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AICost Business Solutions

Sales (SDR/BDR, sales ops, RevOps)

3 AI automations this team 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 SDR for outbound prospecting and inbound lead qualificationCall notes, CRM auto-update and deal risk alertsRFP and security questionnaire answering

AI SDR for outbound prospecting and inbound lead qualification

Owner: Sales development lead / RevOps

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Scale: About 3,000 emails/month from one AI SDR program vs 75-285/month per human rep (SaaStr, self-reported)

How the work runs

  1. RevOps loads a target list or segment (lapsed customers, past attendees, cold accounts)
  2. Agent researches each account and contact
  3. Agent writes and sends personalized email sequences, or chats with website visitors
  4. Agent handles replies and books meetings on rep calendars
  5. Hot or complex replies go to a human rep
  6. Results reviewed weekly and messaging tuned

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

  • Research step re-run on every contact on every send
  • Platform licences of roughly $50k-100k+ per year each, with overlap across vendors (SaaStr)
  • 15-20 hours/week of human management that is not budgeted (SaaStr)

Where risk sits

  • Same contact hit by several agents or wrong claims made about the product
  • Email deliverability, CAN-SPAM/GDPR consent and domain reputation damage
  • Agent quoting prices or terms it is not allowed to offer

Runs on

  • Salesforce
  • HubSpot
  • Outreach
  • Salesloft
  • Apollo
  • ZoomInfo
  • Artisan

Evidence

saastr.com ↗businesswire.com ↗

Call notes, CRM auto-update and deal risk alerts

Owner: Sales managers, sales ops / RevOps

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How the work runs

  1. Rep runs a customer call or meeting
  2. AI records, transcribes and summarizes next steps
  3. AI fills CRM fields (stage, contacts, MEDDICC items) for the rep to confirm
  4. AI flags deals at risk and drafts the follow-up email
  5. Manager reviews pipeline and forecast with AI commentary

AICost engine chain

6 cost engines 2 governance engines

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

Where cost leaks

  • Every internal and external meeting transcribed and summarized, not just deal calls
  • Per-seat conversation intelligence licences for every rep plus add-ons
  • Re-summarizing the full deal history each time a forecast is refreshed

Where risk sits

  • Recording consent and customer confidential information stored in third-party tools
  • Wrong CRM data driving the forecast and comp
  • Customer data used to train vendor models without contract cover

Runs on

  • Gong
  • Salesforce
  • HubSpot
  • Clari
  • Zoom
  • Microsoft Teams
  • Outreach

Evidence

venturebeat.com ↗pipeline.zoominfo.com ↗

RFP and security questionnaire answering

Owner: Sales engineering / presales, proposal team

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How the work runs

  1. RFP or security questionnaire arrives
  2. AI splits it into questions and assigns owners
  3. AI drafts answers from the approved answer library and past bids
  4. Subject-matter experts review and edit low-confidence answers
  5. Final response exported and approved answers saved back to the library

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

  • Very long documents processed in full with large context on every redraft
  • Re-generating whole sections after small edits
  • Separate tools for RFP and security questionnaires with overlapping licences

Where risk sits

  • Outdated or invented security and compliance claims becoming contractual
  • Confidential details from one customer's bid reused in another
  • Overstated product capabilities

Runs on

  • Loopio
  • Responsive
  • SafeBase
  • Salesforce
  • SharePoint
  • Google Drive

Evidence

presalescollective.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.

Industry 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 →