What Are Model Control Planes (MCPs) in AI?

AI development is evolving fast. Teams aren’t just deploying one model anymore. They’re working with many—each fine-tuned for different use cases, often hosted on different platforms, with varying constraints, performance profiles, and compliance requirements. This diversity brings new power, but also new complexity. And without centralized control, that complexity quickly becomes chaos.

Model Control Planes (MCPs) are the emerging solution. They act as the coordination layer that sits between your AI applications and the models they rely on. Whether you’re routing traffic across multiple LLMs, enforcing access controls, or analyzing request logs, MCPs offer a structured, scalable way to manage how models are used in production.

This primer explains what MCPs are, how they work, and why they’re becoming a foundational piece of modern AI infrastructure.

What is a Model Control Plane (MCP)?

A Model Control Plane (MCP) is a centralized layer that manages and governs how AI models are used in production.

Instead of letting applications call individual models or LLM APIs directly, MCPs act as the traffic cop between your apps and your models. They decide where requests go, track what’s happening, and enforce rules.

MCPs differ from MLOps tools like MLflow or Kubeflow, which focus on training and deployment. MCPs step in after that, managing live traffic during inference. They also go beyond simple inference gateways by supporting multi-model routing, policy controls, logging, and usage tracking.

Why MCPs Matter in AI Applications

MCPs are not just a nice-to-have—they’re increasingly a necessity. The more your organization relies on generative models or custom AI workflows, the more you’ll need a way to control access, trace behavior, and balance performance with cost.

Here’s a breakdown of the key problems MCPs address, and why they matter:

With an MCP, you can control which model runs for a request, log every interaction, and swap models without touching application code. It’s about moving from reactive fixes to proactive control.

Core Capabilities of MCPs

Dynamic Routing and Version Control

An MCP can determine which model should handle any given request based on context—user type, input size, cost tier, or region. You can run A/B tests between model versions, roll out new models gradually, or fall back to an alternative model if the primary one fails. These controls allow organizations to optimize for performance, reliability, and cost without touching application code.

Version control is another critical layer: MCPs help teams manage which model version is live, in test, or staged for release. They support rollbacks and controlled promotions, adding guardrails to how models enter production.

Observability and Telemetry

MCPs offer deep visibility into model usage. They log every request and response, including prompt inputs, outputs, latency, token consumption, and error rates. This central log is essential for debugging unusual behavior, monitoring for regressions, and auditing usage across teams and services.

Many MCPs also provide built-in dashboards or export telemetry data to external analytics platforms. With this observability, teams can compare model performance over time, spot anomalies early, and correlate outcomes with model changes.

Policy Management and Governance

MCPs allow teams to define and enforce access policies for model usage. You can use role-based access control (RBAC) or attribute-based access control (ABAC) to limit who or what services can invoke specific models. These controls are particularly important in enterprise and regulated environments where privacy, compliance, or ethical guidelines must be followed.

Beyond permissions, governance also includes policy enforcement around geography (e.g., data residency), usage caps, or human-in-the-loop checks for sensitive outputs. These controls make the MCP a key part of responsible AI operations.

Shadow Testing and Feedback Integration

To validate changes before they go live, MCPs support shadow testing—sending real traffic to a new model version without exposing its outputs to users. This lets you measure how a candidate model behaves under real-world conditions.

Canary testing is another built-in mechanism: gradually increase traffic to a new model and monitor for regressions. MCPs log these runs and provide performance comparisons, helping teams make informed decisions about model promotions.

Some MCPs also support structured feedback collection—user ratings, flagging poor outputs, or integration with business metrics. This data can feed directly into model evaluation or retraining workflows.

Multi-model Orchestration

MCPs simplify scenarios where more than one model or tool is involved in a pipeline. For example, a single user request may require generating an embedding, querying a vector database, invoking a generative model, and post-processing the result.

Rather than hardcoding all this into your app, the MCP orchestrates these steps. It keeps track of dependencies, sequences calls, and enforces routing rules. This makes it easier to build Retrieval-Augmented Generation (RAG) systems, agentic applications, or hybrid workflows that combine internal and external models.

With orchestration in place, your infrastructure becomes more modular, testable, and maintainable.

Where MCPs Fit in the AI Stack

An MCP sits between the application layer and the model execution layer.

It connects client apps, agent frameworks, and RAG pipelines to LLMs, embedding models, and other tools. It makes model usage safe, observable, and manageable—without every developer needing to learn the details of each model provider.

Common Use Cases for MCPs

• LLMOps: MCPs play a central role in managing multiple large language models (LLMs) in production. They can route requests based on task complexity, prompt size, or user tier, helping balance performance and cost. If you’re running both general-purpose and fine-tuned models, the MCP ensures each gets used in the right context. This also streamlines A/B testing new models and monitoring version-specific behavior.
• Enterprise Governance: In sectors like finance, healthcare, or legal services, compliance is non-negotiable. MCPs provide access controls, logging, and policy enforcement, making it easier to meet regulatory obligations. For example, an MCP might restrict sensitive data to internal models only, or ensure EU-based users are served exclusively from EU-hosted endpoints.
• Agent Orchestration: As AI agents become more capable, they also become more complex—often chaining together models, tools, and APIs. MCPs serve as the coordination layer, routing tool calls, enforcing usage policies, and logging all activity. This improves traceability and safety, especially in systems that let agents take semi-autonomous actions.
• Real-Time Systems: Customer support bots, real-time analytics, and user-facing AI services can’t afford downtime. MCPs support failover logic—if one model is slow or offline, traffic is instantly redirected to a backup. You can also prioritize certain traffic, ensuring critical requests get handled first, while lower-priority tasks are delayed or throttled as needed.

Challenges and Considerations

While MCPs offer critical capabilities, they also come with challenges that teams should weigh carefully:

• Latency and Performance Overhead: Routing requests through a control plane introduces an extra hop in the network path. Even with efficient implementations, this can add a few milliseconds of latency. For real-time applications or latency-sensitive systems, this overhead must be benchmarked and minimized.
• System Complexity: Adding an MCP increases architectural complexity. Teams must manage another component in their stack, monitor its health, and troubleshoot new potential failure modes. If not well-integrated, this complexity can slow down development.
• Vendor Lock-In: Many commercial MCP solutions offer proprietary APIs or configurations. Migrating away from these systems can be difficult once your applications rely heavily on their specific behaviors. Open standards and exportability are important factors to evaluate.
• Security and Privacy Risks: The MCP sees all inputs and outputs to your models, including potentially sensitive data. If using a managed service, this can raise compliance concerns. Even self-hosted setups must be tightly secured to prevent misuse or data leakage.
• Integration Effort: MCPs need to integrate with various systems—identity providers for access control, observability tools for logging, CI/CD for rollout automation. These integrations take time and planning. Without them, the control plane can become isolated or underused.
• Immature Ecosystem: MCPs are still a relatively new concept. Tooling varies widely in feature completeness, stability, and community support. Best practices are still evolving, which means teams may need to experiment or even build custom logic to meet their needs.
• Cultural and Organizational Resistance: Introducing centralized control over model usage may be seen as bureaucracy by some teams. Adoption often requires clear communication of benefits and collaborative rollout across data science, engineering, and security stakeholders.

For teams with multiple models, strict compliance needs, or cross-functional AI workflows, the benefits of adopting an MCP often outweigh these challenges—but only with the right planning and infrastructure support.

Tools and the Industry Landscape

The MCP ecosystem is evolving rapidly, with a growing set of commercial platforms, open-source tools, and cloud-native offerings. These fall into several broad categories:

• LLM Gateways and Middleware Platforms: These tools specialize in routing, abstraction, and centralized control across LLM providers.
  • TrueFoundry: Offers an LLM gateway that supports over 100 models and APIs. It emphasizes flexibility and can be deployed on Kubernetes, making it suitable for teams needing on-prem control.
  • Portkey: Middleware for routing between model APIs with support for fallback, caching, and logging. It’s lightweight and easy to integrate, often used by teams just starting to manage multiple providers.
• Model Hosting and Infrastructure Platforms: These tools handle deployment, scaling, and inference infrastructure and sometimes layer in control plane capabilities.
  • Baseten: A platform for hosting and managing models with a clear separation between control plane and workload plane. It’s built for scalability and supports multi-region, multi-cloud setups.
  • Modal: A serverless infrastructure for ML, allowing teams to spin up model backends on demand. While not a full MCP, it’s often a key component in a larger setup.
• Observability and Tracing Tools: These tools focus on logging prompts and responses, tracking model performance, and surfacing insights from usage data.
  • Helicone: Acts as a proxy to log API calls to LLMs, showing latency, token use, and more. It’s now evolving toward full MCP capabilities.
  • Langfuse: A self-hostable tracing platform that integrates with frameworks like LangChain. It allows developers to trace prompt flows, capture feedback, and compare prompt versions.
• Cloud Provider Platforms: Major cloud vendors are building MCP-like features into their AI stacks.
  • AWS Bedrock: Offers access to foundation models with added governance and monitoring layers, integrated within the broader AWS environment.
  • Google Vertex AI: Combines model hosting, prompt management, observability, and policy enforcement under a unified control interface.
• Experimental and Open Source Projects: The space is fertile with innovation. Tools like BentoML, Ray Serve, and KServe are often used to build custom MCP-like solutions. Other projects like LiteLLM and Martian are experimenting with LLM load balancing and routing.

Together, these tools represent a fast-developing market. Some are focused end-to-end MCP platforms; others provide key building blocks. The choice depends on whether you’re prioritizing full-stack integration, flexibility, observability, or ease of deployment. like AWS Bedrock and Google Vertex AI offer vertically integrated versions. Startups like Modal and Arize focus on deployment and observability pieces of the puzzle.

What’s Next for MCPs?

The Model Control Plane space is moving fast—and with good reason. Several forces are accelerating development and adoption across the AI ecosystem:

• Explosion of Model Choice: The rise of foundation models, open-source alternatives, and proprietary APIs has created a fragmented model landscape. MCPs help unify access and provide abstraction, so developers don’t have to lock into a single provider or hardcode endpoints.
• Increasing Operational Demands: As more AI apps move from prototype to production, the need for control, stability, and performance management increases. MCPs bring the kind of runtime control and auditability that modern infrastructure teams expect.
• Emphasis on Responsible AI: Enterprises and regulators are demanding better oversight and accountability. MCPs support compliance through access control, logging, and policy enforcement—making them essential for scaling responsibly.
• Toolchain Maturation: Tools like LangChain, vector databases, and agent frameworks are gaining traction. MCPs integrate with or even coordinate these components, helping standardize workflows and reduce integration complexity.
• Demand for Observability and Cost Control: AI workloads are expensive and opaque. MCPs give teams visibility into usage patterns, help optimize model selection, and track spend in real time.

Looking forward, expect the next generation of MCPs to incorporate:

• Intelligent Routing: Models will be selected based on past performance, user profiles, or cost models—possibly using meta-models or feedback loops.
• CI/CD Integration: Tighter connection between model deployment workflows and control plane logic, supporting canary releases and rollback automation.
• Support for Multi-Modal Workloads: Beyond text, MCPs will orchestrate models for images, audio, video, and mixed pipelines.
• Open Standards: The ecosystem is moving toward shared schemas and interfaces for observability, access control, and routing.

MCPs are evolving from a nice abstraction into essential infrastructure for modern AI—enabling both agility and governance at scale.

FAQ

What’s the difference between an MCP and MLOps tools?

MCPs manage runtime inference and routing. MLOps tools focus on training and deployment.

Is an MCP useful if I only use one model?

Maybe not yet. But if you plan to experiment, enforce access rules, or log usage, an MCP helps.

How does an MCP help with compliance?

It logs every model call, enforces access control, and supports region-based routing for data residency.

Can I build my own MCP?

Yes, but it’s non-trivial. Many teams start with tools like Langfuse or Portkey and evolve from there.

How does an MCP work with RAG pipelines?

It can manage each step: embedding, retrieval, generation. It also logs and traces all requests.

What happens if one model fails?

The MCP can reroute the request to a fallback model or return a cached result.

Are MCPs just for LLMs?

No. They can also manage vision models, audio models, or tools in an agent system.

Further reading…

1. Understanding MCP Architecture – DEV.to
2. Portkey Alternatives – TrueFoundry
3. Baseten: Control Plane vs Workload Plane
4. ZenML Cloud Features
5. Control Plane as a Tool – arXiv
6. LLM Observability – Arize AI