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Find Your Volume Breakpoint
Before choosing a model, estimate monthly calls, average tokens per call, and acceptable latency. If the workflow runs a few hundred times a month, a hosted frontier API is usually worth the convenience. If it runs constantly, such as support triage, document processing, enrichment, or internal search, open weights deserve a cost test.
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Split Workloads by Risk and Repetition
Do not move everything to self-hosted AI just because the model is free. Keep rare, high-stakes, reasoning-heavy work on the best managed model you can afford. Move repetitive, bounded tasks to smaller open models once you can define the input, output, tests, and fallback path.
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Use Local Models for Private Draft Work
Small open models are useful when the job is close to the user and does not need frontier reasoning. Think first-pass email cleanup, note tagging, browser-side classification, template filling, or extracting fields from predictable documents. These workflows can cut API cost and latency while keeping sensitive data off third-party APIs.
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Do a Two-Week Bakeoff
Pick one expensive recurring AI task and run it through your current API model and one open-weight option. Compare total cost, latency, accuracy, setup time, maintenance burden, and failure cases. The winner is not the model with the best benchmark score, it is the one that meets the workflow's quality bar at the lowest operational cost.
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Beware Free Model Math
A free model still needs hosting, monitoring, versioning, security, and someone responsible for breakage. For a small business, those hidden costs can outweigh token savings until usage is high enough. Treat self-hosting as an operations decision, not a download button.
Why it matters
Small businesses waste money when they treat every AI task as a frontier-model task. The practical move is to segment work by volume, risk, privacy, and maintenance cost before picking tools. That keeps you fast today while leaving a clear path to cheaper infrastructure when usage grows.