🤝 System Integrators and Consultants

Deliver AI that is economical and governed

For system integrators, AI consultancies and FinOps providers. Use AICost inside your client projects, sell at list or bundle it, and keep the margin.

Is this you?

  • You build RAG, agents or document AI for clients
  • Your clients ask what it will cost at scale and how it is governed
  • You want a repeatable assessment to open larger projects
Priced on governed scope, not on consumption. You pay for the workloads AICost governs. No token markup, no share of your AI bill and no share of savings, so our price never grows because your usage does.

Plans

One Workload Review

$9,500 fixed
One important production workload, end to end
  • Complete workflow economics
  • Cost per business outcome
  • Step-level optimization
  • Forecast at today, 10 times and production scale
  • Governance envelope
  • Agent, tool and budget boundaries
  • Production-readiness findings
  • Recommended controls
  • Retest after your changes

$5,000 credited toward an enterprise plan if you upgrade within 60 days.

Book a Workload Review

Enterprise AI Baseline

$19,500 fixed
Establish the enterprise view
  • AI estate and inventory
  • Initial workload portfolio
  • Cloud, provider and billing picture
  • Top cost concentrations
  • Optimization opportunities
  • Governance gaps and priorities
  • Route and data-class policies
  • Executive baseline
  • 90-day action plan
Book a free consult

Continuous Enterprise

$4,500/mo
Up to about 10 AI applications or business workflows
  • Continuous economics
  • Monthly optimization review
  • Budget, route and control changes
  • Governance envelopes
  • Evidence and revalidation on change
  • Monthly executive and FinOps review
Start here

Partner pricing on every engagement: 30% off list

ListPartner price
One Workload Review$9,500 fixed$6,650 fixed
Enterprise AI Baseline$19,500 fixed$13,650 fixed
Continuous Enterprise$4,500/mo$3,150/mo

From 5 engagements a year: 35% off list. Beyond that, negotiated.

  • Partner price on every engagement
  • No membership fee
  • Sell at list, mark up or bundle
  • Co-branded delivery
Become a partner
An illustrative workload, priced by AICost engines

What AICost changes in one AI workflow

Each step below is priced by its own AICost engine at the volume shown. The levers and the governance envelope are what AICost adds. Numbers refresh with model prices.

Claims FNOL, triage and fraud flags

10,000 claim opened a month · 8 steps

First notice of loss by phone, app or email; capture photos and documents, then Coverage verification and severity triage (fast-track vs complex), then Fraud scoring and SIU referral, then Claim file summarization for the adjuster.

Transcribe first-notice calls $480/mo Audio Cost
Model calls $420 $126/mo ↓ 14% prompt caching↓ 37% model routing↓ 19% batch the non-urgent work AI Cost
Read claim documents $710/mo Document Reading (OCR)
Damage photos $300/mo Vision Cost
Small model for simple claims /mo Model Router
Fraud-flag accuracy and bias $94/mo ✓ Accuracy and hallucination checks Answer Checking
Adjuster decides $32,000 $16,000/mo ↓ 50% better grounding Human Review
State AI rules for insurers $3,654/mo ✓ Compliance evidence Compliance Cost
Without AICost
$37,657/mo
$3.77 per claim opened
  • Spend shows up on next month's invoice
  • No per-run or daily ceiling on agents
  • Controls written down, not enforced
  • No evidence trail for audits
With AICost
$21,363/mo
$2.14 per claim opened 43% lower cost
Monthly budget$600
Daily cap$26
Per-run ceiling$0.17
Turn cap per run6
  • Accuracy and hallucination checks
  • Human review
  • Compliance evidence
  • Cost firewall at the gateway
  • Data-class model allowlist
Where the savings come from
🎧 Model calls$420 → $126/mo Prompt caching using the AICost Prompt Caching engine −$60 (14%)
Repeated instructions and documents are cached, so you pay less for the same prompt text. *; Model routing using the AICost Model Router engine −$153 (37%)
Routine steps run on a cheaper model; critical steps stay on the primary model. *; Batch the non-urgent work using the AICost Batch or Realtime engine −$81 (19%)
Work that can wait runs in the provider's batch mode at a lower price. *
🧪 Adjuster decides$32,000 → $16,000/mo Better grounding using the AICost Human Review engine −$16,000 (50%)
Answers come only from your approved sources, so fewer cases go to a person. *
What it means against the AICost price: Savings on this one workload pay back the One Workload Review fee in about 18 days.

Illustrative: one workload at the volume shown, priced from current public model prices. Your numbers come from your own workload. Open this workflow in the live demo →

Questions

Is there a partner fee?

No membership fee. Register, then buy at partner price per engagement.

Can we resell above list?

Yes. Sell at list, mark up, or bundle into a larger project. Your client contract stays yours.

Is there a volume tier?

Yes, from the engagement count shown above, and negotiated beyond that.

What counts as a workload?

One AI application or business process with its own outcome and governance boundary. Steps inside it, such as models, agents and tools, are not counted separately.

Do you charge a percentage of AI spend?

No. AICost is priced on the workloads it governs. There is no token markup and no share of savings.

Other paths

Talk to AICost

Bring one workload. We price it step by step and show what changes with AICost.

Book a free consultSee the live demo
📖 Data sources & methodology 172 text models · 9 embeddings · 44 vision · 62 audio · 8 vector DBs across 10 vendor pages · last verified 2026-10-18

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-10-18.
  • 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
2026-10-18
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 704 days captured
Anthropic Docs
2026-10-18
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 704 days captured
OpenAI
2026-10-18
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 705 days captured
Google AI
2026-10-18
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 680 days captured
Google Vertex
2026-10-18
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 680 days captured
DeepSeek
2026-10-18
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 619 days captured
xAI
2026-10-18
https://x.ai/api
Daily snapshot since Nov 2024 · 537 days captured
Mistral
2026-10-18
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 678 days captured
Cohere
2026-10-18
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 704 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 →