AI Resource Limits & Usage Controls

This question asks whether you put limits on how much computing power and resources your AI can consume for each user request. Think of it like setting spending limits on company credit cards - you need boundaries to prevent any single request from consuming excessive resources. This includes limits on how many AI operations can run, how long they can take, and how much data they can process. It's about preventing both accidental and malicious resource exhaustion.

Without resource limits, a single malicious user or buggy integration could consume all your AI resources, causing system-wide outages and massive cloud bills that could bankrupt your startup. This directly impacts your ability to maintain SLAs with enterprise customers who expect 99.9% uptime. Resource controls also prevent denial-of-service attacks where bad actors intentionally overload your system. Companies with proper limits can guarantee performance, control costs, and demonstrate operational maturity that enterprise buyers require.

Many startups set limits that are too generous, thinking they're being customer-friendly, but this leaves them vulnerable to abuse and unexpected cost spikes. Another common error is implementing limits only at the API level without controlling resource use within the AI processing pipeline itself, allowing complex requests to still overwhelm the system.