#llm gateway

AI를 조직에서 안전하게 운영하려면 LLM Gateway가 핵심입니다. Amazon Bedrock과 Claude Code를 결합해 인증, 모니터링, 필터링을 중앙에서 관리하는 방법을 알아보세요. 작게 시작해 점진적으로 확장하면 누구나 기업급 AI 보안 체계를 구축할 수 있습니다.

As organizations transition from AI experimentation to full-scale deployment in 2026, the infrastructure responsible for managing Large Language Model (LLM) access has become a critical component of enterprise architecture. With 40% of enterprise applications now integrated with task-specific AI agents, relying on ad-hoc solutions to handle multi-provider AI infrastructure is no longer viable. The move from prototypes to production systems has elevated the importance of low latency, reliability, and strict cost control.

Enterprise AI gateways address the growing complexity of working with multiple LLM providers. They reduce the risk of vendor lock-in, maintain high availability through automated failover mechanisms, and enforce governance frameworks necessary for regulated industries. This overview explores the five leading enterprise AI gateways in 2026, evaluating them based on performance, feature depth, and readiness for production environments.

Enterprise AI Gateway

Before comparing specific platforms, it’s important to understand the capabilities that differentiate enterprise-grade AI gateways from simple request routers. Production AI infrastructure demands far more than basic API forwarding.

Performance and Latency

Request overhead measured in microseconds rather than milliseconds

Capability to support high-throughput workloads exceeding 5,000 requests per second

Minimal processing delay to maintain seamless user experiences in conversational systems

Provider Management

Unified interfaces supporting 10+ leading LLM providers

Ability to update provider configurations dynamically without redeploying applications

Automatic failover when providers encounter outages or rate limits

Enterprise Governance

Hierarchical budgeting systems to control and monitor spending

Role-based access controls for organizations with multiple teams

Comprehensive audit trails to support regulatory compliance

Observability Infrastructure

Distributed tracing across requests routed to multiple providers

Real-time monitoring for usage and cost analytics

Integration with existing monitoring systems through Prometheus and OpenTelemetry

Top 5 Enterprise AI Gateways in 2026

1. Bifrost by Maxim AI

Bifrost offers industry-leading performance alongside enterprise-grade features, all within a platform designed for AI quality management. Organizations such as Clinc, Thoughtful, and Atomicwork rely on Bifrost to power their production AI systems.

Performance Characteristics

11 microseconds overhead at 5,000 requests per second — 50× faster than Python-based alternatives

Built in Go to maximize throughput and minimize resource consumption

Ultra-low latency supports real-time conversational AI without noticeable delays

Multi-Provider Support

A Unified interface supporting OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Cohere, Mistral, Ollama, Groq, and more than 12 providers

An OpenAI-compatible API allows drop-in replacement with minimal code changes

Flexible Dynamic provider configuration via web UI, API, or configuration files

Advanced Infrastructure Features

Built-in Automatic failover between providers and models without downtime

Intelligent Semantic caching that reduces costs and improves latency by caching responses based on meaning rather than exact matches

Smart Load balancing across multiple providers and API keys

Native Model Context Protocol (MCP) support, enabling models to interact with tools like filesystems, search engines, and databases

Enterprise Governance and Security

Robust Hierarchical budget management using virtual keys to enforce team-level, project-level, and customer-level budgets

Real-time usage tracking with hard spending limits

Built-in SSO integration with Google and GitHub authentication

Secure credential storage through HashiCorp Vault support

Detailed audit logs to meet enterprise compliance requirements

Observability and Monitoring

Built-in Native Prometheus metrics for infrastructure monitoring

Distributed tracing via OpenTelemetry

Extensive logging providing insights into cache performance, provider latency, error rates, and cost analytics

Deployment Flexibility

Quick startup with Zero-configuration startup

Extensibility through Custom plugins built with a middleware architecture

Air-gapped deployment options for environments with strict security requirements

Integrated AI Quality Platform

Bifrost integrates seamlessly with Maxim's AI quality platform to support complete AI development workflows.

Teams can deploy AI agents up to 5× faster by improving experimentation, evaluation, and monitoring processes

A unified platform eliminates fragmentation between infrastructure management and AI quality systems

Best For: Organizations that prioritize top-tier performance, strong governance, and integrated AI quality management. Bifrost is particularly well suited for teams that value developer experience, reliability in production, and cost efficiency.

2. AWS Bedrock

AWS Bedrock provides serverless access to foundation models through Amazon’s cloud ecosystem. Organizations already invested in AWS infrastructure often find Bedrock appealing because of its native integrations.

Key Capabilities

Fully managed, serverless infrastructure that eliminates operational overhead

Access to models from providers such as Anthropic, Amazon, Cohere, and Meta via a unified API

Integration with AWS security tools including IAM, CloudWatch, and CloudTrail

Consumption-based pricing aligned with AWS billing models

Considerations

Strong dependency on the AWS ecosystem may create vendor lock-in

Fewer provider options compared to vendor-agnostic AI gateways

Additional latency from managed service layers

Pricing complexity when combining model usage and AWS infrastructure costs

Best For: Organizations already operating heavily within AWS that want a managed AI service integrated into their existing cloud stack.

3. Kong AI Gateway

Kong extends its established API management platform to support AI workloads, offering AI-specific routing and management features.

Key Capabilities

Large plugin ecosystem supporting advanced routing and load balancing

Six routing strategies for intelligent request distribution

Token-based rate limiting with flexible enforcement options

Enterprise-grade resilience features such as circuit breakers and health checks

Considerations

More complex configuration compared to AI-native gateways

Higher infrastructure overhead due to traditional API gateway architecture

Learning curve for teams adopting AI-specific capabilities

Performance trade-offs from general-purpose design

Best For: Enterprises already using Kong that want to extend their existing API infrastructure to support AI services.

4. LiteLLM

LiteLLM is an open-source gateway that provides a unified API across a wide range of LLM providers while maintaining compatibility with OpenAI-style interfaces.

Key Capabilities

Support for 100+ models from numerous providers

Flexible Python SDK that accelerates development

Active open-source ecosystem with frequent contributions

Freedom from vendor lock-in through open licensing

Considerations

Python implementation introduces performance constraints compared to compiled languages

Requires significant customization to meet enterprise security and governance standards

Limited built-in governance capabilities

Higher operational complexity due to self-managed deployment

Best For: Engineering teams that prefer open-source flexibility and have the resources to extend the platform for enterprise requirements.

Choosing the Right Enterprise AI Gateway

The enterprise AI gateway ecosystem in 2026 reflects the transition of AI from experimental projects to mission-critical production systems. Organizations must evaluate solutions based on performance needs, infrastructure compatibility, governance requirements, and long-term AI strategy.

Among the available options, Bifrost distinguishes itself through exceptional performance and a comprehensive set of enterprise features. Its 50× performance advantage over Python-based gateways, combined with zero-configuration deployment and tight integration with Maxim’s AI quality workflows, enables teams to deploy reliable AI agents more efficiently.

As companies move beyond pilot programs into revenue-impacting AI deployments, selecting the right gateway infrastructure becomes increasingly important. The leading platforms of 2026 provide far more than simple routing — they deliver governance, observability, and quality management capabilities necessary for large-scale AI operations.

Bifrost is the fastest, open-source LLM gateway with built-in MCP support, dynamic plugin architecture, and integrated governance.