Inference

Inference is what happens when you actually use an AI model. Training is the expensive, one-time process of teaching the model. Inference is the ongoing process of giving it inputs and getting outputs — every API call to Claude or GPT-4 is an inference request.

Inference costs depend on model size, input length, output length, and the infrastructure it runs on. Cloud-hosted inference (via APIs like Anthropic or OpenAI) is the simplest approach — you pay per token. Self-hosted inference requires significant GPU infrastructure but can be cheaper at scale. Edge inference runs smaller models directly on devices.

For AI agent systems, inference latency and cost are critical design factors. An agent that makes dozens of LLM calls per task needs fast, reliable inference. At Agentik {OS}, we optimize inference patterns — batching requests, caching repeated queries, choosing the right model size for each task (not every subtask needs the largest model), and parallelizing independent operations. This keeps our agents fast and cost-effective even when orchestrating complex multi-step workflows.