Buyer Guide: How To Manage AI Costs

Manage AI costs by segmenting spend into four categories – LLM licenses, LLM APIs, AI-native tools, and legacy SaaS with AI add-ons – then applying category-specific controls, negotiation tactics, and contract protections to each. A single approach does not work across all four because the cost drivers, leverage points, and negotiation dynamics are fundamentally different.

AI costs are rising because vendors can no longer rely on seat expansion to grow revenue, so they’re compensating through AI-driven price increases, consumption-based models, and forced bundling.

Tropic COO Justin Etkin summed up the problem for buyers well: “The challenge is that finance and procurement teams have decades of default planning experience around seats and heads. You cannot forecast technology spend growth with headcount anymore.”

For finance and procurement teams managing hundreds of thousands to millions in business spend, it’s critical to understand the cost drivers behind these increases and how to manage them category by category. Teams that get this land materially better outcomes to their bottom line.

Why AI Costs Are Rising and Becoming Harder to Manage

The Seat-Based Model Is Breaking Down

For most of the SaaS era, software pricing was straightforward: pay a fixed fee per user, scale with headcount. That model relied on three growth levers working simultaneously – seat counts increasing as companies hired, per-seat prices compounding annually, and buyers lacking the data to challenge what they were paying.

All three have weakened. Tropic VP of Procurement Michael Shields describes how average software seat counts peaked during the pandemic and have been declining since:

• 2014 to 2019: License counts grew steadily as companies scaled and pre-bought seats against hiring projections.
• 2020: The remote work surge drove a sharp spike — everyone needed software to work from home.
• 2021 to 2022: The Great Resignation slowed hiring; widespread layoffs accelerated seat reductions. Companies cut license counts to match lower headcount.
• 2023 to 2024: Finance teams started tracking actual usage rather than purchased capacity. Seats were cut for users not generating sufficient value.
• 2025 to present: AI tools are beginning to replace functions previously performed by humans. Even when companies keep a tool, they run it with fewer seats.

Vendors can no longer rely on seat expansion to drive revenue, so they are reaching for the only remaining lever: price increases justified by AI.

The AI Tax

AI-driven price increases at renewal are currently running 20 to 37%, compared to the historical 3 to 9% annual uplift finance teams budgeted around for years. Vendors are applying the AI tax through four mechanisms:

• Forced SKU migrations: Existing tiers retired and replaced with AI-inclusive packages at higher price points.
• Forced bundling: Features previously available separately only accessible through a higher-cost AI tier.
• Credit-based consumption models: Shift from predictable per-seat pricing to usage-based credit systems that obscure true costs and make benchmarking significantly harder.
• Conditional discounts: Base product discounts offered only when buyers commit to AI add-on SKUs.

The Consumption Pricing Problem

Credit-based pricing exploded 126% year-over-year in 2025. And according to Tropic data, 90% of the fastest-growing vendors – including Anthropic, Clay, n8n, Cursor, and Lovable – charge buyers on consumption.

The challenge is that credits are not standardized:

• Some vendors define a credit as a token.
• Others define it as an API call.
• Others define it as a product action (one action might consume five credits and another just one).

Usage is difficult to predict at the onset and can move materially month to month, creating large swings in budget variance that seat-based planning cannot account for.

How to Manage AI Costs: Four Categories You Need to Track Separately

Tropic Sr. Director of Procurement Services Jacob Leichtman recommends segmenting AI spend into four distinct categories before attempting to manage or negotiate it. Each carries different cost risks, different negotiation tactics, and different controls.

1. Managing LLM License Costs (ChatGPT Enterprise, Claude Enterprise)

LLM licenses are not as non-negotiable as vendors suggest. Most enterprise LLM license models include credit tiers with meaningful variability, and real leverage exists once you are managing a substantial user base.

• Introduce competitive alternatives for leverage: If you are using adjacent productivity tools from a competing ecosystem, positioning that as a credible switch is one of the strongest negotiating positions available.
• Audit credit tier allocations before every renewal: Most organizations are paying for a higher credit tier than their actual usage patterns require. Confirm you are not paying for capacity you are not consuming.
• Audit license types within each platform: Different user roles often qualify for lower-cost license categories. Over-allocating premium license types to users who do not need them is a consistent and avoidable source of waste.
• Set hard spend limits at the organization, team, and individual user level: Configure consumption caps and spending controls before usage scales. Waiting until an overage appears on an invoice is too late. Most enterprise LLM platforms support granular spend controls – use them.
• Train users on token-efficient prompting: Long, detailed prompts rather than back-and-forth exchanges reduce token consumption significantly. If a designer iterates on an image 20 times at 5 credits per iteration, that is 100 credits. One detailed prompt achieves the same result at a fraction of the cost.

2. Managing LLM API Costs (OpenAI API, Anthropic API, Google Gemini API)

API costs are where AI spend can scale fastest and with the least visibility.

• Diversify across providers: Different models excel at different tasks. Testing use cases across providers gives you performance optionality and negotiating leverage. Single-provider concentration removes your ability to credibly threaten a switch.
• Evaluate purchasing channels: Major LLM APIs are available directly and through cloud hyperscaler marketplaces. Purchasing through a hyperscaler can offer better integration, consolidated billing, and in some cases access to lower-cost model variants that satisfy the same use case at reduced per-unit cost.
• Commit conservatively and renew early: One-year deals only, at 60 to 70% of your forecast – not 100%. Discount bands across API providers are wide enough that the difference between commitment tiers is often negligible. Staying at 60 to 70% keeps you flexible as new models emerge and lets you renew early if you burn through your commitment faster than expected.

3. Managing AI-Native Tool Costs (Cursor, Glean, Clay, Perplexity)

AI-native tools are often hungry for market share and their pricing models are still evolving, which creates real negotiating room that doesn’t exist with more mature vendors.

• Negotiate hard upfront: If a vendor's pricing model does not suit your business, propose alternatives. Many AI-native tools will offer different structures to different customers rather than lose the deal.
• Get commercial model change protections (not just price increase caps): A negotiated annual renewal cap is a win, but AI-native vendors are likely to overhaul their pricing models entirely year over year. Protection against unilateral commercial model changes is more valuable than a narrow price cap on a structure that may not exist at your next renewal.

For fastest-growing AI-native vendors – Cursor (650% year-over-year contract growth), Anthropic (425%), and Clay (300%) – significant discounts are unlikely. The priority is locking in current pricing with strong uplift protection before you scale into a higher usage bracket that gives the vendor more pricing power at renewal.

4. Managing Legacy SaaS AI Add-On Costs (Salesforce, Slack, HubSpot, Zendesk)

Legacy SaaS vendors bolting on AI features represent the highest volume of AI-driven uplifts in most companies' renewal calendars.

• Do not accept AI uplifts without proof of ROI: The fact that a vendor increased its R&D spend on AI does not obligate you to pay more if your team is not receiving measurable value. Require the vendor to show how AI features impact a specific measurable outcome: revenue, cost reduction, tickets resolved, or time saved per FTE.
• Use consolidation as leverage: Legacy SaaS companies are under significant pressure to show AI revenue, and their sales reps are incentivized to close AI SKU deals. That pressure creates negotiating room — use the AI upsell conversation to negotiate costs down in other areas of your agreement, or use the credible threat of migrating to an AI-native alternative to keep existing pricing reasonable.

How to Manage AI Costs: Universal Best Practices Across All Categories

As Justin Etkin puts it: “The companies building cross-functional spend accountability right now – negotiated caps, tiered pricing structures, and real usage visibility – will have a real cost and speed advantage over those that don't.”

These practices apply regardless of which category of AI spend you are managing.

1. Audit actual AI usage before every renewal

‍The fastest way to identify AI cost waste is to pull utilization data before any renewal conversation begins. For consumption-based contracts, compare actual usage against contracted minimums and identify which teams or workflows are driving the highest consumption.‍

2. Benchmark AI pricing before accepting any uplift

‍AI-driven uplifts of 20 to 37% are vendor asks, not market rates. Before accepting any renewal quote, pull SKU-level benchmarking data showing what comparable companies paid for the same tool after negotiating.

3. Negotiate the contract terms that protect against AI cost escalation

Beyond price, ensure these guardrails are in your contract before signing any AI agreement:

• Pricing model change protections: Require vendor notification and explicit acceptance before any SKU migration or tier restructuring applies to your contract.
• Annual price caps of 3 to 5%: Vendor standard uplift terms are often 9% or higher. A negotiated cap preserves deal value at every renewal.
• Consumption caps and overage handling: Negotiate a hard ceiling on monthly consumption and define exactly what happens when usage exceeds it.
• Auto-renewal removal: Auto-renewal clauses lock you into another year with no leverage to renegotiate. Remove them entirely.
• Unified credit definitions: For credit-based pricing, require vendors to define exactly what constitutes a credit in writing before signing.

Frequently Asked Questions

What is causing AI software costs to rise?

AI costs are rising because vendors lost the seat expansion revenue they relied on as headcount growth slowed and average license counts declined from their pandemic-era peak. Vendors are compensating through AI-driven price increases of 20 to 37% at renewal, forced SKU migrations into AI-inclusive packages, credit-based consumption models that obscure true costs, and conditional discounts tied to AI add-ons. Credit-based pricing exploded 126% year-over-year in 2025, and 90% of the fastest-growing vendors now charge buyers on consumption.

What is the AI tax in software pricing?

The AI tax refers to AI-driven price increases at software renewal, currently running 20 to 37% based on Tropic customer data. Vendors apply it through forced SKU migrations, feature unbundling, credit-based consumption models, and conditional discounts tied to AI add-ons. It emerged directly from the seat-based growth slowdown — as vendors lost seat expansion revenue, they shifted to price increases and AI feature bundling to compensate.

What are the four categories of AI spend I should track separately?

According to Tropic Sr. Director of Procurement Services Jacob Leichtman, the four categories are: LLM licenses such as ChatGPT Enterprise and Claude Enterprise; LLM APIs such as OpenAI, Anthropic, and Google Gemini APIs; AI-native tools such as Cursor, Glean, and Clay; and legacy SaaS products adding AI features such as Salesforce, Slack, HubSpot, and Zendesk. Each carries different cost risks and requires different management and negotiation tactics.

How do I negotiate AI cost increases at renewal?

Pull SKU-level benchmarking data before responding to any renewal quote. Anchor your opening ask to the 25th percentile of what comparable companies paid after negotiating. For legacy SaaS vendors, demand proof of ROI before accepting any AI uplift. For AI-native vendors, prioritize commercial model change protections over price caps alone. Introduce competitive alternatives early. Tropic's data shows that buyers who negotiate with benchmarking data reduce AI-driven vendor asks by approximately 55%.

How do I manage consumption-based AI pricing?

Set hard spending limits at the organization, team, and individual user level before usage scales. Require vendors to define exactly what constitutes a credit in your contract. Compare actual usage against contracted minimums monthly rather than waiting for renewal. Commit to API contracts at 60 to 70% of forecast rather than 100% to stay flexible as models evolve. Train users on token-efficient prompting to reduce unnecessary consumption.

What contract terms protect against rising AI costs?

The most important terms to negotiate in any AI contract: pricing model change protections requiring your explicit acceptance before any tier restructuring, annual price caps of 3 to 5%, consumption caps with defined overage handling, auto-renewal removal, and written definitions of what constitutes a credit for usage-based pricing. Without these, a vendor can restructure pricing unilaterally at your next renewal.

When should I start renewal conversations for AI contracts?

At least 90 days before the contract opt-out date, and ideally 180 days for your largest AI vendors. Tropic's data shows that teams starting renewal conversations 90 or more days out save 22 to 39% more than those engaging within 30 days. For fast-growing AI-native vendors, starting earlier allows you to lock in pricing before you scale into a higher usage bracket that gives the vendor more pricing power.