27 questions to ask when choosing an LLM
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A practical checklist of 27 questions developers should ask before adopting an LLM. Topics span model size and hardware fit, performance metrics (TTFT, rate limits, context window), reasoning vs. speed tradeoffs, training data concerns (cutoff, synthetic data, copyright, provenance), cost, fine-tuning options, multimodal support, compliance and data residency, environmental impact, tool use, agentic capabilities, and model quirks. The goal is to help teams match the right model to their specific use case rather than defaulting to the most popular option.
Table of contents
Does the model fit in your hardware?What is the time to first token?Are there rate limits?Sort: