Build vs. buy AI agents: What 1,000+ enterprise deployments taught us about the real costs

When it comes to AI agents, every leadership team eventually faces the same question: should we build this ourselves or buy a platform?

It's a familiar debate. The same one that played out with CRM systems, HR platforms, and collaboration tools. But AI agents are different. The technology is moving faster than any category before it. The skills required are scarce. And the stakes are higher because AI agents touch every workflow in your company.

After working with over 1,000 companies deploying AI agents, we've seen this decision play out hundred of times. Here's what actually happens when companies choose to build versus buy, and what factors should inform your decision.

The build instinct: Why it feels right (at first)

The impulse to build is understandable. You have talented engineers. You have specific needs. You've built other internal tools successfully. Why not build your AI infrastructure too? This last part is key. But the question should not be whether your team can build AI infrastructure. It's whether that's the best use of their time.

The hidden costs of building

When companies estimate the cost of building, they typically calculate: engineer salaries + infrastructure costs + model API expenses. But that's just the beginning.

1. Time to value is measured in quarters, not weeks

Building production-grade AI infrastructure takes time. Not just to build the first version, but to reach feature parity with existing platforms.

Consider what you actually need:

• Universal context access: Agents need to understand information across Slack, Notion, GitHub, Salesforce, and every other tool your company uses. Each integration requires custom development, testing, and maintenance.
• Permission systems designed for agents: Traditional user permissions break down when an HR agent needs to access documents that general employees can't see directly. You need an entirely new permission model.
• Semantic search infrastructure: Unlike humans who intuitively know "that document lives somewhere in that part of the system," agents require pre-embedding and semantic search to navigate organizational knowledge effectively.
• Model flexibility: The AI landscape changes monthly. Your infrastructure needs to support multiple model providers and adapt as new capabilities emerge.

At Dust, we've built all of this over two years with a dedicated team. Companies that choose to build typically underestimate the timeline by 6-12 months.

2. Maintenance compounds faster than features

The real cost isn't building version one. It's maintaining and improving it as the AI landscape evolves.

Nacim Rahal, VP of Data & AI at Doctolib, initially tried this approach. When his team built DoctoGPT, an internal ChatGPT-based solution, it quickly gained 800 active users. But success brought an unexpected challenge: "We created a Feature Requests JIRA board that revealed massive demand. We were overwhelmed with requests within days of launch."What started as a simple tool quickly required out-of-the-box connectors, native plugins for platforms like Zendesk, and advanced features users expected. Nacim realized: "This was unsustainable if we had the objective to be at the frontier. We would be a permanent bottleneck requiring resources across multiple teams." Beyond the AI functionality itself, there was extensive supporting infrastructure: connector development, access rights management, security features, audit logs, compliance frameworks, and ongoing maintenance costs.

Wakam, one of Europe's leading insurtech companies, experienced this firsthand. When they encountered GPT-4 in late 2023, their five-person data science team began building a custom AI chatbot with RAG capabilities. While they had the expertise to create a working prototype, maintaining it required constant engineering effort. Every new feature meant weeks of development. They soon realized the AI market was advancing faster than their small team could match. To offer employees functionality comparable to commercial platforms, they would need to triple their team size.

Worse, Wakam's team found themselves rediscovering fundamental challenges that AI platform companies had already solved: implementing effective RAG, managing vector databases, handling model orchestration, and creating user interfaces. They were spending time on undifferentiated technical problems rather than creating business value.Wakam recognized these limitations early and pivoted to Dust. Within two months they achieved 70% employee adoption and deployed 136 AI agents across the organization. Their legal team cut contract analysis time by 50%, and data teams enabled self-service intelligence that dramatically reduced processing time.

3. The opportunity cost is your actual business

Ardabelle, a private equity fund, recognized that their competitive advantage wasn't AI infrastructure—it was deal analysis and portfolio management. The question wasn't whether to embrace AI, but whether to spend 12-18 months building it or partner with a platform that could deliver enterprise-grade capabilities immediately.

Ardabelle chose to buy. Within 90 days, they had achieved:

• Adoption across the entire investment team
• 150+ queries per analyst per week
• 50% more deals evaluated in the same timeframe

Could they have built this themselves? Probably. But those 90 days would have been 9-12 months.

Nacim from Doctolib developed a clear guiding principle: "Build what's in our core business, buy what will be a side project." For a healthcare technology company with 3,000 employees, the choice was clear: "We'd rather put 100% of our core resources on helping patients and solving practitioners' problems." He added: "We won't have a comparative advantage at building permissions systems and connectors."

Anne-Claire Bellec, VP of AI Strategy at Mirakl, understood this instinct well. "We have talented people on our product, design, engineering, and data science teams. And we want them to bring 100% of their value to our customers."

The real question isn't whether you can build AI infrastructure. It's where you want your talent and resources focused: on your core business or on maintaining infrastructure that's already been solved.

When building makes sense

Building isn't always wrong. There are legitimate scenarios where it's the right choice:

1. You have truly unique requirements that no platform can address

Not "we want it to work slightly differently." We mean fundamentally unique workflows that require custom AI architectures that no platform can support. This is extremely rare. 

2. AI infrastructure is your core product

If you're selling AI capabilities to customers, building your own infrastructure may be strategic. But even then, many successful AI companies use platforms like Dust internally while building their customer-facing products.

3. You have dedicated resources and realistic timelines

If you can commit a team of 5-10 engineers for 12-18 months without impacting your core product roadmap, and you have AI expertise in-house, building might work. But be honest about the timeline and resources required. Be brutally honest here.

The buy advantage: Speed, scope, and continuous improvement

The biggest risk of not providing a secured AI platform is shadow AI: employees using personal ChatGPT accounts with sensitive company data, creating uncontrolled security vulnerabilities across your organization.

When companies choose to buy, they're not just getting software. They're getting accumulated expertise from thousands of deployments and eliminating security risks from day one.

Enterprise-grade security from day one

Building secure AI infrastructure is hard. Really hard. When you buy, you get experts solving the hard problems:

• Zero data retention guarantees from model providers
• Fine-grained permissions that work with AI agents
• Compliance with GDPR, SOC 2, and industry-specific regulations
• Regional hosting for data sovereignty

Anne-Claire from Mirakl was clear about this: "We wanted a solution that would fit our security requirements from the start." For a company handling sensitive transactional data, security couldn't be an afterthought.

Building this level of security infrastructure yourself? That's another 6-12 months of development and ongoing compliance work.

Speed to value

If you're measuring success in quarters rather than years, buying gives you immediate capabilities while your competitors are still building. If you're a financial services firm, healthcare company, consulting firm, or any business where AI is a tool rather than the product, your core business isn't AI infrastructure, buying is almost always faster and more cost-effective.

Dario Prskalo from November Five captured this perfectly: "I was able to set up and deploy functional AI agents in just 20 minutes.” Not 20 minutes to a proof of concept. Minutes to agents that his team could actually use. That's the difference between building and buying.

Continuous improvement without ongoing investment The AI landscape changes monthly. Platforms like Dust ship new capabilities, model access, and integrations continuously. Building means maintaining your own upgrade path forever.

At CMI Strategies, a consulting firm of 100 people, they achieved:

• 95% adoption across all consultants
• 60-70% time savings on commercial proposals
• 50% faster executive summary production

But here's what matters: they didn't have to build any of this. And when Dust ships new features like improved model access, advanced permission controls, or new integrations, CMI Strategies gets them automatically.

Bastien Hontebeyrie, Principal at CMI Strategies, explained the economics: "Monthly costs of 30-40 euros per consultant generate multiple hours of weekly time savings, creating obvious ROI before considering quality improvements."

The middle path: The integration trap

Some companies try a middle approach: buy a basic AI platform and build custom integrations and workflows on top.

This seems like a compromise, but it often combines the worst of both worlds:

• You still need dedicated engineering resources
• You're dependent on the platform's API limitations
• You maintain custom code that breaks when the platform updates
• You don't get the full benefit of the platform's built-in capabilities

Watershed took a different approach. Instead of building custom integrations, they embraced Dust's full platform and focused on high-value use cases. The result? Widespread adoption across sales, engineering, and operations without maintaining any custom code. As Jonathan Coveney from Watershed explained: "The biggest success has been as a general-purpose tool that many people have been able to use across different functions."

What we've learned from thousands of successful deployments

The companies that succeed with AI agents share common patterns:

They move fast 

Ardabelle went from evaluation to 95% adoption in 90 days. Wakam hit 70% adoption within two months.  CMI Strategies achieved measurable ROI within weeks. Speed matters because the AI landscape is changing rapidly.

They focus on adoption, not features 

Mirakl's goal wasn't to build the most sophisticated AI infrastructure. It was to transform 75% of their employees from users into builders. That required a platform that made agent creation accessible.

They treat AI as a strategic capability, not a technical project 

At Creative Force, the People team led their AI transformation, not IT. They recognized that AI adoption is a change management challenge, not just a technical one.

They maintain focus on their core business 

Every company we've worked with that chose to buy had the same realization: their competitive advantage wasn't AI infrastructure. It was their product, their customers, their domain expertise.

Where should your best talent focus?

The buy versus build decision for AI agents isn't about whether your team can build it. It's about whether that's the best use of your resources.

That's the real question. Not whether you can build AI infrastructure, but whether building it brings more value to your customers than focusing on your core business.

For the vast majority of companies, the answer is clear: buy the infrastructure, build the competitive advantage.

The companies that will win with AI are the ones that deploy AI agents fast, achieve widespread adoption, and focus their engineering talent on their actual competitive advantage.

Want to see how fast you can deploy AI agents? Most companies go from evaluation to production agents in under 30 days with Dust. Talk to our team about your specific use case.