Build or Buy an MCP Server? A Guide for Enterprise AI Teams

TL;DR

• An MCP server enables AI systems to access live enterprise data securely and contextually. This ensures AI outputs align with real-time permissions, roles, and workflows.

• Building an MCP server in-house may appear attractive for control. But it quickly becomes long-term infrastructure that requires continuous maintenance, governance design, security management, and scaling.

• As specifications evolve and integrations expand, internal teams often underestimate the operational and security burden of running a production-grade MCP server.

• A managed MCP server reduces engineering overhead, accelerates time to market, and allows teams to focus on differentiated workflows instead of maintaining protocol infrastructure.

• For most DAM vendors, system integrators, and enterprise IT teams, buying a managed MCP server delivers faster value and lower long-term risk than building and operating one internally.
  
  

Introduction

Enterprise AI is moving from experimentation into real deployment, and with that transition comes a critical architectural decision for DAM vendors, platform providers, and system integrators: Should you build your own MCP server or use an existing one? According to Gartner, more than 80% of enterprises are expected to use generative AI APIs or deploy AI-enabled applications by 2026, which means AI infrastructure decisions are becoming urgent rather than optional.

Many companies are currently exploring whether to create their own Model Context Protocol (MCP) server to connect AI systems with enterprise data, DAM platforms, and business tools.

On the surface, building sounds attractive, offering full control, custom logic, and tailored use cases. But in practice, MCP servers quickly become long-term infrastructure products. They need to scale, remain secure, evolve with emerging standards, and support far more use cases than initially planned.

What an MCP Server Actually Does

The Model Context Protocol defines a standard way for AI systems to safely access tools and data. An MCP server sits between large language models and controlled, structured way.

This includes retrieving assets and metadata, understanding permissions and user roles, executing actions beyond just answering questions, and returning results in the context of where the user is actually working.

This matters because most early-stage AI and digital asset management integrations struggle dramatically once they move beyond polished demos. Static prompts, brittle APIs, or one-off RAG pipelines are difficult to govern and nearly impossible to scale safely across an organization.

MCP fundamentally shifts AI from "informed guesswork" based on training data to context-aware interaction with real, live systems. Instead of asking an AI generic questions, users can ask it to find the right brand asset, un to use it, retrieve related data, and execute the next action, all within their existing workflow.

Why Enterprise AI Needs an MCP Server

As organizations roll out AI across teams and tools, three pressures consistently emerge and define what MCP must solve.

1) AI Needs Live Data 

Brand assets, campaigns, product information, and customer content change constantly. A static knowledge base becomes outdated almost immediately. An AI system trained on last month's brand guidelines will confidently recommend yesterday's color palette. This gap between what AI knows and what's actually current creates both compliance risk and customer frustration.

2) Context Matters Deeply 

An AI response inside PowerPoint should not be the same as an AI response inside Salesforce. Context includes the application in use, the task at hand, the user's permissions and role, and the content they're working with.

A sales team needs different asset recommendations than a creative team. A contractor should not see the same information as a full-time employee. Context-blind AI either becomes useless or creates security problems.

3) Scale Changes Everything

What works for a pilot group of 50 designers rarely works for 50,000 employees. Governance, access control, monitoring, cost management, and predictability become non-negotiable. At scale, even small security gaps become significant, and performance degradation affects hundreds of users simultaneously.

MCP servers exist specifically to address these challenges. But how they are implemented and operated determines whether they solve the problem or become the problem.

Build vs Buy an MCP Server: Side-by-Side Comparison

Choosing whether to build or buy an MCP server is not just a technical decision. It affects time to market, engineering focus, governance, and long-term scalability. The table below highlights the practical differences.

Challenges of Building an MCP Server In-House

Building an MCP server might start out as a normal development task, but it will inevitably become the creation of a platform component that must be operated, scaled, and maintained over the years. This distinction matters because many organizations underestimate what ownership actually entails. Here’s why:

MCP Itself is Still Evolving

Right now, the specification is young, tooling continues to improve, and best practices are still emerging. When you build your own server, you own that evolution entirely. You must track specification changes, update implementations, retest integrations, and avoid breaking changes to systems that depend on your server.

Integration Complexity Adds up Quickly

A basic MCP demo takes days. An enterprise-ready MCP server that handles production workloads is fundamentally different. Each new system brings its own authentication model, permission structure, edge cases, and failure modes. You need to handle multiple data sources, granular permission mapping, error recovery, rate limiting, logging, and auditing.

In short, what starts as "we'll connect our DAM and a CRM" often expands to multiple systems, and each addition multiplies complexity.

Enterprise Security and Governance Cannot be Bolted On

MCP does not automatically solve requirements such as single sign-on, role-based access control, audit trails, tenant isolation, or compliance mappings. These elements must each be designed, implemented, tested, and maintained. They are often the hardest part of enterprise adoption, because they touch everything, including authentication, permission models, logging, data exposure, and regulatory proof.

MCP Server Becomes Production Infrastructure After Deploying

You now own monitoring, incident response, performance tuning, scaling, and support for both internal teams and external partners. At that point, you are no longer "just enabling AI." You are running a critical service. If it goes down, so does AI access across your organization. And if it leaks data, you own the liability.

One use case is never enough. Teams often start with one server for one task. Then come requests for more systems, more AI models, more host applications, more users, more regions, and more security requirements. What started as a focused solution becomes a platform, whether you planned it that way or not. Managing that growth without collapsing under technical debt requires sophisticated platform engineering.

A Step-by-Step Guide to Building an MCP Server

Many teams search for what an MCP server is or how to build an MCP server, and assume it is a manageable engineering task. The high-level steps look simple, but each one hides serious complexity.

• Define the architecture and scope: Start by clarifying the MCP server definition, what systems it will connect to, and how permissions will be handled. Even at this stage, design decisions affect long-term scalability and security.

• Develop core connectors: To create MCP server functionality, you must connect DAM, CRM, and other enterprise tools. Each integration introduces its own authentication model, error handling, and edge cases.

• Implement governance and access control: Proper MCP server implementation requires role-based permissions, audit logging, and compliance safeguards. These controls are difficult to design correctly and even harder to maintain at scale.

• Test performance and reliability: During the building of an MCP server, you must handle load testing, failover planning, and monitoring. What works in a demo rarely works under enterprise traffic.

• Deploy, host, and maintain: Once live, the MCP server hosting becomes ongoing infrastructure management, including updates, security patches, and compatibility with evolving AI standards.
  

On paper, the process for creating an MCP server appears structured and achievable. In reality, it quickly becomes a long-term platform responsibility that requires ongoing engineering investment.

For most organizations, purchasing a managed MCP server eliminates this operational burden and enables teams to focus on delivering real AI value rather than maintaining infrastructure.

When Building an MCP Server Makes Sense

There are legitimate scenarios where building in-house is the right choice. Build if MCP itself is a core part of your product differentiation. It makes sense to build if you already run a mature, well-staffed internal platform team with a track record of building and maintaining long-term infrastructure products.

For most DAM vendors, system integrators, agencies, and enterprise IT teams, however, MCP is an enabling layer rather than a source of differentiation. The value lies in what you build on top of it, such as your workflows, your customer experience, and your industry expertise, but not in maintaining the protocol layer itself.

The Case for Using a Managed MCP Server

Using a managed MCP server shifts responsibility away from internal teams and accelerates delivery. This mirrors the same logic that led many organizations to adopt cloud services instead of building private data centers, or to use CI HUB connectors instead of building and maintaining custom integrations for each system pairing.

Buying an MCP server typically means:

• Faster time to market: Instead of months of development and internal alignment, partners can enable AI-ready workflows more quickly. Customers see value sooner. Revenue opportunities unlock faster.

• Broader connectivity from day one: A managed platform carries integrations with dozens of systems. You gain those immediately, instead of building them incrementally as budgets and priorities allow.

• Ongoing updates without internal rework: As MCP specifications evolve or AI providers change, the platform provider handles compatibility. You don't need to redeploy or retrain your team.

• Reduced operational and security burden: Monitoring, scaling, security patching, and incident response become someone else's responsibility.

• Enterprise governance is built in: Instead of designing and implementing access control and audit logging yourself, you inherit a system designed for enterprise compliance from the ground up.

This frees your team to focus on workflows, user experience, customer value, and the elements that actually differentiate your business.

How CI HUB Bright's MCP Server Changes the Equation

CI HUB Bright extends CI HUB's long-standing connector approach into the AI space. This is not a theoretical product or a sideline feature. Bright works in tandem with CI HUB's existing ecosystem of 70+ connected DAM systems and 30+ host applications, such as Word, PowerPoint, Salesforce, and others, where people actually work.

Here’s what it offers:

• Unmatched connectivity: Instead of building one MCP server per system or managing a patchwork of custom integrations, partners get one unified MCP layer that immediately connects to an established ecosystem. As CI HUB adds new integrations, Bright gains them automatically, without partners needing to do additional work.

• Enterprise governance is built in from the foundation: Bright is designed with role-based access control, governed data exposure, and secure AI usage at scale from day one. Bright leverages existing system permissions and CI HUB’s integration layer to ensure AI interactions respect user roles and access rights across connected systems.

• AI in context, everywhere: Since CI HUB already lives where people work, Bright can deliver AI assistance that is aware of the application, the content, the user, and their role. An AI copilot in Word behaves differently from one in PowerPoint because it understands the context. This level of contextual awareness is extremely difficult to replicate with a standalone MCP server built in isolation.

• CI HUB owns the complexity: With Bright, CI HUB tracks MCP evolution, updates integrations as standards change, handles security and scaling, maintains compatibility with AI providers, and manages long-term reliability. That means your team won’t need to become MCP specialists. They can use MCP as a capability, not as a product they maintain.

• Faster time to value: With Bright, you can enable AI-ready workflows quickly rather than planning a multi-month build. That means your customers will see tangible benefits sooner.
  
  

The Strategic Question to Ask

The real question is not: "Can we technically build an MCP Server?" The real question is: "Is building and maintaining MCP infrastructure the best use of our engineering time and organizational focus?"

For most companies, the answer is no. Your differentiation lies in your platform, your workflows, your customer expertise, and your industry knowledge. CI HUB Bright exists so you don't have to rebuild the foundational connective tissue of enterprise AI. Your team can focus on what you actually excel at instead of maintaining infrastructure that dozens of other organizations also need to maintain.

Final Thoughts

MCP servers will become a foundational part of enterprise AI workflow automation. But foundations are rarely where companies choose to compete or where they should allocate scarce engineering resources.

By using CI HUB Bright's MCP server, your team can move faster, reduce risk, deliver better AI experiences, and focus on what truly differentiates their business. This is the same reasoning that drove the adoption of cloud infrastructure, managed databases, and API platforms over the past decade.

The smartest move for most teams isn't to build everything themselves; it's to build on top of something that's already world-class, proven, and continuously evolving.