The Build vs. Buy Shift: Why 2026 is the Year Enterprises Stop Paying for Seats and Start Building AI Agents

SaaS costs are climbing 12.2% annually while enterprises watch budgets evaporate on per-seat licenses that deliver less value every quarter. In 2026, a fundamental shift is underway: companies are abandoning the "buy more seats" model and building custom AI agents that cost less, adapt faster, and deliver more value than generic software ever could.

The economics have flipped. Traditional SaaS providers charge $9,100 per employee annually, rising faster than healthcare costs. Meanwhile, AI-native platforms let you build custom automation for a fraction of that price, deploying agents that handle entire workflows instead of paying per user for features you barely use.

This isn't theory. It's happening right now across Europe and beyond.

The SaaS Cost Crisis: When Software Became More Expensive Than Healthcare

Here's a number that should concern every CFO: businesses now spend $9,100 per employee on SaaS annually, up 15% in just two years. That's more than the average employer contribution to healthcare coverage per worker.

SaaS inflation is running at 12.2%, nearly five times higher than the standard market inflation rate of G7 countries. While consumer inflation hovers around 2-3%, software vendors are hiking prices by double digits with little additional value.

The comparison to healthcare isn't accidental. Both industries have become notorious for opaque pricing, aggressive annual increases, and costs that seem disconnected from the value delivered. Healthcare costs are rising 6-7% annually for employer-sponsored insurance in 2026, but SaaS is outpacing it by nearly 2x.

The Per-Seat Model Is Broken

Traditional SaaS pricing made sense when software required installation, updates, and maintenance. You paid per user because each additional seat meant actual costs for the vendor.

That model is obsolete in the AI era. Modern systems cost pennies per API call, not dollars per user. Yet vendors continue charging per seat because it's predictable revenue and customers haven't had alternatives.

Until now.

Why Traditional SaaS Vendors Are Panicking About Agentic AI

The build versus buy debate continues as AI agents make it easier to create applications you used to buy, and build looks like a great option since customers are beginning to push back on SaaS deal inflation.

Software vendors saw this coming. That's why they rushed to introduce consumption-based pricing in 2025. But consumption models created unpredictability, and CFOs demanded stability.

Enter the agentic enterprise license agreement (AELA).

The AELA Trap: All-You-Can-Eat Until Renewal

Salesforce's Miquel Milano laid out the rationale behind AELA: "It's for customers that have already experimented. They're ready to scale. They want to go all in so we agree on a flat fee, and then it's a shared risk".

Sounds great, right? Unlimited AI capabilities for a flat fee?

Here's what vendors aren't saying: they're willing to take a loss on these agreements because they're playing for the renewal. Once you're completely locked in, they can price however they want. Milano is looking at lifetime value of a customer, noting that "if the customers are smart, they can rob the bank" but vendors take the risk because they want customers successful.

Smart enterprises are recognizing this dynamic and choosing a different path: building their own agents.

The Economics of Building vs. Buying AI Agents

Let's break down the real costs of building versus buying AI automation:

Traditional SaaS Costs (Per Employee)

• Sales software: 10.6% annual inflation
• Finance tools: 10.2% annual inflation
• Productivity apps: 10.1% annual inflation
• Average total: $9,100 per employee annually
• Hidden costs: Training, unused features, integration fees, support tickets

For a 100-person company, that's $910,000 annually in software costs, rising to $1.02 million next year.

Building Custom AI Agents

After working with over 1,000 companies deploying AI agents, we've seen build versus buy play out hundreds of times. Here's what the numbers actually look like:

Initial Build Cost (Traditional Approach):

• Timeline: 6-12 months
• Developer cost: €100,000-500,000+
• Risk: High - requires specialized expertise

AI-Native Platform Approach:

• Timeline: Days to weeks
• Annual cost: €20,000-80,000
• Deployment: Multiple interfaces (API, chat, forms)
• Integration: 2,000+ pre-built connections

The math is compelling. Instead of spending $910,000 annually on per-seat licenses, companies can build custom agents for a fraction of the cost using AI-native platforms.

Real Companies Making the Switch

Koninklijke Dekker: 140 Years Old, Radically Modern

Koninklijke Dekker, a Dutch wood company operating since 1883, faced manual order processing that consumed hours daily. Instead of buying more software seats, they built custom AI agents using Lleverage's AI automation platform.

Results:

• 92% reduction in processing time
• 90% fewer errors
• €2.7M in annual savings
• Handles orders in any format (Excel, PDF, email)

The key difference? Their AI agents understand context, adapt to variations, and improve over time without requiring new licenses or support tickets.

Ynvolve: From Bottleneck to Growth Engine

Ynvolve, a European IT reseller, spent 10-300 minutes per quote across 30,000 annual inquiries. Their sales engineers were buried in configuration work.

They built a configuration agent that collaboratively creates quotes:

• 90% reduction in quote creation time
• 50% forecasted revenue growth without hiring
• €30,000 monthly savings
• Deployed in weeks, not months

Roamler: Eliminating Entire Teams

Roamler sells data insights to retail customers, previously requiring a 15-person manual data extraction team processing photos.

They built AI agents that mimic data processor behavior:

• €300,000+ annual savings
• 15 FTE reduction in outsourcing team
• Automated 2 core processes with more coming
• System adapts to new data types automatically

Why 2026 is the Tipping Point

Several converging factors make 2026 the year this shift accelerates:

1. AI Agents Have Reached Production Maturity

In 2026, enterprises will stop debating 'LLMs versus knowledge systems' and start combining them through hybrid architectures that unite the creativity of large language models with the governance and explainability of domain-specific logic.

The technology works. It's reliable. And it's accessible.

2. Enterprise Pushback on Pricing

Gartner forecasts enterprise software spend rising at least 40% by 2027, with generative AI as the primary accelerant, yet 37% of finance leaders have already paused some capital spending in 2025.

CFOs are done accepting annual double-digit price increases. They're demanding alternatives.

3. Build Complexity Has Collapsed

Traditional custom development required:

• Specialized AI engineers
• Months of integration work
• Ongoing maintenance teams
• Complex infrastructure management

AI-native platforms have eliminated these barriers. Business users can now describe what they need in plain English, and the platform builds it.

4. Agentic Enterprise License Agreements Reveal the Risk

Agentic enterprise license agreements will become the norm as CxOs push back on unpredictable consumption models, but vendors may ink AELAs at a loss as they play for the renewal when you're completely locked in.

Smart enterprises see through this strategy and are choosing independence instead.

The Build vs. Buy Decision Framework for 2026

Not every company should build everything. Here's how to decide:

Build Custom Agents When:

✓ The process is core to your differentiationYour competitive advantage depends on doing this differently and better than anyone else.

✓ You handle high-volume, variable workflows
Order processing, invoice handling, customer support - processes that run thousands of times with countless variations.

✓ You're spending >€100K annually on equivalent softwareThe ROI math works when you're replacing expensive per-seat licenses.

✓ Your processes change frequentlyTraditional software breaks when your workflow evolves. Custom agents adapt.

✓ You need deep ERP integrationMcKinsey warns that resources poured into generative AI are starving core ERP capabilities, with only 40% of companies reporting enterprise-level EBIT impact from AI. Custom agents integrate deeply with existing systems.

Buy SaaS When:

✗ The process is generic and standardizedEmail, basic CRM, file storage - commodity needs that don't require customization.

✗ You need it working this weekSometimes speed trumps long-term value.

✗ Your team lacks technical resources
Though AI-native platforms have made this less relevant.

✗ The total cost is under €20K annuallyBelow this threshold, buying is usually simpler.

The Hybrid Approach: The Sweet Spot for Most Enterprises

Increasingly, enterprises are taking a hybrid path, combining in-house customization with third-party speed by building what differentiates them while licensing prebuilt solutions for non-core functions.

This looks like:

• Build: Order processing automation, quote generation, invoice processing
• Buy: Email platform, file storage, basic CRM
• Platform: Use AI-native tools like Lleverage to build quickly without massive technical teams

How to Make the Shift: A Practical Roadmap

Month 1: Audit Your SaaS Spending

Calculate your true cost per employee. Include:

• Base subscription fees
• Per-seat licenses
• Integration costs
• Training expenses
• Support tickets
• Unused features

Most companies discover they're spending 20-30% more than they realized.

Month 2: Identify Build Candidates

Look for processes where you're:

• Paying per user but only a few actually use it
• Working around software limitations
• Integrating multiple tools to achieve one outcome
• Spending hours on manual work the software can't handle

These are your prime candidates for custom agents.

Month 3: Start Building

Choose an AI-native platform that offers:

• Natural language development (describe what you want)
• Pre-built integrations (2,000+ for platforms like Lleverage)
• Multiple deployment options (API, chat, forms, embedded)
• Enterprise security and compliance
• Flexible pricing that scales with value, not seats

Start with one high-impact process. Build it. Measure results. Expand.

The Cost Comparison: A Real Example

Let's compare a 200-person European manufacturing company:

Traditional SaaS Approach

Annual Costs:

• Sales software (Salesforce): €200,000
• Finance tools (NetSuite): €150,000
• Productivity apps (Microsoft 365): €80,000
• Integration platform: €60,000
• Customer support tools: €40,000
• Total: €530,000 annually

Rising 12% annually = €594,000 next year, €665,000 the year after.

AI Agent Approach

Year 1 Costs:

• AI automation platform: €60,000
• Integration setup: €20,000
• Customer support automation: Build cost included
• Production planning agents: Build cost included
• Total: €80,000

Year 2+ Costs: €60,000 (platform only, no build costs)

5-Year Savings: Over €2 million compared to traditional SaaS trajectory.

What This Means for Different Industries

Manufacturing & Wholesale

These sectors handle massive document volumes with endless variations. Invoice processing automation, data transformation, and order processing become competitive advantages when built as custom agents.

Traditional software struggles with format variations. AI agents handle them naturally.

Professional Services

Consulting firms, law firms, and accounting practices bill for expertise. Every hour spent on administrative work is revenue lost.

Custom agents for document review, research, and client communication deliver immediate ROI by freeing billable hours.

Technology Companies

Tech companies have the expertise to build but often fall into the trap of building everything from scratch. AI-native platforms let them build faster while maintaining control.

Financial Services

Regulatory requirements and data sensitivity make custom agents essential. Pre-built software can't adapt to unique compliance needs without expensive customization.

The Counter-Arguments (And Why They're Wrong)

"We Don't Have the Technical Expertise"

This was true three years ago. It's not true in 2026.

AI-native platforms let business users describe what they need in plain English. The platform handles the technical implementation. Companies like Koninklijke Dekker (a wood company!) are building sophisticated automation without data science teams.

"Custom Solutions Are Risky"

Traditional custom development was risky. You invested months and hundreds of thousands before knowing if it would work.

Modern AI platforms let you prototype in days and deploy in weeks. The risk profile has completely flipped. Now the risk is being locked into expensive, inflexible SaaS agreements.

"We'll Lose Support"

With traditional SaaS, you get support. With custom agents on AI-native platforms, you get:

• Platform support for the infrastructure
• Documentation and community resources
• Systems that adapt without support tickets
• No waiting for vendors to fix bugs or add features

The support model is better, not worse.

"What About Updates and Maintenance?"

AI-native platforms handle infrastructure updates automatically. Your agents improve through training, not manual updates.

Compare that to traditional SaaS where vendor updates often break your workflows and require reconfiguration.

The Compliance and Security Advantage

Custom agents built on enterprise platforms offer security benefits traditional SaaS can't match:

Data Sovereignty: Your data stays in your infrastructure or chosen cloud region, crucial for European companies navigating AI security and compliance requirements.

Access Control: Granular permissions based on your organizational structure, not vendor-defined roles.

Audit Trails: Complete visibility into every action and decision your agents make.

Regulatory Compliance: Adapt to new regulations immediately without waiting for vendor updates.

The Network Effect Is Reversing

Traditional software benefited from network effects: more users meant more value. This created monopolies and pricing power.

AI agents reverse this dynamic. The value comes from how well the agent understands YOUR business, YOUR data, YOUR processes. Other companies using the same software doesn't help you. It just makes you more similar to competitors.

Custom agents create competitive moats, not vendor dependency.

Looking Beyond 2026: The Long-Term Shift

According to Deloitte Insights, successful organizations are accelerating from experimentation to impact, leveraging AI for workflow automation and decision-making with agentic AI systems that operate autonomously.

The transformation happening in 2026 will accelerate through the decade:

2026-2027: Early adopters demonstrate clear ROI
2027-2028: Mainstream enterprises begin major transitions
2028-2030: Custom agents become the default for core processes
2030+: Per-seat SaaS models limited to true commodity software

Companies making the shift now will have 3-4 years of competitive advantage while others pay increasing premiums for legacy software.

Getting Started: Your First 90 Days

Days 1-30: Assessment Phase

1. Calculate your true SaaS cost per employee
   Include all hidden costs: integrations, training, unused licenses, support overhead.
2. Identify your top 5 automation opportunities
   Look for high-volume, high-variation processes where you're working around software limitations.
3. Quantify potential savings
   Use our AI Invoice Processing ROI Calculator as a template.

Days 31-60: Platform Selection

4. Evaluate AI-native platforms
   Focus on platforms with proven enterprise deployments, not experimental tools.
5. Run a proof-of-concept
   Build one small agent to validate the approach and technology.
6. Calculate your ROI timeline
   Most companies achieve positive ROI within 6 months.

Days 61-90: Implementation

7. Start with your highest-impact process
   Choose something that delivers clear business value quickly.
8. Build your first production agent
   Deploy to a subset of users initially, then scale based on results.
9. Measure and expand
   Track savings, efficiency gains, and error reduction. Use success to fund expansion.

The Lleverage Advantage: Build Without Building

Lleverage represents the new paradigm: giving you the flexibility and control of custom-built agents without the complexity and cost of traditional development.

Our platform enables you to:

• Describe your automation needs in natural language
• Deploy agents across order processing, invoice handling, quote generation, and more
• Integrate with 2,000+ systems including Business Central, SAP, Dynamics 365, and other European ERP platforms
• Scale from prototype to enterprise deployment in weeks, not months

The difference shows in results: Koninklijke Dekker saves €2.7M annually. Ynvolve reduced quote time by 90%. Roamler eliminated a 15-person team. These aren't exceptions - they're the new baseline for companies that make the shift.

The Reality Check: You're Already Paying the Build Cost

Here's what most CFOs miss: you're already paying to build custom workflows. You're just doing it the expensive way.

Every integration between software tools. Every workaround for missing features. Every manual process bridging software gaps. Every support ticket waiting for fixes. Every consultant hour customizing your software.

That's all build cost hidden in your operating expenses.

The shift to custom agents just makes these costs explicit and dramatically more efficient. Instead of paying hundreds of thousands annually to work around software limitations, you pay once to build exactly what you need.

Conclusion: The Clock Is Ticking on the Old Model

The per-seat SaaS model served its purpose. It funded the cloud revolution and made enterprise software accessible to mid-market companies.

But like mainframes before it, the model has become a constraint on progress rather than an enabler.

In 2026, enterprises face a clear choice:

• Continue paying escalating per-seat fees for generic software
• Build custom AI agents that cost less and deliver more value

Build will beat buy as AI agents make it easier to create applications you used to buy, and in 2026, there will be an inflection point where enterprises become convinced that applications custom to their use cases are the way to go.

The companies making this shift now will have sustainable cost advantages, operational flexibility, and competitive moats that compound over time.

The question isn't whether to make the shift. It's whether you'll lead the transition or scramble to catch up in three years when your competitors are operating at half your software costs with twice your automation capability.

The seat-buying era is ending. The agent-building era has begun. Which side of history will your company be on?

Ready to make the shift from buying seats to building agents? Book a demo to see how Lleverage helps European enterprises automate complex processes without the complexity of traditional development, or explore our platform capabilities to understand how we're making custom AI automation accessible to businesses of every size.

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