What is AI TRiSM?
Learn what AI TRiSM (Trust, Risk, and Security Management) is, why it matters now, and how to implement it to ensure safe, explainable, and compliant AI systems at scale.
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Breaking down the real cost factors behind generative AI
Discover the true costs of implementing Generative AI beyond API charges
Why forward compatibility is critical for Agentic AI companies
Learn why forward compatibility is crucial for agentic AI companies seeking enterprise adoption. Discover how Portkey's AI Gateway helps organizations safely integrate new AI capabilities, test models in real-time, and manage resources—all without disrupting existing systems or breaking budgets
Securing your AI via AI Gateways
Learn how AI gateways like Portkey with security solutions like Pillar security help to protect against prompt injections, data leaks, and compliance risks in your AI infrastructure.
Bringing GenAI to the classroom
Discover how top universities like Harvard and Princeton are scaling GenAI access responsibly across campus and how Portkey is helping them manage cost, privacy, and model access through Internet2’s service evaluation program.
What is LLM tool calling, and how does it work?
Explore how LLM tool calling works, with real examples and common challenges. Learn how Portkey helps tool calling in production.
Geo-location based LLM routing: Why it matters and how to do it right
As LLM-powered applications scale across global markets, user expectations around performance, reliability, and data compliance are higher than ever.
Enterprises now prefer geo-location-based routing. Whether it's reducing latency, staying compliant with regional data laws, or optimizing infrastructure costs, geo-routing ensures your AI workloads are not just smart, but also efficient
Task-Based LLM Routing: Optimizing LLM Performance for the Right Job
Learn how task-based LLM routing improves performance, reduces costs, and scales your AI workloads
Beyond the Hype: The Enterprise AI Blueprint You Need Now (And Why Your AI Gateway is Non-Negotiable)
The Gen AI wave isn't just approaching—it's already crashed over every industry, leaving enterprises to navigate the aftermath. As a CTO or CIO, you've moved past the demos and proofs-of-concept. The questions keeping you up at night are now existential: How do we operationalize this technology at scale? How
Canary Testing for LLM Apps
Learn how to safely deploy LLM updates using canary testing - a phased rollout approach that lets you monitor real-world performance with a small user group before full deployment.
Ethical considerations and bias mitigation in AI
Discover how to address ethical issues through better data practices, algorithm adjustments, and system-wide governance to build AI that works fairly for everyone.
FinOps chargeback and how it can help GenAI platforms
Learn how FinOps chargeback helps AI teams control GenAI platform costs by linking expenses to specific teams.
Prompting Claude 3.5 vs 3.7
Claude models continue to evolve, and with the release of Claude 3.7 Sonnet, Anthropic has introduced several refinements over Claude 3.5 Sonnet. This comparison evaluates their differences across key aspects like accuracy, reasoning, creativity, and industry-specific applications to help users determine which model best fits their needs.
Claude
Benefits of using MCP over traditional integration methods
Discover how the Model Context Protocol (MCP) enhances AI integration by enabling real-time data access, reducing computational overhead, and improving security.