AI Gateway

TL;DR * AI cost optimization works best at the gateway layer because that is the only place where every model call can be priced, attributed to a team, and blocked before it reaches a provider. * Bifrost enforces budgets independently at the customer, team, virtual key, and provider-config levels, and

TL;DR * LLM cost is token-based and unpredictable: an unoptimized prompt chain can multiply spend 10x, and without real-time visibility teams find overruns only after the damage is done. * A cost monitoring tool should give per-key, per-team, and per-model attribution, budget enforcement before spend happens,

TL;DR * An AI gateway is a unified entry point between applications and multiple LLM providers, applying routing, failover, caching, governance, and observability through a single API. * It differs from a traditional API gateway because cost is token-based rather than request-based, and it needs model-aware failover, semantic

Compare the top AI gateways to reduce LLM cost and latency in production. See how Bifrost, Cloudflare, LiteLLM, Kong, and Vercel handle caching, routing, and governance. TL;DR * An AI gateway reduces LLM cost and latency by centralizing caching, routing, failover, budgets, and observability in one control plane, so no

TL;DR * Enterprise AI security is the set of controls that govern how an organization's applications, users, and agents send data to and receive data from language models. * LLM traffic is a distinct security surface: prompts can carry sensitive data out, responses can carry it back, and agents

TL;DR * GPT-5.6 (OpenAI, released July 9, 2026) leads on frontier reasoning, the hardest coding and agentic tasks, and math and science, scoring 80 on the Artificial Analysis Coding Agent Index and 89% on FrontierMath Tier 1-3. * Gemini 3.7 Flash (Google DeepMind) wins on cost per