Your CTO just returned from a conference buzzing about MCP. “This is exactly what we need,” she says. “We can finally connect our AI to all our internal systems. I’ll have the team spin up a few servers—should be straightforward with the protocol being standardized and all.”
Two months later, your lead engineer is still debugging why the authentication keeps failing in production even though it works perfectly on his laptop. Your AI initiative, which was supposed to launch last quarter, is stuck waiting for “just a few more integrations.” Meanwhile, your competitor just announced an AI feature that does exactly what you’ve been trying to build.
Sound familiar?
The Model Context Protocol is real, it’s powerful, and it’s becoming the standard. But there’s a significant gap between understanding MCP conceptually and actually deploying it in production. Engineering teams report that each integration requires 2-3 full sprints to complete. Configuration errors remain the number one cause of production failures. And most organizations drastically underestimate what “building a few MCP servers” actually means.
If you’re evaluating an AI initiative that involves MCP, here’s what you’re actually signing up for.
What Actually Goes Into Building an MCP Server
MCP servers sound straightforward. You’re connecting AI to your existing systems using a standardized protocol. There are quickstart libraries. Developer blogs show working weather API examples in 50 lines of code.
But that’s not what you’re building.
Production-ready MCP servers need persistent processes managing sessions, maintaining context, and handling continuous communication between AI and your systems. That 50-line example? It grows to over 1,000 lines once you add authentication, error handling, logging, rate limiting, and the retry logic you need for reliability.
The Timeline: Each MCP integration requires 2-3 full sprints. This includes understanding the protocol, setting up architecture, implementing tool definitions, handling state management, and writing integration logic specific to your systems.
Testing: Production systems need comprehensive testing—experts recommend 400+ tests covering hit rates, success rates, error recovery, and edge cases. Plus security testing for command injection, SSRF vulnerabilities, path traversal attacks, and authorization issues.
Documentation: Here’s a hidden cost: your tool documentation must be clear enough for AI to use effectively. You’re no longer just writing for human developers—you need precise parameter definitions and explicit usage examples that AI systems can parse and understand.
The Numbers: Each integration requires 2-3 full sprints to complete. At typical enterprise rates ($100-$150/hour fully loaded), that translates to roughly $16,000 to $36,000 per integration for a single engineer—though complex integrations with multiple engineers often cost significantly more.
Let’s make this concrete: A mid-sized company needs AI to access their order data from an ERP system, files stored in object storage, PostgreSQL customer database, internal API for inventory management, and their documentation stored in a custom knowledge base. Five integrations. Even if everything goes perfectly, you’re looking at 10-15 sprints and $80,000-$180,000 in development costs alone—and that’s with a single engineer per integration. Assign multiple engineers or encounter any complexity, and costs escalate quickly. This is before considering infrastructure or maintenance.
It’s Not About Time, It’s About Resources
Real development includes planning, code review, testing, and deployment overhead. Each integration takes 2-3 sprints.
You might be thinking: “We’ll just throw more engineers at it. Parallelize the work.”
Sure, you could assign five teams instead of one. You’ll reduce calendar time from months to weeks. But you’ve just committed five engineering teams to integration work. Those same teams could be shipping customer-facing features, improving your core product, or building competitive differentiators.
The person-months are the same whether you do it sequentially or in parallel. The opportunity cost might actually be higher with more teams diverted from product work.
One company building HR system integrations described it this way: “It was the same process, go talk to their team, figure out their API. It was taking a lot of time. And then before we knew it, there was a laundry list of HR integrations being requested.”
The real question isn’t calendar time—it’s resource allocation. Are MCP integrations the best use of your engineering capacity right now?
The Infrastructure You’ll Need to Run
Building the servers is just the beginning. Running them in production requires real infrastructure.
Even a single MCP server needs multiple instances for reliability:
- Development environment: 1-2 instances ($500-$1,500/month)
- Production environment: 5-10 instances with high availability ($5,000-$15,000/month)
- Enterprise scale: 20+ instances with global deployment ($20,000-$100,000+/month)
Why multiple instances? Production systems need redundancy for uptime, load balancing for performance, and geographic distribution for global users. The persistent memory and state management requirements mean each instance runs 30-50% more expensive than traditional API hosting.
Back to our five-integration example: at production scale, you’re now managing 25-50 server instances. Ten integrations? You’re at 50-100 instances.
Maintenance consumes 10-20% of your annual engineering capacity—just keeping existing servers running, updated, and secure. Configuration errors cause most production failures: incorrect endpoints, mismatched credentials, missing environment variables. Things that work fine on a laptop break under production concurrency and network latency.
What Happens When You Have More Than a Few Servers
Beyond individual server costs, organizational problems emerge as you scale.
The Server Sprawl Problem:
Without central coordination, different teams build MCP servers independently.
One organization discovered they had three different MCP servers all accessing their customer database: one built by the checkout team for order data, another by analytics for reporting, and a third by customer service for their support dashboard. Each had slightly different capabilities. None of the teams knew about the others. No central catalog. Unclear ownership.
When the database schema changed, only one server got updated. AI queries started returning inconsistent data across different teams. It took weeks to diagnose why their AI assistant was giving different answers depending on which tool it used.
This happens at scale. Organizations end up with hundreds of little-managed servers scattered across teams. Which one should the AI use? Which is up-to-date? Which is maintained? Nobody knows.
Security Grows Exponentially:
Every new server is a new attack surface. Command injection vulnerabilities. SSRF attacks. Path traversal issues. Community servers from GitHub introduce supply chain risks. Authentication and authorization complexity compounds with each addition.
Tool Management Gets Messy:
Different servers define similar tools with slightly different names. Conflicting endpoint definitions cause failures. Poor documentation makes tools unusable by AI—you’ve built the capability, but the AI can’t figure out how to use it properly.
Adding It All Up
When you factor in everything, here’s what building and running MCP infrastructure actually costs:
Per Integration:
- Development: $16,000-$36,000+ per server (single engineer)
- Timeline: 2-3 sprints minimum
- Costs multiply with multiple engineers or complexity
Monthly Infrastructure:
- Small-scale/Development: $500-$5,000/month
- Mid-sized production: $7,000-$20,000/month
- Enterprise deployments: $29,000-$68,000+/month
Ongoing Costs:
- Maintenance: 10-20% of annual engineering capacity
- AI specialists: $50,000-$150,000+ annually per role
Hidden Costs:
- Server sprawl and governance overhead
- Security reviews and compliance
- Opportunity cost: features not shipped while building infrastructure
These aren’t toy examples or proof-of-concepts. These are operational deployments with multiple servers, production instances, monitoring, and security.
What This Means for Your Decision
If you’re a CTO, VP of Engineering, or business leader evaluating AI initiatives, understand what you’re committing to when building MCP servers from scratch:
- Months of development time per integration
- Significant initial and ongoing costs
- Infrastructure and maintenance burden
- Security and governance challenges
- Engineering resources diverted from core product work
This isn’t an argument against MCP. The protocol is solid and becoming the industry standard. For organizations with truly unique requirements, building custom servers makes sense.
But most businesses don’t need to build this infrastructure from scratch. They need a practical way to connect their systems to AI without consuming months of engineering time and hundreds of thousands of dollars.
The question isn’t whether MCP is the right protocol. It is.
The question is whether building everything yourself is the right approach.
In Part 3, we’ll introduce MCP Express—a solution designed to eliminate the infrastructure burden we’ve discussed here. We’ll show you how to get MCP servers up and running in hours instead of weeks, without sacrificing control or flexibility. You’ll see exactly what’s possible when you don’t have to build everything from scratch.
The bottom line: MCP is the future of AI integration. But you don’t have to build the infrastructure yourself.
References and Excellent Resources
- Merge.dev Blog – “6 Challenges of Using MCP”
- UTCP Blog – “Why MCP Servers Are a Nightmare for Engineers”
- Infracost Glossary – “MCP Servers”
- Arsturn Blog – “The Real Cost of Running MCP Servers at Scale”
- Arsturn Blog – “Why MCP Servers Keep Failing in Production”
This is Part 2 of our MCP in Practice series exploring how MCP is changing AI integration in business.
Oct 1, 2025