If vLLM already solved LLM serving, why did SGLang appear?
Enterprise AI Has an Architecture Problem
Most teams discover this once they try to operationalize the full AI tech stack across data, orchestration, inference, and governance.
One framework trying to change that is MCP.
Model Context Protocol (MCP) is an open standard Anthropic released in November 2024. A shared interface MCP solves tool connectivity, but production still depends on the infrastructure layer described in AI infrastructure as a service, which lets AI models connect to external tools without custom plumbing for every connection.
But knowing what MCP does is different from knowing how to build with it, especially in environments that weren’t designed for AI in the first place.
The Integration Debt Nobody Budgets For
Almost every enterprise AI deployment is embedded in an existing system. There’s a CRM that’s been running for eight years. An ERP that three different teams have customized. A data warehouse built before anyone thought about real-time access.
These systems weren’t designed to talk to each other, let alone an AI layer sitting on top of them.
When a new AI system drops into this environment, the expectation is that it just works. It doesn’t. What usually happens instead:
- Teams build point-to-point integrations: One connector for Salesforce, one for SAP, one for the internal knowledge base.
- It works until someone adds a new tool: Then breaks when an API changes. Breaks again when the model gets updated.
- By the time a team is managing ten or fifteen integrations: A significant chunk of engineering time will be spent on maintenance rather than on anything useful.
The architecture, not the model, is the problem.
Sixty Integrations vs Ten
MCP shifts the architecture from point-to-point to hub-and-spoke.
Instead of building a custom connection between the model and every tool, you build one MCP interface per tool. The model talks to everything through the same protocol.
The practical impact:
- New tools are added by writing a single MCP server, not by creating a custom integration for every model that might need them.
- Model upgrades don’t break tool connections because the protocol sits between them as a stable layer.
- The same MCP servers get reused across every AI application inside the organization.
Suppose each of your six AI applications needs to connect to ten tools.
Point-to-point gives you sixty integrations to build and maintain.
MCP gives you ten servers that all six applications share.
That’s the real productivity argument: not that it makes AI smarter, but that it dramatically reduces the integration surface area teams have to manage.
Keep the Layers Separate or Pay Later
MCP has three layers:
- The Host (the AI application)
- The Client (the MCP layer inside the Host)
- The Server (the tool-specific wrapper).
Each layer has a specific job. The key is keeping them genuinely separate.
The most common failure mode is letting the Host know too much about what the Servers do. When the AI application starts embedding tool-specific logic, you’ve recreated the point-to-point problem inside MCP.
You have the protocol’s overhead without the benefit of decoupling.
The cleaner approach: make each MCP Server responsible for its own context. The Server that wraps your CRM handles authentication, data formatting, and error handling internally. The Host doesn’t know or care how Salesforce works. It just knows that a “customer_history” tool exists and what it returns.
What this buys you in practice:
- Salesforce changes its API structure?
One MCP Server needs updating. Nothing else changes. - Switching CRMs entirely?
Replace one Server. The rest of the system keeps running. - Adding a new model? No integrations to rewrite. It speaks the same protocol that everything else already does.
The Coordination Tax
Single-agent MCP deployments are relatively straightforward.
The complexity scales fast once you introduce multiple agents.
In a common enterprise setup: an orchestrator agent that breaks down a complex task, a retrieval agent that pulls relevant data, a processing agent that analyzes it, and an execution agent that acts on the result.
Each agent needs its own tool access. Each produces a context that the next one depends on. And this needs to run reliably even when individual steps take variable amounts of time.
Without a shared structure, the same problems show up repeatedly:
- Agents duplicate work: Two agents pull the same data because neither knows the other has already retrieved it.
- Context gets lost between handoffs: There’s no consistent way to pass state from one agent to the next.
- One tool call fails: and the whole workflow needs human intervention to restart.
MCP addresses part of this by giving all agents access to the same server layer. But it doesn’t solve the coordination problem on its own. You still need to define how state is shared between agents, how workflows recover from partial failures, and how you prevent agents from stepping on each other when they’re running in parallel. At multi-agent scale, throughput limits become visible fast, and high throughput in inference becomes a practical constraint, not a theory.
The teams that get this right treat orchestration as an integral architectural decision, not something to figure out once the agents are already running.
Governance Built-in, Not Bolted-on
One thing that tends to surprise teams moving from single-model deployments to multi-agent systems is how quickly the auditability question becomes urgent.
In a simple chatbot, you can trace what happened easily. In a multi-agent system with six tools, three agents, and a workflow that runs for forty-five seconds, tracing what happened is genuinely hard. Which agent accessed which tool? What data did it retrieve? Which decision triggered which action?
This matters across the board:
- Compliance teams in regulated industries need a clear record of how decisions were made.
- Engineering teams need to trace the exact sequence of events when something breaks.
- Leadership needs confidence that AI systems doing real things in the world can be audited and explained.
MCP helps here in a way that’s often under-appreciated. Because all tool interactions go through a consistent protocol, they can all be logged in a consistent format. You don’t need custom logging for every integration. Every Server interaction follows the same pattern, meaning observability is built into the protocol layer rather than added as an afterthought.
The result: you can reconstruct exactly what happened in any workflow run, which is what makes compliance manageable rather than impossible.
Infrastructure is a Design Constraint, Not a Supporting Layer
There’s a point in every MCP-based architecture where the design is solid, the protocol is right, and performance still isn’t where it needs to be. That’s usually when teams realize the infrastructure question matters more than they expected.
Agentic workflows are punishing on compute in ways simple inference isn’t:
- A single user request might trigger eight to ten tool calls, each adding latency.
- The model holds context across all of them while continuing to reason, not just respond.
- This isn’t happening for one user. It’s happening for dozens or hundreds concurrently.
Shared GPU infrastructure, where most teams start, handles this poorly.
When concurrent sessions compete for the same pool, latency becomes unpredictable, with effects that compound across multi-step workflows.
A 200-millisecond variance on a single tool call becomes a two-second variance across a ten-step agentic loop.
Neysa’s Velocis platform runs on dedicated GPU clusters to avoid exactly this. Isolated compute, low-latency memory access, and no pool contention mean agentic sessions maintain consistent performance as workflows become more complex. Which is what makes multi-agent architectures production-viable rather than demo-viable.
Build AI Agents That Scale
Support complex agentic workflows with infrastructure built for low-latency, reliable performance.
What Production-Ready Really Means
The teams getting the most out of MCP-based architectures have shifted how they frame the problem. The question isn’t “how do we connect this model to these tools?” It’s “how do we build a system that can evolve without constant rebuilding?”
That shift changes what you optimize for:
- Modular server design over tight integration that creates rebuild cycles every time something changes.
- Shared observability over per-system logging that fragments your view of what’s actually happening.
- Infrastructure that supports long-running agentic sessions, versus infrastructure tuned for fast single-turn responses.
MCP gives you the protocol layer. The rest comes down to what you build around it and whether your infrastructure can hold the weight of what you’re asking it to run.
