We negotiate the contract and govern the meter, from your side of the table, with zero fees from any vendor.
OpenAI, Anthropic, Google, Bedrock. Pricing benchmarks, discount structure, rate-lock and price-protection clauses, renewal caps, negotiated for the buyer.
Both frontier-model contracts and embedded-AI premiums, delivered by a senior advisor who has sat on the publisher's side of the table: not a sales rep, and not a vendor partner.
and any application vendor adding GenAI to a renewal.
Seat vs. token vs. hybrid, modelled on your own telemetry, so you don't buy a three-year commit to cover a six-month pilot, or the vendor's forecast.
The control we installCommits sized on your telemetry, with rate-lock, price-protection and renewal caps written into the paper.
Copilot, Einstein, Joule, Firefly, Oracle's AI agents: the premium rarely arrives as a decision you make. It arrives embedded in a renewal you were always going to sign. We separate the AI premium from the base and opt you out of what you'll never use.
Embed & bundle AIMarkups hidden in renewals
FOMO pricing“Don't get left behind”
User-plus licensingOpaque metrics, audit risk
Price inflationAI premium in the base
Lost leverageBundled, no walk-away
Forecast trapsCommits sized by vendor
Blurred costsSaaS + AI inseparable
Unbundle & opt outSeparate AI line items
Audit user-plus termsMetrics, pre-signature
Size on telemetryYour data, not their forecast
Unmask the premiumLine-by-line analysis
Build AI deal skillNew contract anatomy
Pay only for the AI you use, at a price you can see.
The operating model behind the four leaks: model routing, spend attribution, output metrics, agent ceilings. Governance that survives the next renewal. A cap is a confession, not a control: it says you can't yet tell productive spend from wasteful spend, so it throttles both.
Every task runs on the priciest model, even ones a model 90% cheaper handles.
A routing policy and right-sizing rules that reserve frontier models for the genuinely hard minority; the routine majority goes to cheaper or open models.
Agents work for hours on their own, with no spend limit and no one owning the tab.
Budget ceilings, named owners and stop conditions around every autonomous workflow, so cost can't accrue in the background unwatched.
Spend shows up as one lump sum, never per team or task, so the waste stays hidden.
Spend attributed to team, task and outcome: cost per shipped feature, per resolved ticket, per closed case. The denominator that tells you if the bill was worth it.
Heavy usage gets praised as “adoption,” so teams burn more instead of doing more.
Consumption KPIs retired and output metrics installed. Salesforce swapped tokens for “agentic work units”; whatever the label, no one is rewarded for the burn.
Training-data carve-outs, IP indemnity, output ownership, model-deprecation and migration clauses, data residency: the terms that bite eighteen months in.
Like-for-like comparison on cost per outcome, not cost per token, because we earn nothing from any vendor, and can say so in writing.
Spent a full year's AI budget in about four months, then placed limits on its AI coding tools.
USA Today · NYT · BloombergCited an “exponential increase” in cost, took down usage leaderboards and began limiting use.
The New York TimesMoved to ration tokens per employee: a cap that throttles good and bad spend alike.
Reported 2026 · NYTThe winners of the next eighteen months will not be the firms that spend the least on AI. They will be the ones that govern it like the variable cost it now is. Read the full story: The Tokenmaxxing Hangover
Six workstreams, run individually or as a full engagement. You don't need a finished AI strategy to start. If there's a proposal on your desk, that's enough.
We review current AI spend, active contracts and whatever proposal is in front of you.
The leaks and premiums specific to you, quantified. Not a generic maturity model.
Terms fixed on the contract side; controls installed on the governance side.
Overage alerts, true-up prep and quarterly recalibration as usage and prices move.
AI bill shock and software-licensing exposure are the same failure: consumption running ahead of governance, and the controls that manage one manage the other. Rythium takes no fees, commissions, referral payments or partnerships from any software or AI vendor, ever. We are paid only by the buyer. It is what lets us tell you the cheaper model is “90% as good at 10% of the price” when it is, and put that in writing, because we have nothing to gain from steering you anywhere.
A senior advisor, not a sales rep, reads your proposal or renewal and tells you, in plain language, where the money and the risk are. No slideware, no vendor badges, no pitch for a platform we resell. If we can't find you leverage, we'll say so.
The first read is on us.
No, none. No fees, commissions, referral payments or partnerships from any software or AI vendor, ever. We are paid only by the buyer, which is what keeps our benchmarks and advice honest.
Three forces at once: pricing shifted from flat subscriptions to usage-based metering, AI agents began running multi-step tasks unattended for hours, and teams defaulted to the most expensive model for everything. Budgets set against a subscription mindset met a bill that behaves like a meter.
As emergency triage, yes: a cap stops runaway spend while you get your bearings. As a strategy, no. A cap throttles good and bad spend alike because it can't tell them apart. The goal is attribution and routing, so you cut waste without starving the uses that create value.
A great deal. AT&T's chief AI officer told The New York Times that using less advanced models where they suffice can cut costs by up to ninety percent, and a WEKA executive told USA Today the cheaper models are frequently around ninety percent as capable at a tenth of the price.
OpenAI Enterprise, Anthropic Claude, Google Gemini and Vertex, Microsoft Copilot, AWS Bedrock, Salesforce Einstein, SAP Joule, Adobe Firefly, and any application vendor adding GenAI SKUs to a renewal. Both frontier-model contracts and embedded-AI premiums.
Fundamentally, yes. The controls that govern licensing exposure (attribution, contract discipline, independent validation, right-sizing) are the controls that govern AI spend. We bring twenty years of that scar tissue to the AI table.
Reporting referenced: USA Today, The New York Times, Bloomberg and The Wall Street Journal on 2026 enterprise AI spending, and Rob May's “Tokenminning Manifesto.” Analysis and framing are Rythium's own.
You don't need to wait. Send the contract over and a senior advisor will tell you where the money and the risk are, before you sign.
Talk to an advisor
Sheshagiri spent years on the publisher's side of the table before founding Rythium as a fully independent licensing and AI procurement advisory. He writes and speaks on enterprise software economics, vendor negotiation and AI cost governance, from the buyer's side, always.
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