Inference

Think of inference like the difference between a student studying for an exam and actually taking the test. During training, an AI model learns patterns from data. During inference, it applies those patterns to answer new questions it's never seen before.

Here's a marketing analogy: You train a salesperson on your product features and customer objections (training). When that salesperson talks to a prospect on a call, they're doing inference—applying what they learned to a specific, unpredictable situation.

In marketing tools, inference happens constantly. When you use an AI chatbot to answer customer questions, that's inference. When a recommendation engine suggests products to a website visitor, that's inference. When an email subject line generator creates copy for your campaign, that's inference. The model has already been trained; now it's performing.

Why this matters for your budget and operations: inference speed and cost directly impact your tool's usability. Fast inference means real-time personalization on your website. Slow inference means customers wait for recommendations. Expensive inference means your AI tool costs more per interaction, which affects your ROI on that platform. When evaluating AI vendors, ask about their inference latency (how fast) and inference cost (price per prediction). A tool with great accuracy but slow inference will frustrate your team and customers.