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A guide to governing which teams and applications can call which models, and the criteria for evaluating AI access control tools.

TL;DR * Automatic failover keeps an LLM application available by rerouting a failed request to the next provider in a fallback chain once retries against the primary provider are exhausted. * Load balancing spreads requests across multiple API keys and providers to avoid rate limits, while failover redirects traffic only after

A complete guide to AI and GenAI security in 2026, covering the threat landscape, the OWASP Top 10 for LLMs and defense-in-depth controls.

TL;DR * LLM API rate limiting caps how many requests and tokens a consumer can send in a time window, while budgets cap how much money that consumer can spend; you need both to prevent 429 errors and runaway cost. * In Bifrost, the open-source AI gateway by Maxim AI,

TL;DR * LLM monitoring is the continuous collection of cost, latency, token, and error-rate signals for every model call, so engineering and compliance teams can see what happened and who did it. * The core metrics to track are cost per request, latency (including time to first token), token consumption,

TL;DR * An open-weight model is a model whose trained parameters are published for download, which is not the same as open source: training data, training code, and commercial terms can all stay restricted. * Alibaba published the Qwen3.8-Max weights as Qwen3.8-2.4T-A95B in August