Solutions
MCP & LLM Gateway
As AI applications multiply, so do the questions: which model is calling which tool, under what policy, at what cost, and who's watching.
We design MCP-based tool integration and LLM gateway architectures that give you a governed, observable layer between models and the systems they touch.
What it is
Model Context Protocol (MCP) and LLM gateways solve two related but distinct problems, and we design them accordingly rather than treating them as interchangeable.
MCP is a pattern and protocol that can connect AI applications or models to tools, data sources, and workflows in a structured, reusable way — it standardizes how a model requests access to a capability, such as a database query or an internal API.
An LLM gateway is a control layer for model access itself: routing requests across providers and models, enforcing policy, managing cost, logging for audit, and providing resilience if a provider degrades or becomes unavailable.
Where it helps
MCP integration helps when multiple AI applications need consistent, reusable access to the same internal tools and data sources, reducing one-off point integrations. An LLM gateway helps once an organization runs more than a handful of models or applications and needs centralized visibility, cost control, and policy enforcement across all of them.
What PacificTahoe does
We assess whether MCP, a gateway, or both are the right fit for your environment, then design and implement the integration: tool and resource definitions for MCP, and routing, authentication, and policy configuration for a gateway.
Access control and governance are treated as implementation requirements we design in from the start — MCP is not inherently secure by default, and a gateway is only as effective as the policies configured on it. Both require deliberate design of authentication, authorization, and monitoring.
- MCP server and tool integration design for internal systems
- LLM gateway selection, deployment, and routing/policy configuration
- Access control, rate limiting, and audit logging design
- Cost visibility and provider failover strategy
Integration considerations
- Which internal systems and tools should be exposed to AI applications, and to what extent
- How multiple models or providers should be routed and load-balanced
- Existing API management, identity, and secrets infrastructure
Governance & security considerations
- Authentication and authorization for every tool and data source exposed via MCP
- Policy enforcement, rate limits, and cost controls at the gateway layer
- Comprehensive audit logging for every model and tool call
Running more than one model or AI application?
Let's talk about where governance and access control need to sit.