning, and infrastructure design—everything engineering teams need to build reliable LLM systems.