AI Inference definition
AI inference is the stage where a trained machine learning model is used to make predictions or generate outputs on new data, such as classifying an image, scoring a transaction for fraud or answering a prompt. Unlike training, which happens occasionally, inference runs every time a user or system calls the model, so it drives ongoing cost and latency.
Inference vs training
Training is how a model learns: it processes large datasets, adjusts billions of parameters over many passes and can take days or weeks on clusters of GPUs. Inference is how a model is used: given an input, it runs a forward pass through the fixed parameters and produces an output. A model is trained once, or periodically, but may serve millions of inference requests every day.
That difference shapes the economics. For many AI products, especially those built on large language models, the cumulative cost of inference over a product's life far exceeds the cost of training or fine-tuning, so serving efficiency becomes a core engineering concern rather than an afterthought.
It also shapes where models run. Training needs data center GPUs, while inference can happen almost anywhere: in a cloud API, on your own servers, in a browser, on a phone or on a factory camera, depending on latency, privacy and cost requirements.
Real-time, batch and edge inference
Inference workloads fall into a few patterns, and each calls for different infrastructure, scaling rules and cost controls. Most production AI systems end up combining at least two of them for different features, such as live chat plus nightly scoring:
- Real-time (online) inference: a user waits for the answer, as in chatbots, search or fraud checks at checkout, so latency is critical
- Streaming inference: LLM responses sent token by token so users see output immediately
- Batch (offline) inference: large scheduled jobs, such as scoring every customer for churn overnight, optimized for throughput and cost
- Edge inference: models running on phones, browsers or devices for offline use, privacy or very low latency
What drives LLM inference cost and latency
For language models, cost scales with tokens: the input prompt, including retrieved context, and the generated output, where output tokens are usually pricier and slower because they are produced one at a time. Model size matters too, since larger models need more GPU memory and compute per token, and long conversations quietly multiply costs. Our LLM token estimator helps size prompts before you build.
Latency has two parts users notice: time to first token and the rate of tokens after that. GPU type, batching of concurrent requests, model size and network distance all affect both. See LLM tokens for how text becomes billable units and why some languages use more tokens than others.
How to optimize inference
Common techniques include choosing the smallest model that meets quality targets, routing easy requests to cheaper models, caching repeated prompts and responses, trimming retrieved context, quantization of self-hosted models, and serving engines such as vLLM, SGLang or TensorRT-LLM that batch requests and manage GPU memory efficiently. For classic ML models, ONNX Runtime or TensorFlow Lite speed up serving.
Measure before optimizing: track cost per request, latency percentiles and quality together, so savings do not silently degrade answers. Nexzem designs inference architecture during AI development projects, comparing hosted APIs and self-hosted models on cost, latency and data control.