You're building an AI agent that needs to read files, search the web, and query your database. Model Context Protocol (MCP) standardizes how agents discover and use these tools.
But you quickly discover that running MCP servers directly in production creates problems: no observability into what tools your agents actually use, no security controls over dangerous operations, and no centralized way to manage dozens of tool integrations.
MCP gateways solve these problems. This post compares the top 5 solutions based on performance, security, and ease of use.
Why You Need an MCP Gateway
Running MCP servers without a gateway works for demos. Production needs:
Security: Your MCP server can execute arbitrary code. Without a gateway, every agent has full access to every tool. No granular permissions, no audit trail, no way to restrict dangerous operations.
Observability: Direct MCP connections give you zero visibility. You can't see which tools got called, how long they took, or how much they cost. Debugging failures becomes guesswork.
Management: Each agent manages its own MCP server connections. This works with 2-3 tools. At 20+ tools across multiple environments, manual configuration becomes unmanageable.
Gateways centralize security, observability, and management at the infrastructure level.
1. Bifrost by Maxim AI
maximhq
/
bifrost
Fastest LLM gateway (50x faster than LiteLLM) with adaptive load balancer, cluster mode, guardrails, 1000+ models support & <100 µs overhead at 5k RPS.
Bifrost
The fastest way to build AI applications that never go down
Bifrost is a high-performance AI gateway that unifies access to 15+ providers (OpenAI, Anthropic, AWS Bedrock, Google Vertex, and more) through a single OpenAI-compatible API. Deploy in seconds with zero configuration and get automatic failover, load balancing, semantic caching, and enterprise-grade features.
Quick Start
Go from zero to production-ready AI gateway in under a minute.
Step 1: Start Bifrost Gateway
# Install and run locally
npx -y @maximhq/bifrost
# Or use Docker
docker run -p 8080:8080 maximhq/bifrost
Step 2: Configure via Web UI
# Open the built-in web interface
open http://localhost:8080
Step 3: Make your first API call
curl -X POST http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "openai/gpt-4o-mini",
"messages": [{"role": "user", "content": "Hello, Bifrost!"}]
}'
That's it! Your AI gateway is running with a web interface for visual configuration, real-time monitoring…
What it is: High-performance MCP gateway built in Go with comprehensive tool management and observability.
Performance: Sub-3ms latency, 350+ requests/second per core
Why it's fast: Built in Go (compiled language), not Python. Native concurrency, minimal memory overhead, no interpreter.
Key MCP features:
- Connect to any MCP server via STDIO, HTTP, or SSE
- Agent mode: autonomous tool execution with configurable auto-approval
- Code mode: AI writes TypeScript to orchestrate multiple tools
- Expose Bifrost as MCP server for Claude Desktop
- Filter tools per-request, per-user, or per-virtual-key
Security approach:
By default, tool calls are suggestions only. Execution requires explicit API call. This "explicit execution" model prevents accidental dangerous operations.
Enable auto-execution for specific tools:
{
"tools_to_auto_execute": ["web_search", "file_read"]
}
Risky tools (database writes, file deletions) require manual approval.
Setup:
npx -y @maximhq/bifrost
Configure MCP servers via web UI at http://localhost:8080 or edit config.json:
{
"mcp": {
"servers": [
{
"name": "filesystem",
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem", "/allowed/path"],
"type": "stdio"
}
]
}
}
Observability:
- Built-in dashboard shows tool execution in real-time
- Prometheus metrics at
/metrics - OpenTelemetry distributed tracing
- Cost tracking per tool
Best for: You need sub-3ms latency, want comprehensive observability, and don't want to spend days configuring security policies.
Docs:
2. TrueFoundry
What it is: AI infrastructure platform with MCP gateway built-in.
Performance: 3-4ms latency, 350+ RPS per core
What makes it different: TrueFoundry isn't just an MCP gateway. It's a complete AI infrastructure platform that also manages models, deployments, and monitoring.
Key features:
- MCP server management and deployment
- Model deployment and versioning
- Unified observability across models and tools
- Multi-environment support (dev, staging, prod)
Best for: You want single platform managing both your models AND your tools. You're willing to adopt TrueFoundry's broader infrastructure.
3. IBM Context Forge
What it is: Enterprise MCP federation for large organizations.
Performance: 100-300ms latency (depends on configuration)
What makes it different: Designed for enterprises with 10,000+ employees needing complex multi-tenant governance.
Key features:
- Federation across multiple MCP server deployments
- Enterprise governance and compliance
- Complex routing and policy enforcement
- Multi-tenant isolation
Trade-offs:
- 100-300ms latency (vs sub-3ms alternatives)
- Difficult integration (no official support)
- High operational complexity
Best for: You're a large enterprise requiring federated MCP governance across multiple business units. Latency is acceptable tradeoff for governance features.
4. Microsoft MCP Gateway
What it is: Azure-native MCP gateway with deep Microsoft ecosystem integration.
Performance: 80-150ms latency, cloud-limited concurrency
What makes it different: Built specifically for Azure. If you're already on Azure, integration is seamless.
Key features:
- Deep Azure integration
- Cloud-managed infrastructure
- Azure AD authentication
- Enterprise compliance features
Trade-offs:
- 80-150ms latency
- Azure vendor lock-in
- Complex management interface
Best for: You're already on Azure and want native MCP support without managing your own infrastructure.
5. Lasso Security
What it is: Security-focused MCP gateway with comprehensive threat detection.
Performance: 100-250ms latency (security overhead)
What makes it different: Security-first design. Every tool execution monitored for threats, jailbreaks, and data exfiltration.
Security features:
- Real-time threat detection for AI agents
- Tool reputation analysis (tracks MCP server behavior)
- Jailbreak monitoring
- Data exfiltration detection
- Detailed audit trails for compliance
Trade-offs:
- 100-250ms latency overhead
- High memory usage
- Security adds operational complexity
Best for: You're in healthcare, finance, or other regulated industry requiring comprehensive security monitoring and audit trails.
Performance Reality Check
Latency matters when your agent makes multiple tool calls.
Example: Agent making 50 tool calls per interaction
- Bifrost: 50 × 3ms = 150ms total overhead
- TrueFoundry: 50 × 3ms = 150ms total overhead
- Microsoft: 50 × 80ms = 4 seconds total overhead
- IBM Context Forge: 50 × 100ms = 5 seconds total overhead
- Lasso Security: 50 × 100ms = 5 seconds total overhead
Sub-3ms latency eliminates the gateway from your latency budget. 100ms+ latency makes it the bottleneck.
Quick Comparison
| Gateway | Latency | Setup | Integration | Security | Best For |
|---|---|---|---|---|---|
| Bifrost | <3ms | Very Easy | Drop-in | Granular | Performance + ease of use |
| TrueFoundry | 3-4ms | Easy | Platform | Standard | Unified AI infrastructure |
| IBM Context Forge | 100-300ms | Difficult | Complex | Enterprise | Large enterprise federation |
| Microsoft | 80-150ms | Medium | Azure-only | Azure-native | Azure ecosystem |
| Lasso Security | 100-250ms | Medium | Moderate | Comprehensive | Regulated industries |
How to Choose
Start with Bifrost if you need production-ready performance (sub-3ms), comprehensive observability, and want to get running in under 5 minutes. It's the fastest option and requires zero configuration to start.
Choose TrueFoundry if you want single platform for models and tools. Good if you're already adopting their infrastructure.
Choose IBM Context Forge if you're a large enterprise (10,000+ employees) requiring complex multi-tenant MCP governance. Accept higher latency and complexity for enterprise features.
Choose Microsoft if you're all-in on Azure and willing to accept 80-150ms latency for native integration.
Choose Lasso Security if you're in a regulated industry and need comprehensive security monitoring. The latency overhead is acceptable when compliance requires detailed audit trails.

Top comments (1)
Nice breakdown of MCP Gateways, I appreciate the comparison table! I will bookmark this for later for my projects.