What is LLM Inference? Costs, Latency, and Workflows

This dashboard demonstrates how a machine learning lead evaluated 31 models across six providers for a 300,000-record bulk text-structuring workload to understand exactly what is llm inference costing their team. By mapping blended batch costs against median latency on a log-scaled scatter plot, the team identified llama-3.1-8b as the frontier model with a lowest blended cost of $9.00 and fastest median latency of 180 ms. The visualization explicitly flagged 19 models with misleading pricing signals, enabling the engineering team to secure procurement sign-off based on a defensible cost-latency tradeoff.

What it shows:

Visualizing blended batch costs against median latency reveals the true operational expense of deploying language models at scale.

This CambioML dashboard provides a structural engineer with data-driven material inputs for finite element analysis by analyzing concrete mix-design variables and applying llm interpretability concepts to feature importance. An executive readout clarifies that curing age and water-to-binder ratio are the actual primary drivers of compressive strength, accounting for 62.4% of total importance across 1,030 observations. A horizontal bar chart quantitatively disproves the historical cement-centric assumption, while a smoothed LOWESS curve illustrates the non-linear relationship and explicitly identifies diminishing marginal lift where the taper begins at 355 kg/m³.

What it shows:

Applying feature importance rankings to engineering datasets can disprove historical assumptions and correct systematic modeling errors.