AI Gateway

TL;DR * AI endpoint security is the practice of governing and securing the AI applications, browser AI, and coding agents that run on employee machines, where most ungoverned "shadow AI" usage actually happens. * Traditional endpoint protection secures the device against malware; AI endpoint security controls what corporate data

TL;DR: Enterprises run LLMs from multiple providers across dozens of applications. Without a shared control point, every team reimplements routing, failover, rate limits, and security on its own.An enterprise AI gateway puts one layer in front of every model, so scaling and safety are handled once instead of

As enterprise LLM spending continues to surge, with 72% of organizations expecting their AI budgets to increase, the infrastructure layer between your application and model providers has become mission-critical. AI gateways solve a growing set of production challenges: fragmented provider APIs, unpredictable outages, runaway token costs, and zero visibility

TL;DR * Claude Code is a terminal-based coding agent from Anthropic that runs autonomous, multi-file changes from the command line; Cursor is an AI-native IDE built on a VS Code fork with inline editing, tab completion, and an agent mode. * Choose Claude Code for long-running refactors,

TL;DR * Bifrost is an open-source AI gateway written in Go that adds only 11 microseconds of overhead per request at 5,000 requests per second, and it runs entirely inside your own infrastructure. * Portkey ships an open-source gateway alongside a managed control plane; its deepest governance, analytics,

TL;DR * AI cost optimization is the practice of measuring, routing, caching, and capping LLM spend so a team pays for the output it needs instead of the tokens it wastes. * The highest-return tactics, in order, are: meter spend per workflow, route each task to the cheapest capable model,