{"uid":"cap_rGXDpvGKrADeB4STQL2VG","slug":"always-on-local-model-vs-paid-inference-selection-procedure-afe8fe65","name":"Always-On Local Model vs Paid Inference Selection Procedure","description":"Deterministic procedure for an agent deciding between an always-on local model and paid hosted inference. Steps: profile the workload (interactive latency floor, batchable background tasks, data sensitivity, request volume), verify local feasibility (RAM/VRAM budget vs model size and quantization, tokens/sec needed vs achievable), compute hosted per-call cost at the observed request rate, find the amortized cost crossing point where monthly local cost beats per-call hosted spend, and output a","url":"https://k2so.wrong.systems/api/services/always-on-local-agent-model-selection-and-infere?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method"],"properties":{"type":{"type":"string","const":"http"},"method":{"enum":["GET","HEAD","DELETE"],"type":"string"},"queryParams":{"type":"object","properties":{"meta":{"enum":["0","1"],"type":"string","description":"Set to 1 for free metadata JSON (no payment 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a deterministic decision procedure helping agents choose between running a local always-on model and using paid hosted inference, based on workload profile, hardware feasibility, and cost crossover analysis.","exampleAgentPrompt":"Help me figure out whether I should run a local LLM on my own hardware or pay for hosted inference — I'm getting around 500 requests per day, have 24GB VRAM available, need interactive response times, and my data is sensitive enough that I'd prefer to keep it on-prem if cost-justified.","exampleUseCases":[{"title":"Privacy-first agent infrastructure decision","prompt":"I'm building an agent that processes confidential legal documents and I'm wondering if I should run a local model or use a hosted API — can you walk me through the decision procedure given my data sensitivity requirements and roughly 200 requests per day?"},{"title":"Cost break-even for high-volume batch workload","prompt":"We're running about 10,000 background summarization tasks per day and spending a lot on hosted inference — can you run the local vs hosted cost crossover analysis to see if buying dedicated hardware makes financial sense?"},{"title":"Startup GPU budget vs API spend tradeoff","prompt":"Our startup is deciding whether to buy a GPU server or keep paying for API calls — we have around 1,000 interactive requests per day, need under 2 seconds latency, and want to know the amortized monthly cost crossover point."}],"resultDescription":"A structured response containing decision procedure prose (brief), the recommended approach, cost crossover analysis, the topic evaluated, an ISO-8601 timestamp, and the generation source (llm, reasoning, or deterministic). Also includes payment metadata confirming the paid call was processed.","failureModes":["Missing required input fields returns an error","Invalid method type causes schema validation failure","Payment not settled results in 402 response without procedure content","High load or provider unavailability may return 5xx","Topic override not recognized may fall back to default procedure"],"whenToPreferThis":"Use this endpoint when an agent or operator needs a structured, deterministic framework for deciding between local model hosting and paid hosted inference — especially when cost, latency, data sensitivity, and hardware constraints all need to be weighed together. Prefer this over ad-hoc reasoning when you want a reproducible, auditable decision procedure rather than one-off estimates.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-21T07:41:18.157Z","isFirstParty":false,"canonicalSlug":"always-on-local-model-vs-paid-inference-selection-procedure-afe8fe65"}