---
title: "AI Is Repricing SaaS: Why Renting Software Just Got More Expensive Than Building It"
url: "https://goremy.ai/blog/articles/ai-is-repricing-saas-the-build-vs-buy-math-for-2026"
author: Luis Chavez-Mattos
editor: Luis Chavez-Mattos
editor_title: Director, Product
published: "2026-09-09T06:20:06.370Z"
updated: "2026-09-09T06:20:06.370Z"
topic: Build vs Buy Software
description: "Per-seat SaaS pricing assumed human-paced software. Real-time AI broke that math. Here's how to calculate your own build-vs-buy delta."
---

# AI Is Repricing SaaS: Why Renting Software Just Got More Expensive Than Building It

> Per-seat SaaS pricing assumed human-paced software. Real-time AI broke that math. Here's how to calculate your own build-vs-buy delta.

Edited by Luis Chavez-Mattos, Director, Product · Published 2026-09-09

## Why is AI making it more expensive to rent software than to build it?

AI is repricing SaaS in two directions at once: it raises the vendor's cost to serve you, because inference isn't free, while it collapses your cost to build your own replacement, because AI-assisted coding is now fast enough to ship what you used to rent. That's the whole story. Everything else is detail.

## TL;DR

- Pure seat-only pricing has collapsed to **just 8% of the market**, because AI agents break the link between human logins and value.
- AI coding assistants have made rebuilding commoditized software dramatically cheaper, with one rebuild costing **under $1,000 AUD** where it once would have cost about $10,000 AUD.
- The average US company spends **$349,000 a year on SaaS**, and subscription sprawl keeps accelerating as AI tools stack on top of legacy subscriptions.
- Retool's survey found that **35% of builders** have already replaced at least one SaaS tool with a custom AI-built alternative.
- SaaS still wins for tools with **deep integrations, regulatory weight, or continuous vendor R&D**, while commoditized tools are the first candidates to build.
- Without oversight, cheap internal tools become **unpatched liabilities** that nobody owns, so every build needs an owner, a maintenance budget, and a security review.

## Why was SaaS pricing never built for this?

Per-seat pricing worked for twenty years because it was a decent proxy for value. More employees logging in generally meant more value extracted from the tool. That assumption is broken now. An AI agent handling support tickets that used to require 50 human agents doesn't need 50 CRM seats to do it. [1] Under a pure per-seat model, the vendor's revenue falls as the customer's output holds steady or improves. As one analysis put it, no business model survives that math. [1]

**The build-vs-buy delta, one team's real case**

- $10,000 AUD: Rebuild cost, two years ago
- under $1,000 AUD: Rebuild cost with Claude Code today
- $15,000 AUD: Estimated 3-year savings

Forbes framed the same shift from the buyer's side: when agents are the users, not people, "the idea of a 'seat' starts to lose meaning." [2] Pricing tied to human logins stops tracking how value actually gets created.

## What does 'real-time' mean now?

DIY software got competitive for one reason: latency, not sentiment. At NVIDIA GTC in early 2026, Runway, Decart, and PixVerse demonstrated AI video generation running under 100 milliseconds of latency, the response time of a multiplayer game server, not a rendering queue. [3] FastVideo generated 1080p video faster than playback speed, turning a 30-second clip into roughly 4.5 seconds of compute. [3] Code generation made the equivalent jump: AI coding assistants now ship production-ready features on the same afternoon a ticket gets written, not the same quarter.

**How SaaS vendors responded to AI pricing pressure**

|  | Share of vendors analyzed |
| --- | --- |
| Raised per-seat prices | 35% |
| Went hybrid (seat + usage metering) | 65% |

_Based on Bain & Company's analysis of more than 30 SaaS vendors; no vendor made a clean break to pure usage or outcome pricing._

When video and code both generate at or near interaction speed, the gap between buying a tool and building it closes to almost nothing for a large class of software. That's the zero-marginal-cost app: not free to run, but cheap enough to build and rebuild that renting it forever stops making sense.

## Why is per-seat pricing breaking?

Vendors know this. Bain & Company analyzed more than 30 SaaS vendors and found none have made a clean break to pure usage or outcome pricing. [4] Instead:

- **35% raised per-seat prices** and bundled AI features into the higher number, hoping customers wouldn't do the math. [4]
- **65% went hybrid**, layering AI-usage metering on top of the existing seat count rather than replacing it. [4]

**Seat-based pricing's collapse as the sole pricing metric**

| Pricing approach | Share of SaaS market |
| --- | --- |
| Some seat component still present | 80% |
| Seats as the only value metric | 8% |

Seat-only pricing has collapsed from the dominant model to just 8% of the market, even though more than 80% of SaaS companies still keep some seat component in the mix out of habit or contract inertia. [5]

The transition itself hurts vendors. Bain describes a SaaS company pitching a $40,000 AI agent to replace an $80,000 sales rep role. During the evaluation period, the human and the agent run in parallel, so the customer's total cost rises 50% before any savings show up. [4] That's a hard sell, and it's why public vendors are already repricing under pressure. Atlassian and HubSpot have moved from flat-fee AI subscriptions to credit-metered, outcome-based pricing (Rovo AI, Breeze AI), and they're doing it with HubSpot down 44% and Atlassian down 55% in stock price. [6] When the market punishes you for guessing wrong on pricing, you stop guessing.

## How much does the average company spend on SaaS?

Run the numbers before you decide anything. The average US company spends $349,000 a year on SaaS, or about $6,980 per employee at a 50-person company. [7] That's not shrinking. AI tools alone grew from 8.8% of SaaS purchases in April 2025 to 26.4% by March 2026, and in most organizations that spend stacks on top of legacy subscriptions instead of replacing them. [7] Subscription sprawl is accelerating too: companies added an average of 53 new subscriptions a month in early 2025; by March 2026 that number was 401 a month, on top of a median 25 active subscriptions already running. [7] Nobody is pruning. Everyone is adding.

**AI's share of SaaS purchases is rising fast**

| Month | AI tools as share of SaaS purchases |
| --- | --- |
| April 2025 | 8.8% |
| March 2026 | 26.4% |

## How do you calculate your own build-vs-buy delta?

The formula is simple. Compare (subscription cost × years you'd keep paying) against (AI tool subscription + build hours + ongoing maintenance hours, priced at your loaded rate).

**Subscription sprawl is accelerating, not shrinking**

| Period | New subscriptions added per month |
| --- | --- |
| Early 2025 | 53 subscriptions |
| March 2026 | 401 subscriptions |

A real case: a team on a free-tier tool faced a jump to $360 AUD/month. Two years ago, a custom rebuild would have cost roughly $10,000 AUD, more than twice the subscription cost, so renting won. Using Claude Code today, the same rebuild cost under $1,000 AUD and 14 hours of work, netting an estimated $15,000 AUD in savings over three years. The tool didn't change. The cost of building it did.

That pattern shows up at scale too. ClickUp built six internal AI tools and cut $200,000 a year in automation software spend. [8] Harmonic replaced a $20,000/year third-party tool with an internal rebuild, then kept going, scaling to 33 internal apps. [8] Retool's survey of 817 builders found 35% have already replaced at least one SaaS tool with a custom AI-built alternative, and 78% expect to build more in 2026, with workflow automation (35%) and internal admin tools (33%) the categories most exposed. [8]

If you want a structured way to run this comparison across your own stack, our piece on [self-hosted alternatives to enterprise SaaS](https://goremy.ai/blog/articles/5-self-hosted-alternatives-to-enterprise-saas) walks through category-by-category math.

**What SaaS actually costs a company**

- $349,000: Average annual US SaaS spend
- $6,980: Average annual spend per employee

## Where does DIY AI win, and where does SaaS still win?

Not every subscription is a target. The tools most exposed are commoditized: CRUD forms, internal dashboards, approval workflows, ticket routing, scheduling glue. These are thin logic layers with no real moat, and they're exactly what Retool's data shows getting replaced first. [8]

SaaS still wins where the product carries genuine depth:

1. **Deep integrations and network effects.** Tools that connect dozens of third-party systems reliably are hard to rebuild and harder to maintain.
2. **Regulatory and compliance weight.** Payroll, tax, and audited financial systems carry liability you don't want to own.
3. **Continuous vendor R&D.** Categories where the vendor is still meaningfully outpacing what a small internal team can build and keep current.

**Internal AI-built rebuilds already replacing paid tools**

| Company | Annual value |
| --- | --- |
| ClickUp (annual savings) | $200,000 |
| Harmonic (tool replaced) | $20,000 |

_ClickUp figure is savings from six internal tools; Harmonic figure is the cost of the single third-party tool it replaced before scaling to 33 internal apps._

Everything else is a candidate for the build column.

## What is the governance catch?

Here's the part that gets skipped in the excitement: DIY savings evaporate fast without oversight. Retool found 60% of builders have shipped software outside official IT oversight in the past year, and 35% of organizations have no AI productivity metrics at all. [8] More broadly, over 80% of employees report using unapproved AI tools at work, and Deloitte's 2026 State of AI in the Enterprise research found only about one in five companies has a mature governance model for autonomous AI agents. [9]

**Build vs. buy: where each approach wins**

| Option | Upfront Cost | Maintenance Burden | Compliance & Liability Fit | Speed to Ship | Fits Commodity Tools |
| --- | --- | --- | --- | --- | --- |
| Keep renting SaaS | low | low | high | high | No |
| Build in-house with AI tools (recommended) | low | medium | low | high | Yes |
| Hybrid (rebuild core, rent edge cases) | medium | medium | medium | medium | Yes |

_Ratings are relative across these options, not absolute; grounded in the article's build-vs-buy framework._

That gap is where the real cost hides: unpatched internal tools, undocumented logic, and nobody responsible when the thing breaks at 2am. Building software cheaply doesn't mean building it for free. It still needs an owner, a maintenance budget, and a security review, or the $1,000 rebuild turns into a liability nobody priced in. Our coverage of the [shadow AI stack](https://goremy.ai/blog/articles/5-shadow-ai-tools-employees-use-to-build-their-own-apps) and how to [secure database access for internal agents](https://goremy.ai/blog/articles/5-ways-to-secure-database-access-for-ai-agents) both go deeper on managing that risk. Platforms like [Remy](https://goremy.ai) exist specifically to give these AI-built internal tools the accountability structure that ad hoc shadow builds usually lack.

## What is the takeaway?

Software is becoming an asset you own, not a service you rent, at least for the layer of your stack that isn't actually differentiated. The decision rule is straightforward: if a tool is commoditized, cheap to rebuild with AI, and low-stakes if it breaks, build it and own it. If it's compliance-heavy, deeply integrated, or genuinely hard to replicate, keep paying for it, and negotiate the price with the vendor's own repricing anxiety in mind. Everyone in between deserves the math, not a gut call.

## Frequently Asked Questions

### Why is per-seat SaaS pricing breaking down because of AI?

AI agents let companies get the same or better output with fewer human users logging in. Under per-seat pricing, that means vendor revenue falls as customer value holds steady, an incentive mismatch that most vendors are now patching with hybrid usage-based models rather than solving outright. [1] [4]

### How much does the average company spend on SaaS per employee?

US companies spend about $349,000 a year on SaaS tools overall, roughly $6,980 per employee at a 50-person company, and that figure keeps climbing as AI tools add a new line item rather than replacing existing subscriptions. [7]

### Is it actually cheaper to build software with AI instead of buying SaaS?

For commoditized, low-differentiation tools, often yes. Retool found 35% of teams have already replaced a SaaS tool with a custom AI-built one, and case studies show real six-figure annual savings, but only when the build gets proper maintenance and oversight. [8]

### What kind of SaaS tools are safest to keep paying for?

Tools with deep third-party integrations, heavy regulatory or compliance exposure, or vendor R&D that a small internal team can't realistically match. Simple CRUD, workflow, and admin tools are the most exposed to replacement. [8]

### What's the biggest risk of replacing SaaS with DIY AI tools?

Ungoverned shadow AI. 60% of builders have shipped internal tools outside official IT oversight, and most companies still lack a mature governance model for the AI agents and tools employees are building on their own. [8] [9]

## Sources

1. [The Death of Per-Seat SaaS: How AI Is Forcing a Complete Repricing of Enterprise Software in 2026](https://www.aimagicx.com/blog/death-of-per-seat-saas-pricing-ai-agents-2026) (AI Magicx)
2. [Why Agentic AI Is Breaking The SaaS Pricing Model](https://www.forbes.com/sites/peterbendorsamuel/2026/02/18/why-agentic-ai-is-breaking-the-saas-pricing-model/) (Forbes)
3. [Real-Time AI Video Arrives at Game Engine Speed](https://megaton.ai/news/real-time-ai-video-arrives-at-game-engine-speed) (Megaton AI)
4. [Per-Seat Software Pricing Isn't Dead, but New Models Are Gaining Steam](https://www.bain.com/insights/per-seat-software-pricing-isnt-dead-but-new-models-are-gaining-steam/) (Bain & Company)
5. [SaaS Pricing Models for Series A Founders and Investors](https://www.crv.com/content/saas-pricing-models) (CRV)
6. [SaaS pricing model shifts to outcome-based AI pricing (LinkedIn post)](https://www.linkedin.com/posts/davidelkington_public-saas-companies-continue-to-show-the-activity-7454294026490494976-6stN) (LinkedIn / David Elkington)
7. [Average SaaS Spend Per Employee in 2026: The Definitive Benchmark](https://www.cledara.com/blog/average-saas-spend-per-employee-2026) (Cledara)
8. [The build vs. buy shift: how vibe coding and shadow IT have reshaped enterprise software](https://retool.com/blog/ai-build-vs-buy-report-2026) (Retool)
9. [What Is Shadow AI? Risks, Governance, and How to Take Control](https://www.wiz.io/academy/ai-security/shadow-ai) (Wiz)