◢ AICost Leverage

so you negotiate like the Fortune 500 does

Pay what enterprises actually pay — not list price

Anthropic, OpenAI, Bedrock, Azure OpenAI, Vertex all offer 20–60% discounts through committed-use structures. Most enterprises pay list. AICost Leverage is the commitment desk for AI inference — we negotiate commitments on your behalf, arbitrage overcommitted capacity, and earn a percentage of realized savings. The reserved-instance playbook that built billion-dollar FinOps practices at AWS, applied to AI.

Engagement + % of savings
Talk to our desk → See crawler price data →

WHO SHOWS UP HERE

If one of these sounds like you, keep reading

“We spend $200K/month at list price. I know peers pay 30–40% less. I don't know how to close that gap.”
VP Procurement · Enterprise SaaS
“Our Anthropic renewal is in 60 days. We have no leverage and no internal AI FinOps team.”
CFO · Series C Scale-up
“Bedrock provisioned throughput is underutilized. Can we resell or convert?”
Head of Cloud Economics · Fortune 500
“Our Azure OpenAI commit expires Q4. Nobody has negotiated these before — too new.”
CPO · Insurance tech

FREE · SELF-SERVE

Start here. No signup. No gate.

Every tool on this page runs live. Use them, share them, come back if you want us to do it for you.

📊
Live vendor price crawler
See every price change we've detected — across Anthropic, OpenAI, Google, Mistral, Cohere, xAI, DeepSeek. Critical context for any commitment negotiation.
See latest changes →
📋
Playbook: Vendor lock-in risk
The cost of committing to a single vendor — and how to structure commits with exit optionality.
Read playbook →
🛠️
Self-host breakeven calculator
At what monthly spend does dedicated GPU infrastructure beat API? Needed before any large commit decision.
Run breakeven →
📋
Playbook: Price-change resilience
Commitment structures that protect you from vendor rate changes without locking you in.
Read playbook →

DONE-FOR-YOU

Want a human on it? Pick an engagement.

Productized engagements with clear scope, price, and deliverable. No custom SOW negotiation on the first call.

Commitment Desk Engagement
$15,000 + 15–30% of savings
6 weeks (first engagement)

We negotiate your next Anthropic, OpenAI, Bedrock, or Azure OpenAI commit on your behalf. Benchmarked against peer pricing. Share the upside: you keep 70–85% of realized savings over the commit term.

Learn more →
Commitment Optimization Software
$50–500K/year
Annual subscription

Continuous monitoring of actual usage vs commitment. Alerts before commits expire, utilization drops, or rates shift. Recommends rebalancing across vendors.

Learn more →
Cross-Vendor Capacity Swap
% of transaction
As needed

Brokerage service. Overcommitted Bedrock capacity? We find an undercommitted buyer — or swap your commit structure across vendors.

Learn more →

DEEP DIVES

Playbooks that go with AICost Leverage

Vendor lock-in risk Price-change resilience Self-host vs API decision Hidden TCO of AI

FAQ

Questions we hear before people book a call

Why you? What's the track record?

Our founder ran reserved-instance arbitrage at Ingram Micro on $1B+ of Azure ARR. The playbook — negotiate commits below list, arbitrage over-committed capacity, earn a percentage of realized savings — is directly transferable from cloud to AI inference. Same mechanics, three years earlier in the market curve.

What savings should I expect?

Depends on your starting point and commit size. Typical range: 20–40% off effective list for a first commitment, 5–15% incremental improvement on renewal cycles. At $200K/month spend, that's $40K–80K/month in realized savings.

Do you have direct relationships with vendor pricing teams?

Through CloudArmee (AWS Advanced Partner with GenAI competency) and direct outreach via our Inception membership. Not every vendor has formal commitment programs yet — for those, we negotiate custom terms.

What happens if my usage drops mid-commit?

This is a core risk we help manage. The Commitment Desk engagement includes utilization monitoring + renegotiation support if usage materially changes. The Cross-Vendor Swap service exists to handle over-commitment outcomes.

Is this a conflict of interest with AICost Optimize?

No — complementary. Optimize reduces your usage; Leverage reduces your per-unit rate. Teams that do both get compounding savings. We don't push you to over-commit; over-committed customers become unhappy customers.

INSTANT ANSWERS

Not sure if AICost Leverage is the right fit? Ask.

Describe your situation — we’ll route you to the exact playbook, tool, or engagement that matches.

🧞
AICost Genie — Frameworks · Tools · Playbooks

👋 Tell me what’s going on. I’ll surface the right frameworks, tools, and playbooks — and tell you which product line fits.

Pick the problem closest to yours:

THE OTHER FOUR

Five product lines, one platform

AICost Leverage is one of five outcomes. Keep exploring:

◇ AICost Clarity
so you understand why your bill is what it is
 ✦ AICost Optimize
so you pay less — measurably
 ◎ AICost Forecast
so you know what you'll pay — before you ship
 ▲ AICost Risk
so you don't pay later for what goes wrong

Stop paying list price at enterprise scale.

Commitment Desk engagement starts with a 30-min call. We'll assess fit before anyone signs anything.

Talk to our desk → See consulting ladder
📖 Data sources & methodology 161 text models · 9 embeddings · 24 vision · 41 audio · 8 vector DBs across 10 vendor pages · last verified 2026-06-05

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-06-05.
  • 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
2026-06-05
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 578 days captured
Anthropic Docs
2026-06-05
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 578 days captured
OpenAI
2026-06-05
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 579 days captured
Google AI
2026-06-05
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 554 days captured
Google Vertex
2026-06-05
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 554 days captured
DeepSeek
2026-06-05
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 493 days captured
xAI
2026-06-05
https://x.ai/api
Daily snapshot since Nov 2024 · 411 days captured
Mistral
2026-06-05
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 552 days captured
Cohere
2026-06-05
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 578 days captured
Voyage AI
2026-06-05
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.
Google — Gemini 2.0 Flash cachedInput Derived at 25% of input per Google 2.0 family caching rates.
Google — Gemini 2.0 Flash batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.0 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.0 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 →