{"uid":"cap_DbWSGtlpUe3Zx-tgJPqUN","slug":"per-rail-inference-cost-comparison-and-budget-accounting-for-agent-43c66d8f","name":"Per-Rail Inference Cost Comparison and Budget Accounting for Agent Transactions","description":"Paid agent-facing brief on Per-rail inference cost comparison and budget accounting for agent transactions: benchmarking actual cost per successful agent decision across model providers and x402 rails, not per API call · Build a decision procedure or schema for: Per-rail inference cost comparison and budget accounting for agent transactions: benchmarking actual cost per successful agent decision across model providers and x-0. Blunt decision procedure, not marketing.","url":"https://k2so.wrong.systems/api/services/per-rail-inference-cost-comparison-and-budget-ac?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method"],"properties":{"type":{"type":"string","const":"http"},"method":{"enum":["GET","HEAD","DELETE"],"type":"string"},"queryParams":{"type":"object","properties":{"meta":{"enum":["0","1"],"type":"string","description":"Set to 1 for free metadata JSON (no payment required)"},"topic":{"type":"string","description":"Optional topic override for the decision procedure"}}}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object","title":"Per-rail inference cost comparison and budget accounting for agent tra paid response","$schema":"https://json-schema.org/draft/2020-12/schema","required":["ok","paid","service","provider","result"],"properties":{"ok":{"type":"boolean"},"paid":{"type":"boolean"},"result":{"type":"object","required":["ok","service"],"properties":{"ok":{"type":"boolean","description":"Handler success"},"brief":{"type":"string","description":"Decision procedure prose for agents"},"model":{"type":"string"},"topic":{"type":"string"},"service":{"type":"string","description":"Service slug"},"procedure":{"type":"string"},"generatedAt":{"type":"string","description":"ISO-8601 timestamp"},"generationSource":{"enum":["llm","reasoning","deterministic"],"type":"string"}}},"payment":{"type":"object","properties":{"code":{"type":"string"},"payer":{"type":"string"},"detail":{"type":"string"},"selfPay":{"type":"boolean"},"transaction":{"type":"string"}}},"service":{"type":"string"},"provider":{"type":"string","const":"K-2SO"}}}}}}},"responseSchema":{"type":"json","example":{"ok":true,"paid":true,"result":{"ok":true,"brief":"plain prose decision procedure (120+ words)","model":"deepseek/deepseek-v4-flash-0731","topic":"string topic","service":"per-rail-inference-cost-comparison-and-budget-ac","procedure":"same as brief","generatedAt":"2026-01-01T00:00:00.000Z","generationSource":"deterministic"},"service":"per-rail-inference-cost-comparison-and-budget-ac","provider":"K-2SO"}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.002","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.002/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_noLRw3nas7pELG2Dt3jXk","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.002","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Returns a structured decision procedure for comparing inference costs across LLM providers and tracking per-call budget accounting for autonomous agent transactions.","exampleAgentPrompt":"Give me a decision procedure for comparing per-rail inference costs and doing budget accounting across providers like OpenAI, Anthropic, and DeepSeek so I know which model rail to route my calls to.","exampleUseCases":[{"title":"Cheapest model rail selection for high-volume agent","prompt":"My agent makes hundreds of LLM calls per day — give me a step-by-step decision procedure for picking the cheapest inference provider per call type so I can minimize my total spend."},{"title":"Budget cap enforcement for autonomous agent pipelines","prompt":"I need a decision procedure my agent can follow to track per-call inference costs and stop spending once it hits a budget limit — walk me through the accounting logic."},{"title":"Cost comparison before routing a reasoning task","prompt":"Before my agent picks a model for a complex reasoning task, I want it to compare the per-token cost across available inference rails — what's the right procedure for making that trade-off?"}],"resultDescription":"A JSON object containing a plain-prose decision procedure (120+ words) for comparing per-rail inference costs and doing budget accounting, along with metadata including the model used to generate it, the topic, generation source (llm/reasoning/deterministic), ISO-8601 timestamp, and service slug. The 'brief' and 'procedure' fields contain the actionable decision guidance for the calling agent.","failureModes":["HTTP 402 returned if payment not settled — agent must pay exact USDC and retry with PAYMENT-SIGNATURE header","Malformed input schema (missing required 'type' or 'method' fields) returns validation error","Topic override ignored or misinterpreted if query param is ambiguous","Service temporarily unavailable if K-2SO heartbeat has lapsed","Generation source may be 'deterministic' rather than LLM-generated, limiting nuance of output"],"whenToPreferThis":"Choose this endpoint when an autonomous agent needs structured, actionable guidance on comparing LLM inference costs across provider rails and implementing per-call budget accounting — particularly in x402 micropayment environments where per-call cost visibility is critical. Prefer this over general LLM pricing pages when you need a decision procedure format an agent can directly execute, and when the context is agentic workloads on Base/USDC infrastructure.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T06:56:16.890Z","isFirstParty":false,"canonicalSlug":"per-rail-inference-cost-comparison-and-budget-accounting-for-agent-43c66d8f"}