model-selection

To choose between API and local deployment, you must weigh cost, latency, privacy, and capability constraints. Structured decision workflows help compare hosted models against on-device inference based on your specific data sensitivity and hardware environment.

The best way to compare LLM provider and model tiers is using a structured decision workflow that evaluates capability against cost and latency. Capturing constraints and tradeoffs in a structured log ensures reproducible deployments from prototyping to production.

You balance cost and latency by capturing hardware constraints, deployment environment, and data sensitivity as explicit inputs. This allows teams to systematically evaluate deployment paths and record the resulting tradeoffs for reproducible configuration.

You need to record LLM configuration and prompt engineering tradeoffs throughout prototyping to production to support reproducible deployments. Keeping a structured decision log of artifacts and evaluations maintains consistency across different model tiers and deployment paths.

Data sensitivity directly impacts API versus local LLM inference decisions by acting as an explicit input constraint. High privacy requirements often favor local deployment, while balancing this against available hardware and required model capability dictates the final path.

You can use this to guide prompt engineering considerations alongside deployment path and model selection. It helps weigh configuration tradeoffs and capture evaluations, ensuring your team records the optimal approach between fine-tuning and prompt adjustments in a decision log.