AICost Business Solutions

The AI automations every team is running in 2026, with the engines that keep them in budget and in bounds

Pick a business function. Each automation shows the process your people already know, then the chain of AICost engines that prices every step and the governance controls that keep it safe. 124 engines, all available over MCP.

30reference automations
10business functions
42engines in these chains
124engines to build your own
1Find your process

Start from the work your team already does: the ticket, the invoice, the claim, the visit note.

2See the engine chain

Each step maps to an AICost engine that prices it and a control that keeps it safe: eval, human review, guardrails, budgets.

3Make it yours

Run the same chain over MCP from Claude, Codex or your app, or open each calculator. We help you adapt it to your stack.

Top 10 AI automations in 2026

Ranked from public spend, adoption and scale data. Many figures are vendor-published, so treat them as directional.

01 AI coding assistant and ticket-to-pull-request coding agent 🛠️ Engineering and DevOps

Largest AI spend category: 55% of 2025 departmental AI spend (about $4.0B); software engineering has the highest scaled agent use (24%) in McKinsey 2025.

Source: menlovc.comSee the chain →
02 Ticket deflection with a self-service AI agent 🎧 Customer service

Customer success is about $630M of 2025 departmental AI spend; 57% of surveyed mid-market companies use AI for CX.

Source: businesswire.comSee the chain →
03 Employee self-service IT agent (password resets, access, software) 💻 IT service desk

IT is the second-highest function for scaled agent use (22%) and about $700M of departmental AI spend.

Source: mckinsey.comSee the chain →
04 AI code review on every pull request 🛠️ Engineering and DevOps

GitHub's review agent had reviewed 8M+ pull requests by May 2025; built into tools developers already use.

Source: itwire.comSee the chain →
05 Content and creative production at scale (copy and image variants) 📣 Marketing

Marketing is about $660M of 2025 departmental AI spend, driven by content generation; Klarna reported about $10M a year saved.

Source: marketingdive.comSee the chain →
06 Agent assist with automatic case and call summaries 🎧 Customer service

Low-risk, quick win built into contact center suites; ServiceNow reports about 80% time saved per resolution note.

Source: servicenow.comSee the chain →
07 Alert triage and investigation agent (AI Tier-1 analyst) 🛡️ Security operations

Triage is the top SOC AI use (67%), with 40% of alerts going uninvestigated today (vendor-sponsored survey).

Source: thehackernews.comSee the chain →
08 Touchless invoice processing in accounts payable 🧾 Finance

Document-heavy and measurable; enterprise cases report about 80% touchless processing.

Source: genpact.comSee the chain →
09 AI SDR for outbound prospecting and inbound lead qualification 📈 Sales

Fast-growing category with public results and costs (SaaStr: 3,000 emails/month, $50k-100k+ per platform).

Source: saastr.comSee the chain →
10 First-pass contract review and redlining against the playbook ⚖️ Legal

52% of in-house teams are using or evaluating contract AI, and active use nearly quadrupled since 2024.

Source: artificiallawyer.comSee the chain →

Browse by area

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.

📖 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

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 →