Right-sized AI for real business impact, no matter your scale, stage, or stack.

Choose small LMs when you need low latency, low cost, and mostly “local” tasks (classification, short replies, basic assistants) that don’t require state‑of‑the‑art reasoning; they’re ideal for embedded features inside eCommerce apps. Use GPT‑4‑class models when quality, complex reasoning, or multilingual support is critical (high‑value support, complex merchandising, analytics copilots), and you can afford higher per‑call cost.​

Move to an enterprise or on‑prem/VPC LLM when you have strict data residency/privacy needs, heavy usage that makes SaaS token pricing uneconomical, or deep domain adaptation needs. This usually happens after one or two successful SaaS‑based pilots, when security and finance push for more control over data and spend.

Scope a pilot around one concrete workflow (e.g., agent assist, product copy, or analytics Q&A) in a single team, with clear success metrics and a 6–12 week build window. Architect it API‑first (LLM behind a service), log all interactions, and avoid provider‑specific lock‑in so you can later swap models, add tenants, or harden governance without rewriting everything.

Estimate cost by modeling requests × average tokens × price per 1K tokens per use case, then add infra/observability for self‑hosted or gateway layers. Control spend with smaller models where possible, context truncation, caching, routing low‑risk calls to cheaper models, setting per‑team budgets, and monitoring cost per task (e.g., per resolved ticket or generated page).​

Track KPIs tied to business outcomes, not just usage: for eCommerce, things like ticket handle time and FCR for support copilots, uplift in conversion/AOV for AI‑enhanced UX, content throughput per person, and internal time saved on reports or ops tasks. At the portfolio level, monitor payback period (implementation + run cost vs monthly benefit), model quality metrics (accuracy, CSAT, rejection rates), and safety metrics (escalations, policy violations).