Top 5 MCP Gateways in 2025: The Complete Guide to Enterprise-Ready AI Agent Infrastructure

TL;DR: Model Context Protocol (MCP) gateways have become essential infrastructure for production AI agents. This guide evaluates the top 5 MCP gateway solutions in 2025: Bifrost leads with its developer-first approach and comprehensive feature set including sub-3ms latency, built-in tool registry, and seamless integration capabilities. TrueFoundry offers unified AI infrastructure management, IBM Context Forge provides sophisticated federation for large enterprises, Microsoft's solution integrates deeply with Azure, and Lasso Security focuses on threat detection. Bifrost stands out for teams prioritizing speed, flexibility, and production readiness.

In late 2024, engineering teams across enterprises hit a common wall. AI agents could write code, analyze data, and generate reports beautifully in demos, but connecting them to real tools like Slack, Jira, and internal databases turned into integration nightmare. Authentication flows, security reviews, and custom API wrappers consumed months of development time.

Anthropic's Model Context Protocol (MCP), released in November 2024, promised to solve this by standardizing how AI agents discover and interact with tools. Instead of building custom integrations for every API, MCP provided a unified protocol for tool communication. But as teams rushed to adopt MCP, they discovered a new challenge: managing MCP servers at scale introduced operational complexity that the base protocol doesn't address.

This is where MCP gateways enter the picture. These aren't simple proxy servers but complete control planes that transform MCP from a protocol specification into production-ready infrastructure. They handle security isolation, comprehensive observability, centralized management, and performance optimization that enterprises cannot ignore.

In this blog, we're examining five solutions that represent fundamentally different approaches to MCP gateway architecture. Each solves the same critical problem through distinct design philosophies, making the choice highly dependent on your specific infrastructure requirements and team priorities.

Why MCP Gateways Are Non-Negotiable for Production

Before diving into specific solutions, understanding the core problems MCP gateways solve helps frame the evaluation criteria. Running MCP servers directly works for individual use cases or prototypes, but it exposes three critical gaps in production environments:

Security Vulnerabilities: MCP servers execute with whatever permissions you grant them. As your agent ecosystem grows from a handful to dozens of tools across multiple environments, managing authentication, role-based access control, container isolation, and security groups becomes unmanageable. A single misconfigured MCP server can expose sensitive data or enable unauthorized actions.

Observability Black Holes: Direct MCP connections provide zero insight into what agents actually do with your tools. Without structured logging, performance metrics, and cost tracking, debugging failures becomes guesswork. When an AI agent makes 50 tool calls across 10 different services, understanding where things went wrong requires comprehensive tracing.

Operational Chaos: Each MCP server needs its own deployment, monitoring, versioning, and maintenance. Multiply this by dozens of tools across development, staging, and production environments, and operational overhead spirals quickly. Teams need centralized control without sacrificing flexibility.

MCP gateways solve these by providing security isolation through containerization or sandboxing, comprehensive observability with distributed tracing and metrics, and centralized management for all MCP servers. The difference between solutions lies in how they architect these capabilities.

1. Bifrost: Developer-First MCP Gateway with Production-Grade Performance

Core Philosophy: Empower developers to build AI agent infrastructure as fast as they ship features, without compromising on security, performance, or observability.

Bifrost represents a fundamentally different approach to MCP gateway architecture. Rather than treating MCP as an isolated capability requiring separate infrastructure, Bifrost integrates it as a native feature of a high-performance AI gateway. This design decision unlocks significant advantages for teams building production AI agents.

The Performance Advantage: Sub-3ms Latency at Scale

Performance isn't just about raw speed but about predictability under load. Bifrost achieves sub-3ms latency overhead for MCP operations through intelligent in-memory processing. When agents make hundreds of tool calls per conversation, milliseconds compound into seconds of user-facing delay.

The architecture handles 350+ requests per second on a single vCPU without tuning, achieved through asynchronous execution patterns and zero-copy message passing. This matters particularly for conversational AI applications where response time directly impacts user experience.

Stateless Architecture: Security by Design

Bifrost's stateless design fundamentally changes how teams think about AI agent security. Unlike traditional MCP implementations that maintain persistent connections with ongoing state, Bifrost treats every API call as independent.

Here's how it works in practice:

1. Discovery Phase: Bifrost connects to configured MCP servers and discovers available tools
2. Suggestion Phase: Chat completion requests return tool call suggestions without executing them
3. Review Gate: Your application reviews tool calls and decides which to execute
4. Explicit Execution: Separate API calls execute specific tool calls with full audit trails
5. Assembly Phase: Your application manages conversation state and assembles chat history

This architecture prevents unintended API calls, accidental data modifications, and execution of potentially harmful commands. Each tool execution requires explicit approval, creating natural audit points for compliance and security reviews.

// Bifrost's stateless tool execution pattern
func handleAgentRequest(ctx context.Context, client *bifrost.Client) {
    // Step 1: Get tool call suggestions (not executed)
    response, err := client.ChatCompletionRequest(ctx, request)

    // Step 2: Your application reviews tool calls
    for _, toolCall := range response.Choices[0].Message.ToolCalls {
        // Apply business logic, security rules, rate limits
        if !validateToolCall(toolCall) {
            continue
        }

        // Step 3: Explicit execution with full control
        result, err := client.ExecuteMCPTool(ctx, toolCall)
        conversationHistory = append(conversationHistory, result)
    }

    // Step 4: Continue conversation with complete history
    finalResponse, err := client.ChatCompletionRequest(ctx, finalRequest)
}

Built-in Tool Registry: Host Custom Tools Without External Infrastructure

The Go SDK's tool registry eliminates the need for external MCP server deployments for custom business logic. Teams can host tools directly within their applications using typed handlers, reducing operational complexity while maintaining type safety.

This approach transforms what would typically require separate deployment infrastructure into simple function registration:

type CalculatorArgs struct {
    Operation string  `json:"operation"`
    A         float64 `json:"a"`
    B         float64 `json:"b"`
}

func calculatorHandler(args CalculatorArgs) (string, error) {
    switch args.Operation {
    case "add":
        return fmt.Sprintf("%.2f", args.A + args.B), nil
    case "multiply":
        return fmt.Sprintf("%.2f", args.A * args.B), nil
    default:
        return "", fmt.Errorf("unsupported operation")
    }
}

// Register with compile-time type checking
client.RegisterMCPTool("calculator", "Perform arithmetic", calculatorHandler, schema)

The tool registry provides in-process execution with zero network overhead, compile-time type safety, structured error handling, and automatic integration with all AI requests. For internal business logic, this eliminates an entire class of deployment and networking concerns.

Flexible Connection Types: Support for Any Integration Pattern

Bifrost supports three connection types, each optimized for different deployment scenarios:

STDIO Connections launch external processes and communicate via standard input/output, ideal for local tools and scripts. This works perfectly for filesystem operations, database queries with local credentials, and Python or Node.js MCP servers.

HTTP Connections communicate with remote MCP servers via HTTP requests, suited for microservices architectures, cloud-hosted MCP services, and third-party tool providers.

Server-Sent Events (SSE) provide real-time, persistent connections for streaming data, live updates, and event-driven workflows like market data feeds or system monitoring.

This flexibility means teams can choose the right integration pattern for each tool rather than forcing everything through a single communication model.

Granular Filtering and Access Control

Request-level filtering gives teams precise control over which tools and clients are available for specific operations:

// Include only specific clients for sensitive operations
ctx := context.WithValue(context.Background(),
    "mcp-include-clients", []string{"secure-database", "audit-logger"})

// Include specific tools with wildcard support
ctx = context.WithValue(ctx, "mcp-include-tools",
    []string{"secure-database/*", "audit-logger/log_access"})

response, err := client.ChatCompletionRequest(ctx, request)

This pattern enables sophisticated access control policies. Financial applications might restrict production agents to read-only tools while development environments have full access. Customer-facing agents might only access approved external APIs while internal tools remain isolated.

Production-Ready Observability and Monitoring

Bifrost integrates native Prometheus metrics, distributed tracing, and comprehensive logging out of the box. Teams get visibility into tool execution patterns, performance bottlenecks, error rates, and cost attribution without building custom instrumentation.

For teams already using Maxim AI's observability platform, Bifrost integrates seamlessly to provide end-to-end visibility from model inference through tool execution. This unified view is critical for debugging multi-step agent workflows where failures can occur at any point in the execution chain.

Zero-Configuration Startup with Dynamic Management

Bifrost's zero-config startup means developers can begin experimenting immediately without complex setup procedures. Add providers, configure MCP clients, and start making requests within minutes.

The runtime management APIs enable dynamic infrastructure changes without restarts:

// Add new MCP client at runtime
client.AddMCPClient(newClientConfig)

// Update tool availability without downtime
client.EditMCPClientTools("filesystem-tools",
    []string{"read_file", "write_file"})

// Monitor client health and reconnect if needed
clients, _ := client.GetMCPClients()
for _, mcpClient := range clients {
    if mcpClient.State == "Disconnected" {
        client.ReconnectMCPClient(mcpClient.Name)
    }
}

This flexibility is essential for teams running continuous evaluation workflows where MCP server configurations evolve as quickly as agent capabilities.

Integration with Broader AI Infrastructure

Bifrost's position as a comprehensive AI gateway means MCP capabilities integrate with semantic caching, automatic fallbacks, load balancing, and multi-provider support. Teams get unified cost tracking, consistent authentication patterns, and consolidated observability across both model inference and tool execution.

For organizations already invested in the Maxim AI platform, Bifrost provides the production runtime that complements Maxim's experimentation, simulation, and evaluation capabilities. Agents tested in Maxim's simulation environment deploy directly to Bifrost-powered production infrastructure.

When Bifrost Is The Right Choice

Bifrost excels for teams that prioritize developer velocity without sacrificing production readiness. The combination of sub-3ms latency, stateless security architecture, built-in tool registry, and comprehensive observability makes it ideal for:

• Teams building production AI agents that need enterprise-grade performance
• Organizations requiring strict security controls with audit trails
• Developers who value type-safe tool development and quick iteration cycles
• Companies running multi-agent systems with complex tool interaction patterns
• Teams already using Maxim AI for agent quality evaluation seeking unified production infrastructure

The zero-configuration startup and dynamic management capabilities mean teams spend time building agent capabilities rather than managing infrastructure complexity.

2. TrueFoundry: Unified AI Infrastructure Platform

Core Philosophy: If you're already managing AI infrastructure, why fragment it across different systems?

TrueFoundry's MCP Gateway extends their existing AI infrastructure platform to include MCP capabilities. The approach builds on a simple insight: most organizations already have infrastructure for managing LLMs. Instead of building parallel systems for MCP tools, they unify everything into a single control panel.

Consolidated Infrastructure Management

The unified approach provides identical security, observability, and performance characteristics for both LLM calls and tool executions. Organizations tracking LLM costs get consolidated views of tool usage costs and performance metrics without integrating multiple systems.

MCP Server Groups provide logical isolation that other gateways overlook. Different teams can experiment with different MCP servers without creating security holes or configuration conflicts. This matters more in practice than most teams initially realize.

Performance and Integration

TrueFoundry achieves sub-3ms latency under load by handling authentication and rate limiting in-memory rather than through database queries. When agents make hundreds of tool calls per conversation, this performance difference compounds significantly.

The interactive playground generates production-ready code snippets across multiple programming languages, reducing friction between experimentation and deployment. Native integration with Azure AD, comprehensive rate limiting, and fallback mechanisms work without additional configuration.

Ideal Use Case

Organizations already running significant AI workloads that want to extend existing infrastructure rather than fragment it. The unified approach particularly appeals to teams preferring comprehensive AI infrastructure management from a single vendor. Teams with complex multi-cloud requirements should carefully evaluate the integration patterns.

3. IBM Context Forge: Federation-First Architecture

Core Philosophy: Enable sophisticated multi-gateway deployments with maximum architectural flexibility.

IBM's Context Forge represents the most architecturally ambitious approach in the market. The explicit disclaimer about lack of official IBM support creates adoption friction for enterprise customers, but the federation capabilities distinguish it from simpler gateway approaches.

Advanced Federation Capabilities

Auto-discovery via mDNS, health monitoring, and capability merging enable deployments where multiple gateways work together seamlessly. For very large organizations with complex infrastructure spanning multiple environments, this federation model solves real operational problems.

The virtual server composition feature lets teams combine multiple MCP servers into single logical endpoints, simplifying agent interactions while maintaining backend flexibility. Flexible authentication supports JWT Bearer tokens, Basic Auth, and custom header schemes with AES encryption for tool credentials.

Multi-Database Support

PostgreSQL, MySQL, and SQLite support allows integration with existing enterprise systems without architectural changes. This flexibility matters for organizations with established database infrastructure and compliance requirements around data residency.

Important Considerations

The alpha/beta status and explicit lack of commercial support make this primarily suitable for organizations with internal expertise to handle production issues independently. The legacy nature of IBM products, complicated management processes, and steep learning curve should be carefully weighed. Organizations should have dedicated DevOps teams comfortable with infrastructure management before considering Context Forge for production deployments.

4. Microsoft MCP Gateway: Azure-Native Integration

Core Philosophy: Leverage existing Azure infrastructure rather than building parallel systems.

Microsoft's MCP Gateway reflects their broader ecosystem strategy. Instead of creating standalone infrastructure, they've built multiple MCP integration points across Azure services that work together.

Deep Azure Integration

Native Azure AD integration eliminates authentication complexity for Azure customers. OAuth 2.0 flows, policy enforcement through Azure API Management, and integration with existing identity providers work without additional configuration.

The Azure MCP Server provides direct integration with Azure resources, reducing code required to connect AI agents with cloud services. Kubernetes-native architecture handles session-aware routing and multi-tenant deployments using familiar container orchestration patterns.

Operational Trade-offs

The Azure-first design works exceptionally well for organizations heavily invested in the Microsoft ecosystem. However, multi-cloud or hybrid deployments face integration challenges that the architecture doesn't elegantly address.

Organizations should carefully consider vendor lock-in implications, development complexity, and the highly intricate management requirements. The solution suits large-scale enterprise use cases with dedicated Azure infrastructure teams but may introduce unnecessary complexity for teams seeking flexibility across cloud providers.

5. Lasso Security: Security-First Approach

Core Philosophy: Provide visibility and control where traditional security tools fall short for AI agent operations.

Lasso Security, recognized as a 2024 Gartner Cool Vendor for AI Security, focuses on what they call the "invisible agent" problem. Their gateway prioritizes security monitoring and threat detection over raw performance.

Specialized Security Capabilities

The plugin-based architecture enables real-time security scanning, token masking, and AI safety guardrails. This modular design allows organizations to add security capabilities incrementally rather than adopting an all-or-nothing approach.

Tool reputation analysis addresses supply chain security concerns that many organizations cite as their primary barrier to MCP adoption. The system tracks and scores MCP servers based on behavior patterns, code analysis, and community feedback.

Real-time threat detection monitors for jailbreaks, unauthorized access patterns, and data exfiltration attempts. Unlike general-purpose security tools, these capabilities specifically target AI agent behavior patterns.

Target Segment

Organizations in regulated industries or handling sensitive data where comprehensive security monitoring is non-negotiable. The security-first approach particularly appeals to teams needing detailed audit trails and specialized threat detection capabilities for compliance purposes.

Performance and Cost Reality Check

Real-world deployment data reveals significant differences between marketing claims and production performance. Based on testing across multiple implementations, here's what organizations should actually expect:

Cost Impact Analysis

The operational cost implications extend beyond simple pricing comparisons:

Latency Reduction: Sub-3ms overhead means 97% less latency compared to 100ms solutions. For applications making 50 tool calls per interaction, this translates to 5 seconds versus 150 milliseconds of accumulated overhead.

Success Rate Improvement: Better observability and error handling reduce retry costs and improve first-call success rates, significantly impacting operational expenses at scale.

Operational Efficiency: Unified management reduces manual tool integration costs and ongoing maintenance overhead, freeing engineering resources for feature development.

How to Evaluate The Right MCP Gateway

The choice isn't just about features but matching architectural philosophy with organizational reality. Here's a practical evaluation framework:

Decision Framework

Choose Bifrost if: You prioritize developer velocity, production-grade performance, and comprehensive observability. The stateless architecture, built-in tool registry, and sub-3ms latency make it ideal for teams building serious production AI agents. The seamless integration with Maxim AI provides end-to-end visibility from experimentation through production.

Choose TrueFoundry if: You're already managing significant AI workloads and want unified infrastructure. The consolidated approach reduces operational complexity without sacrificing enterprise features.

Choose IBM Context Forge if: You have sophisticated DevOps teams and need advanced federation capabilities across multiple environments. Prepare for steep learning curves and limited commercial support.

Choose Microsoft if: You're heavily invested in Azure and want native integration with existing infrastructure. Accept vendor lock-in and increased management complexity in exchange for ecosystem consistency.

Choose Lasso Security if: Security monitoring is your primary concern and you operate in highly regulated industries. Be prepared to trade performance for specialized threat detection capabilities.

The Future of MCP Gateway Infrastructure

The MCP gateway market is evolving rapidly, but fundamental patterns are crystallizing. The solutions that will dominate balance three critical imperatives:

Security Depth: As agent capabilities expand, the potential impact of security failures increases exponentially. Gateways providing comprehensive threat detection and policy enforcement will capture market segments where security is non-negotiable. Bifrost's stateless architecture and explicit execution model represents this trend.

Operational Simplicity: The complexity of managing hundreds of MCP tools across multiple environments drives adoption toward solutions providing unified management without sacrificing functionality. Zero-configuration startup and dynamic management capabilities become table stakes.

Architectural Adaptability: As agentic AI requirements evolve, organizations need infrastructure that can adapt without complete reimplementation. Flexible connection types, extensible plugin architectures, and runtime configuration changes enable teams to iterate quickly.

But here's the deeper insight: MCP gateways represent just the first wave of infrastructure requirements for agentic AI. Agent-to-agent communication protocols, multi-modal tool interfaces, and autonomous workflow orchestration will all require similar infrastructure layers. The organizations building comprehensive, secure, and scalable MCP capabilities today lay foundations for broader transformation toward autonomous AI systems.

Conclusion: Building Production-Ready AI Agent Infrastructure

The rise of MCP gateways reflects a fundamental shift in how organizations think about AI agent infrastructure. The days of treating tool integration as an afterthought or building custom solutions for each new capability are over. Modern AI agents require production-grade infrastructure that handles security, observability, and operational management at scale.

Bifrost leads this evolution with its developer-first approach, sub-3ms latency, stateless security architecture, and comprehensive feature set. The built-in tool registry eliminates entire classes of deployment complexity while maintaining type safety and performance. For teams serious about shipping production AI agents, Bifrost provides the foundation to move fast without breaking things.

The market is moving quickly, but the fundamentals remain constant: enterprises need solutions that work reliably at scale, integrate with existing security frameworks, and provide operational visibility into AI agent behavior. The MCP gateway vendors solving these enterprise realities, rather than just protocol compliance, will capture the largest share of the emerging agentic AI infrastructure market.

For organizations making infrastructure decisions today, the key is choosing a solution that can evolve with your agentic AI requirements while solving immediate security, observability, and operational challenges. The future belongs to teams that get this infrastructure layer right, and that future is arriving faster than most realize.

Ready to see Bifrost in action? Explore the documentation or schedule a demo with the Maxim team to discuss your specific MCP gateway requirements.