Open your credit card statement. Count the software subscriptions. The project management tool your team half-uses. The design software one person needs twice a month. The analytics platform that sends weekly reports nobody reads. The CRM that is somehow both essential and infuriating. The password manager, the email tool, the file storage, the communication platform, the video conferencing, the scheduling app, the note-taking app, the other note-taking app you switched to last quarter.
Add them up. For most businesses, the total is somewhere between horrifying and existential. The average company with 50-200 employees now spends between $200,000 and $500,000 per year on SaaS subscriptions. Not on servers. Not on custom development. On monthly fees for software that someone else built, someone else hosts, and someone else can raise prices on at any time.
This was not the original promise. SaaS was supposed to democratize software. Instead of paying $10,000 upfront for a license, you would pay $50 a month and get automatic updates, cloud access, and the freedom to cancel anytime. That sounded like a great deal in 2010. Fifteen years later, the math looks very different.
How We Got Here
The SaaS model did not become dominant because it was the best deal for customers. It became dominant because it was the best deal for investors.
Recurring revenue is the most valuable type of revenue in the tech world. A company that earns $10 million in one-time sales might be valued at 3-5x revenue. A company that earns $10 million in annual recurring revenue might be valued at 10-20x. Same money in, dramatically different valuation.
This valuation premium created a powerful incentive. Every software company, regardless of what they built or who they served, was pushed by their investors to convert to subscription pricing. Products that made perfect sense as one-time purchases (design software, word processors, image editors) were rebuilt as monthly subscriptions. Products that could have been simple tools became "platforms" with feature bloat designed to justify the monthly price.
The result is a landscape where businesses are trapped in a web of subscriptions they cannot easily escape, paying for features they never use, locked in by data migration costs and team retraining friction.
Sound familiar? It should. The SaaS economy recreated the exact same vendor lock-in dynamics that cloud and subscription pricing were supposed to eliminate. Just with smaller monthly numbers that are easier to ignore.
The average company with 50-200 employees spends $200,000-$500,000 per year on SaaS. Most use less than 40% of what they pay for. That gap is not an optimization opportunity. It is a structural flaw in the model.
The Math Nobody Wants to Do
Let us do a real calculation that most companies avoid.
Take a mid-size marketing agency with 30 employees. A reasonable SaaS stack looks something like this:
| Tool | Per User/Month | Annual Cost |
|---|---|---|
| Project management | $25 | $9,000 |
| CRM | $75 | $27,000 |
| Email marketing | $300 (flat) | $3,600 |
| Design tools | $55 | $19,800 |
| Analytics | $200 (flat) | $2,400 |
| Communication | $12.50 | $4,500 |
| Video conferencing | $15 | $5,400 |
| Cloud storage | $12 | $4,320 |
| Social media management | $200 (flat) | $2,400 |
| Accounting | $80 (flat) | $960 |
| Password management | $8 | $2,880 |
| Misc tools (10-15 others) | Various | $15,000 |
Total: roughly $97,000 per year.
Now here is the uncomfortable question: how much of that $97,000 delivers daily value? In most organizations, the honest answer is about 40-50%. The rest is either underused, redundant, or kept alive purely because nobody wants to deal with the migration pain of canceling it.
That means somewhere around $40,000-$50,000 per year is being spent on software that is not meaningfully contributing to business outcomes. For a 30-person company, that is enough to hire another employee. Or fund a significant marketing campaign. Or, perhaps more relevant to this moment in time, invest in AI tools that could replace half the stack entirely.
About $40,000-$50,000 per year in a typical 30-person company is spent on software that does not meaningfully contribute to business outcomes. That is enough to hire another employee or fund an AI transformation.
The Pricing Models That Are Breaking Through
Something interesting is happening in software pricing, driven partly by AI and partly by customer exhaustion with the subscription model. Three alternative approaches are gaining real traction.
1. Usage-Based Pricing
Instead of paying for access (whether you use it or not), you pay for what you actually consume. AWS pioneered this in infrastructure, and now it is spreading to application software.
Stripe charges per transaction. Twilio charges per message sent. OpenAI charges per token processed. None of them charge you a monthly fee for the privilege of having an account.
The appeal is obvious: your software costs scale with your business activity. Slow month? Lower bill. Big month? Higher bill, but also higher revenue to cover it. The alignment between cost and value is natural instead of forced.
The risk is equally obvious: unpredictable bills. Some companies have been burned by usage-based pricing when an unexpected spike in demand created a surprise invoice. But the tools for managing this (budgets, alerts, rate limits) are maturing fast.
2. Outcome-Based Pricing
This is the model that scares software vendors the most and excites customers the most. Instead of paying for the tool, you pay for the result.
Imagine a sales tool that charges you a percentage of the deals it helps close, instead of a flat monthly fee. Or a marketing analytics platform that charges based on the revenue attributed to its recommendations. Or an AI writing tool that charges per published piece rather than per seat.
The logic is compelling. If the tool delivers value, the vendor gets paid well. If it does not, the vendor gets nothing. The incentives are perfectly aligned.
A handful of companies are experimenting with this now, mostly in the AI space where outcomes are more measurable. It is still early, but the model has a gravitational pull that is hard to ignore. Customers want it. The vendors that offer it will steal market share from those that do not.
Usage-Based
- Pay for what you consume
- Costs scale with business activity
- Risk: unpredictable bills on spikes
Outcome-Based
- Pay for results, not access
- Incentives perfectly aligned
- Vendors share the risk with you
AI-Native Bundling
- One AI tool replaces many SaaS apps
- 80% functionality at 20% cost
- Disruption of the mid-market stack
3. AI-Native Bundling
Perhaps the most disruptive shift is not a pricing model at all. It is a product architecture that makes the pricing question moot.
AI tools are increasingly capable of performing tasks that previously required separate specialized software. A single AI workspace can now handle writing, research, data analysis, project planning, email drafting, and code generation. That is six different SaaS subscriptions replaced by one. The trend is already well underway: AI tools are systematically replacing traditional SaaS subscriptions across every category.
ChatGPT at $20 per month can do passable work in categories that, if you bought dedicated tools for each, would cost $200-$500 per month. Claude can analyze documents, write code, create content, and reason through complex problems. These are not perfect replacements for specialized tools, but for many use cases, they are good enough. And "good enough at one-tenth the price" is how disruption works.
The businesses that figure out the right mix of AI-native tools and specialized software will have a significant cost advantage over those still running a traditional SaaS stack.
The Audit That Saves Thousands
If you manage software spending for a business (or even just for yourself), here is a practical framework for reducing costs without losing capability.
Step 1: The Usage Audit
Get real usage data, not "who has a license" but "who actually logged in this month." Most SaaS platforms provide admin dashboards with this information. What you will typically find:
- 20-30% of licenses are completely unused
- Another 20-30% are used less than once a week
- Only 30-40% of users are active daily
Those unused and barely-used licenses are pure waste. Cancel them. If someone complains later, reactivate. This single step typically saves 15-25% of total SaaS spend.
Step 2: The Overlap Analysis
Map every tool to the job it does. You will almost certainly find overlap:
- Two project management tools (one from an acquisition, one from a team preference)
- Email marketing and CRM both have campaign features
- Three different ways to store and share files
- Multiple tools with built-in scheduling features
Consolidate ruthlessly. Every tool in your stack should be the only tool that does its job. If two tools do the same thing, pick the better one and migrate.
Step 3: The AI Replacement Test
For each remaining tool, ask: could an AI tool do this adequately? Not perfectly. Adequately. The bar is not "can AI do this as well as the specialized tool." The bar is "can AI do this well enough that the specialized tool is not worth its cost."
For many categories (content drafting, data analysis, research, basic design, scheduling, email management), the answer in 2026 is increasingly yes. You can run a surprisingly capable operation on a $50/month tech stack if you pick the right tools. Not for power users who need every feature. But for the average user who needs 30% of the features 80% of the time.
Step 4: The Negotiation Round
For the tools that survive the first three steps, negotiate. Most SaaS companies would rather give you a 20-30% discount than lose you as a customer, especially in a market where AI alternatives are making buyers more price-sensitive.
Tactics that work:
- Annual prepayment in exchange for discount (most vendors offer 15-20%)
- Mention specific AI alternatives you are evaluating (this is not a bluff anymore)
- Right-size your plan (most companies are on a tier above what they need)
- Ask for a custom plan that matches your actual usage pattern
A company that runs all four steps typically reduces SaaS spend by 30-50%. On a $100,000 annual stack, that is $30,000-$50,000 back in the budget.
Usage Audit
Get real login data, not license counts. Cancel unused and barely-used licenses. Saves 15-25% immediately.
Overlap Analysis
Map every tool to its job. Find duplicates. Pick the better one, migrate, and consolidate ruthlessly.
AI Replacement Test
For each remaining tool, ask: could AI do this adequately? Not perfectly. Just well enough to drop the dedicated tool.
Negotiation Round
For survivors, negotiate hard. Mention AI alternatives. Ask for 20-30% discounts on annual prepayment.
What Comes Next
The SaaS pricing model is not going to disappear overnight. It is too entrenched, too profitable for vendors, and too convenient for buyers who want predictable monthly costs.
But it is going to evolve, significantly, over the next three to five years.
1. Hybrid pricing becomes standard. A base subscription for core access plus usage-based pricing for AI features, compute, and advanced capabilities. This gives buyers predictability with flexibility.
2. Outcome-based tiers emerge in competitive markets. Vendors in crowded categories (CRM, email marketing, project management) will offer "pay for results" options to differentiate. The first major CRM to offer commission-based pricing will force others to follow.
3. AI collapses the mid-market SaaS stack. Tools that serve the "good enough for most users" segment will lose customers to AI platforms that provide 80% of the functionality at 20% of the cost. Specialized, power-user tools will survive. The mediocre middle will not. Some are calling it [the SaaSpocalypse](../saaspocalypse-claude-cowork-300-billion-crash/), and the disruption is accelerating faster than most vendors expected.
The implication for businesses is clear: the cost of being passive about software spending is going up. Companies that actively manage their SaaS portfolio, audit regularly, negotiate hard, and adopt AI alternatives where appropriate, will operate at a structural cost advantage over those that do not.
That advantage compounds. Lower software costs mean more budget for people, marketing, and investment. Better tool utilization means higher productivity. More AI adoption means faster operations. The gap between the strategic and the passive widens every quarter.
The Bigger Picture
Zoom out from the spreadsheet for a moment. Something philosophically interesting is happening to the relationship between businesses and their tools.
For the last fifteen years, software companies have had an asymmetric relationship with their customers. They controlled the pricing, the features, the data migration costs, and the switching friction. Customers paid, year after year, with limited leverage.
AI is shifting that balance. Not because AI tools are inherently better (though they often are). Because AI gives customers alternatives they did not have before. When every conversation with a SaaS vendor includes the unspoken possibility of "or we could just use Claude for that," the negotiating dynamic changes fundamentally.
This is not about AI replacing all software. It is about AI creating competitive pressure that forces the entire software industry to deliver more value at lower prices. That is good for everyone who buys software, which in 2026 is essentially everyone.
The subscription model worked because customers had no better alternative. Now they do. What happens next will be determined by which vendors adapt and which ones insist the old model still works.
The smart money is on adaptation. But history suggests a lot of companies will choose denial first.
Count your subscriptions. Do the math. The trap is only a trap if you do not see it.
