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AI Socratic

Practical AI

Open-model deployment

Compare an open-model deployment with a hosted API using the same workload and acceptance criteria. Include engineering time, idle capacity, reliability, and licensing alongside inference cost. The cheapest token price is useful only when the system meets the required quality and service level.

AI Socratic editorial resources · Methods and worksheets, not measured benchmark results. Editorial policy

Specify the workload

Create a representative set of requests with known acceptance criteria. Record input and output length distributions, expected concurrency, peak traffic, context requirements, data handling constraints, and the latency users can tolerate. Keep sensitive production examples out of public evaluation datasets.

Freeze the comparison configuration

For self-hosting, record model revision, license, quantization, serving software, accelerator type, memory, and replica count. For an API, record the model identifier, region where known, request settings, rate limits, and the date of the price quotation. Compare identical tasks and disclose configurations that cannot be held constant.

Measure service quality

Track accepted results, time to first token, completion latency, throughput, errors, and cold starts under expected load. Report medians and tail latency with sample counts. Test peak demand and degraded service rather than extrapolating from a single request on an idle machine.

Include the operating costs

Add compute, storage, network, observability, engineering and on-call effort. Divide total cost by accepted tasks, keeping failed requests and retries in the numerator. Show the traffic level and utilization assumed; a deployment that is economical at steady load may be expensive when mostly idle. Check license terms for the intended use before committing.

Download the deployment comparison (CSV)

Primary references

Related coverage

Published community reporting and analysis. Check each article’s sources and publication date.

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