Explore evidence-led notes on where AI should run, what agents need after the demonstration, and how technical mechanisms change workflow economics. Claims are bounded to their workload and evidence.
Compare managed API, self-hosted, and hybrid AI in currency per accepted completed workflow, not token price alone. This transparent ledger incorporates acceptance, retries, exceptions, human review, latency, caching, idle capacity, operating labor, recovery, and demand sensitivity while keeping savings, payback, and business value as unproven hypotheses.
Read MoreUse a benchmark card to decide when measurements from a private 27B language-model environment are complete, comparable, reproducible, and relevant to architecture. The method records workload, hardware, model representation, cache state, concurrency, acceptance, units, matched baselines, raw outputs, and failures without publishing unsupported performance claims.
Read MoreUse this nine-row workload-placement matrix to decide when one enterprise AI workload belongs on a managed API, self-hosted infrastructure, or a deliberately bounded hybrid path. It turns data, capability, latency, demand, operations, exit cost, and control evidence into hard gates instead of a single weighted score.
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