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AICost Industry Solutions

Clinics and physician practices (ambulatory, dental, specialty)

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.
AI scribe for independent and specialty practicesAI front desk: phone, scheduling, intake and eligibilityDental radiograph AI and treatment-plan acceptance

AI scribe for independent and specialty practices

Owner: Practice owner / medical director, practice administrator; used by physicians, NPs, PAs

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Scale: Outpatient providers' adoption of specialized AI tools about 18% (Menlo Ventures 2025)

How the work runs

  1. Patient consent at check-in
  2. Record the visit (in person or telehealth)
  3. Generate the SOAP note and patient summary
  4. Provider reviews and signs; push to the EHR by integration or copy-paste
  5. Code suggestion and charge capture

AICost engine chain

6 cost engines 3 governance engines

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

Where cost leaks

  • Per-provider subscriptions that keep running for part-time or inactive providers
  • Stacking a scribe on top of an EHR that already bundles ambient
  • Usage-priced tools billed per audio minute with long visits

Where risk sits

  • Consumer-grade scribes used without a BAA
  • Unreviewed notes signed in bulk
  • No consent record for the recording

Rules that apply

  • HIPAA (BAA required with the scribe vendor)
  • state recording consent laws
  • California AB 3030 for patient-facing summaries

Runs on

athenahealth (athenaOne ambient)eClinicalWorksNextGenModMedFreedHeidiNablaSuki

Evidence

intuitionlabs.ai ↗physicianspractice.com ↗

AI front desk: phone, scheduling, intake and eligibility

Owner: Practice manager, front office / patient access staff

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Scale: Patient engagement and call-center AI is a $100M+ market growing about 20x year over year (Menlo Ventures 2025)

How the work runs

  1. AI voice agent answers inbound calls after hours or on overflow
  2. Identify the patient and intent (schedule, refill, billing, directions)
  3. Book or reschedule in the PM system; send reminders and intake forms
  4. Verify insurance eligibility
  5. Hand off to staff with a summary when needed

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

  • Per-minute voice costs (STT, LLM and TTS stacked) on long hold or loop calls
  • Bots that fail to resolve, so staff handle the call anyway (double cost)
  • Outbound reminder campaigns at scale

Where risk sits

  • Clinical triage questions answered by a scheduling bot
  • Identity verification failures exposing PHI
  • Undisclosed AI voice

Rules that apply

  • HIPAA
  • TCPA for outbound AI voice calls (FCC 2024 ruling that AI-generated voices are 'artificial')
  • state AI disclosure rules
  • California AB 3030 if clinical information is conveyed

Runs on

athenahealtheClinicalWorksNextGenDentrix / Open Dental / Eaglesoft (dental)WeaveHello PatientAssort HealthTwilio

Evidence

hellopatient.com ↗healthcare.digital ↗

Dental radiograph AI and treatment-plan acceptance

Owner: DSO clinical leadership, dentists and hygienists; DSO revenue cycle for claims attachments

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Scale: Smile Brands rolling Pearl out to about 600 locations (Becker's Dental)

How the work runs

  1. Capture bitewings, periapicals and panoramic images
  2. AI flags caries, calculus, bone loss and margins in real time
  3. Chairside review with the patient using annotated images
  4. Build the treatment plan in the PMS
  5. Attach AI-annotated images to insurance claims; payer-side review

AICost engine chain

4 cost engines 2 governance engines

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

Where cost leaks

  • Per-location, per-image licensing across hundreds of offices
  • Duplicate AI on both provider and payer side for the same claim
  • Integration and IT cost per imaging system

Where risk sits

  • Overtreatment driven by false positives (treatment acceptance used as a KPI)
  • Use outside FDA-cleared indications
  • Payer AI denials based on images

Rules that apply

  • FDA 510(k) clearance for radiograph detection devices
  • HIPAA
  • state dental board standard-of-care rules

Runs on

Pearl (Second Opinion)OverjetVideaHealthDentrixOpen DentalEaglesoftCurve

Evidence

beckersdental.com ↗DrBicuspid.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 →