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AICost Industry Solutions

Manufacturing and industrial

3 AI automations this industry runs today, each with the process, the AICost engine chain that prices and governs it, and the evidence behind it.

Cost engine Governance engine Each step priced and controlled. Click any engine to open its calculator or playground.
Predictive maintenance with technician copilotVisual quality inspectionIndustrial copilot for automation engineering and operations

Predictive maintenance with technician copilot

Owner: Maintenance and reliability engineering, plant operations

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Scale: WEF: more than 70% of industrial AI projects are abandoned after pilot (cited in Automation.com)

How the work runs

  1. Stream sensor data (vibration, temperature, torque)
  2. Detect anomalies and predict failure
  3. Auto-create the work order in the CMMS / EAM
  4. Technician asks the copilot for a troubleshooting procedure over manuals and history
  5. Close out with voice or text notes; feed back into models

AICost engine chain

5 cost engines 2 governance engines

Each step priced and controlled. Click any engine to open its calculator or playground.

Where cost leaks

  • High-frequency sensor data pushed to cloud LLMs instead of edge models
  • Re-indexing large manual libraries per plant
  • Pilots that never scale (sunk cost)

Where risk sits

  • Unsafe procedure advice (lockout steps skipped)
  • False negatives causing unplanned downtime
  • OT/IT security exposure from cloud connectivity

Rules that apply

  • OSHA lockout/tagout (29 CFR 1910.147) for procedures
  • EU Machinery Regulation 2023/1230
  • EU AI Act where AI is a safety component
  • export controls for defense manufacturers

Runs on

Siemens SenseyeSAP S/4HANA PM / SAP EAMIBM MaximoMaintainXAVEVA PIAWS Monitron

Evidence

automation.com ↗sap.com ↗

Visual quality inspection

Owner: Quality engineering, production line management

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Scale: Siemens Inspekto setup under an hour with about 20 'good' images (Automation.com, vendor-sourced)

How the work runs

  1. Capture images on the line
  2. AI classifies good or defect in real time
  3. Reject, rework or route to manual inspection
  4. Log defects and root cause (SPC)
  5. Retrain on new defect types

AICost engine chain

4 cost engines 1 governance engines

Each step priced and controlled. Click any engine to open its calculator or playground.

Where cost leaks

  • Sending every frame to cloud vision APIs instead of edge
  • Per-camera licensing across lines
  • Retraining for every product changeover

Where risk sits

  • Escaped defects reaching customers (recalls)
  • Unvalidated model changes in regulated lines
  • Drift with lighting or material changes

Rules that apply

  • ISO 9001 / IATF 16949 quality records
  • FDA 21 CFR 820 QMSR (medical device makers, effective Feb 2026) and software validation
  • EU AI Act where part of safety components

Runs on

Siemens InspektoCognexKeyenceLanding AIAWS Lookout for Vision (retired)SAP / Siemens Opcenter MES

Evidence

automation.com ↗blogs.nvidia.com ↗

Industrial copilot for automation engineering and operations

Owner: Automation / controls engineers, manufacturing engineering, shift supervisors

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How the work runs

  1. Engineer describes the required logic or change in natural language
  2. Copilot generates PLC code (SCL), HMI screens or test cases
  3. Simulate or virtually commission
  4. Engineer review and approval under change management
  5. Operations copilot answers alarm and shift questions

AICost engine chain

4 cost engines 3 governance engines

Each step priced and controlled. Click any engine to open its calculator or playground.

Where cost leaks

  • Large project files and documentation in context per prompt
  • Seat licenses across engineering teams with uneven use
  • Iterative generate-simulate loops

Where risk sits

  • Unsafe control logic reaching production
  • IP and source code leakage
  • Weak change control and traceability

Rules that apply

  • IEC 61508 / IEC 62061 functional safety
  • IEC 62443 OT cybersecurity
  • EU Machinery Regulation 2023/1230
  • EU Cyber Resilience Act

Runs on

Siemens Industrial Copilot (TIA Portal)Rockwell FactoryTalk Design Studio CopilotMicrosoft Azure OpenAISAP Joule

Evidence

iiot-world.com ↗automation.com ↗

Reference chains are a starting point. Your volumes, models and vendors change the numbers; the engines recompute them from live prices.

Your process is not on the list, or runs differently? These chains are a starting point. We assemble the right engines for your workflow, your models and your vendors, and you run it over MCP or in the calculators.

Business solutions → Build my chain
📖 Data sources & methodology 172 text models · 9 embeddings · 44 vision · 62 audio · 8 vector DBs across 10 vendor pages · last verified 2026-10-15

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-10-15.
  • Vendor-published values have no mark. Inferred/extrapolated values are marked with * and listed below.
  • Batch API discounts are 50% off standard rates across providers that offer Batch mode.
  • Prompt caching discounts vary by provider (typically 80-90% off cached input tokens).
  • Regional data-residency surcharges (Anthropic 1.1x, OpenAI 1.1x, Google regional tiers) are NOT included in base rates.
  • Long-context pricing tiers apply when input exceeds model threshold.
  • Embedding prices are input-only (no output tokens generated).

Primary sources

Last-verified date is the most recent successful daily snapshot (aicost_pricing_snapshots) or, when no snapshot exists yet, the latest successful crawler run (aicost_crawler_runs). 10 of 10 vendors are currently verified. Aggregator services (TokenCost, AI Pricing Guru, etc.) are not listed.

Anthropic
2026-10-14
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 700 days captured
Anthropic Docs
2026-10-14
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 700 days captured
OpenAI
2026-10-14
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 701 days captured
Google AI
2026-10-14
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 676 days captured
Google Vertex
2026-10-14
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 676 days captured
DeepSeek
2026-10-14
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 615 days captured
xAI
2026-10-14
https://x.ai/api
Daily snapshot since Nov 2024 · 533 days captured
Mistral
2026-10-14
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 674 days captured
Cohere
2026-10-14
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 700 days captured
Voyage AI
2026-10-15
https://docs.voyageai.com/docs/pricing

Inferred values (marked with * in calculator tables)

Derived from industry conventions, not directly published by the vendor. Typical conventions: cached input = 10% of base (90% off), Batch API = 50% of base (50% off).

Vendor / Model Field Why it’s inferred
Anthropic — Claude Sonnet 4.6 cachedInput Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier.
Anthropic — Claude Sonnet 4.5 cachedInput Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6.
Anthropic — Claude Sonnet 4.5 batchInput Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Sonnet 4.5 batchOutput Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Haiku 4.5 cachedInput Derived at 10% of input rate — Anthropic 90% cache-hit discount convention.
OpenAI — GPT-5.4 Mini cachedInput Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier.
OpenAI — GPT-5.4 Nano cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Nano batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Nano batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Pro batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.2 cachedInput Derived at 10% of input; no residency uplift.
OpenAI — GPT-5.2 batchInput Derived at 50% of input.
OpenAI — GPT-5.2 batchOutput Derived at 50% of output.
OpenAI — GPT-5 cachedInput Derived at 10% of input.
OpenAI — GPT-5 batchInput Derived at 50% of input.
OpenAI — GPT-5 batchOutput Derived at 50% of output.
OpenAI — GPT-5.5 Pro cachedInput Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention.
OpenAI — GPT-5.5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.2 Pro cachedInput Derived at 10% of input — pro-tier convention.
OpenAI — GPT-5.2 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.2 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.1 batchInput Derived at 50% of input.
OpenAI — GPT-5.1 batchOutput Derived at 50% of output.
OpenAI — GPT-5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5 Nano cachedInput Derived at 10% of input.
OpenAI — GPT-5 Nano batchInput Derived at 50% of input.
OpenAI — GPT-5 Nano batchOutput Derived at 50% of output.
Google — Gemini 3 Flash cachedInput Derived at 10% of input — Google caching discount convention ~90%.
Google — Gemini 3.1 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 3.1 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 3.1 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Pro cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.5 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
xAI — Grok 4 (legacy) cachedInput Extrapolated at 25% of base.

Pricing is cross-verified against the LiteLLM community registry when available. Daily snapshots are kept in aicost_pricing_snapshots; every change is logged to aicost_price_changelog with old & new values for full audit trail. Read the full methodology →