LLM Mesh
The common backbone of generative AI — connecting and governing multiple LLMs through one secure gateway.
A central layer that connects, manages, and governs multiple LLMs, letting you switch and mix models by cost, performance, and policy without vendor lock-in. It unifies commercial, cloud, and open-source LLMs behind a vendor-neutral gateway.
What Is LLM Mesh
Once an organization adopts generative AI, it quickly ends up using multiple LLMs — commercial, cloud, and open-source — and cost, security, and governance become dramatically more complex. LLM Mesh is a central layer that connects, manages, and governs these models, letting you switch and mix them by cost, performance, and policy without vendor lock-in. Applications talk only to the Mesh, while the Mesh handles actual model selection, routing, and control — the common backbone of generative AI.
Key Capabilities
Vendor-neutral gateway
Unifies commercial, cloud, and open-source LLMs behind a single gateway
A single point of control
Routing, usage limits, cost tracking, and audit logs in one place
LLM Guard Services
Cost / Safe / Quality Guards manage cost, safety, and quality
RAG integration
Connects with Prompt Studio, Knowledge Bank, and Vector Stores
LLM Guard Services
| Guard | Role |
|---|---|
| Cost Guard | Tracks per-model usage and cost, and enforces caps |
| Safe Guard | Filters PII and harmful content for safe inputs and outputs |
| Quality Guard | Evaluates and monitors response quality (LLM-as-a-judge) |
What It's Used For
Standardizing multi-LLM usage
Central control over LLM usage that varies by department and app
Cost & security governance
Manage usage, cost, and sensitive data from a single point
A secure RAG backbone
The common gateway for internal knowledge-based generative AI apps