MCP Gateway vs MCP Proxy vs MCP Server: Key Differences
Compare MCP gateway vs MCP proxy vs MCP server architectures. Learn how each layer fits into production AI agent stacks and where Bifrost fits in.
Latest
Understanding LLM Guardrails and How to Implement Them for Enterprise AI
LLM guardrails enforce content safety, PII protection, and policy compliance for enterprise AI. Learn how Bifrost implements them at the gateway layer.
LLM guardrails are the runtime controls that validate every prompt and response flowing through an enterprise AI application, blocking harmful content, redacting sensitive data, and enforcing policies before
Top 5 MCP Gateways for Production AI Workloads in 2026
Compare the top MCP gateways for production AI workloads in 2026 on performance, governance, audit, and tool orchestration for enterprise AI agents.
The Model Context Protocol (MCP) has moved from a December 2024 specification to the default integration layer for production AI agents in less than 18 months. Choosing the
MCP Proxy Server Explained: Architecture and Use Cases
Learn what an MCP proxy server is, how the architecture works, and the production use cases where it secures and scales AI agent tool access.
An MCP proxy server sits between AI clients and the external tool servers they need to call, brokering every tool discovery, authentication step, and execution
Top 5 Enterprise MCP Gateways in 2026
Compare the top enterprise MCP gateways for production AI agents in 2026 on governance, performance, audit, and tool orchestration for agentic workloads.
Enterprise MCP gateways have become the default control plane for AI agents that read repositories, query databases, and execute workflows on behalf of users. The Model Context Protocol
5 Best Practices to Optimize Your LLM Costs in Production
Optimize LLM costs in production with five gateway-level practices: semantic caching, model routing, MCP Code Mode, virtual keys, and observability.
Monitoring Latency and Cost in LLM Operations: Essential Metrics for Success
A practitioner's guide to monitoring LLM latency and cost in production, covering trace-level observability, tail percentiles, token accounting, semantic caching, and gateway-level governance.
TLDR
User experience and unit economics in production AI hinge on LLM latency and cost. Effective monitoring rests on end-to-end traces,