Top 5 AI Gateways for Production LLM Applications (2026)

TL;DR

AI gateways have become essential infrastructure for production LLM applications, providing centralized control over API routing, cost management, security, and observability. This article examines five leading AI gateway solutions: Maxim AI offers end-to-end simulation, evaluation, and observability with enterprise-grade gateway capabilities; Portkey provides a unified API across multiple LLM providers with fallback and load balancing; LiteLLM delivers an open-source gateway with extensive provider support; Kong extends its API gateway platform with AI-specific features; and Azure API Management integrates AI gateway capabilities into Microsoft's cloud ecosystem. Each platform addresses different aspects of production LLM management, from comprehensive quality assurance to provider abstraction and enterprise integration. The right choice depends on your existing infrastructure, multi-provider requirements, and need for evaluation and monitoring capabilities.

Table of Contents

1. Understanding AI Gateways
2. Why AI Gateways Matter for Production
3. Maxim AI: AI Quality Platform with Gateway Capabilities
4. Portkey: Unified API for Multi-Provider Management
5. LiteLLM: Open-Source Gateway with Extensive Provider Support
6. Kong: Enterprise API Gateway with AI Features
7. Azure API Management: Cloud-Native AI Gateway
8. Comparison Table: Features and Use Cases
9. Choosing the Right AI Gateway
10. Further Reading

Understanding AI Gateways

AI gateways serve as centralized control layers between your applications and LLM providers. They abstract the complexity of managing multiple models, handle routing and fallback logic, enforce security policies, and provide observability into LLM usage patterns.

Core Functions of AI Gateways

AI gateways typically provide several essential capabilities:

• Provider Abstraction: Unified interface across OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, and other providers
• Request Routing: Intelligent routing based on model availability, cost, latency, or custom rules
• Cost Management: Track spending across providers, set budgets, and optimize usage
• Security and Compliance: Enforce access policies, sanitize inputs, and maintain audit trails
• Caching: Reduce redundant API calls and lower costs through intelligent caching
• Rate Limiting: Protect against quota exhaustion and manage concurrent requests

The complexity of production LLM applications has made gateways increasingly critical. Organizations often use multiple providers for resilience, cost optimization, and access to specialized models. Managing this complexity without a gateway leads to fragmented code, inconsistent error handling, and limited visibility into usage patterns.

Why AI Gateways Matter for Production

The shift from prototype to production introduces several challenges that AI gateways help address.

Production Challenges

Provider Reliability: LLM APIs experience outages, rate limits, and performance degradation. Without automatic fallback, these issues directly impact user experience. AI gateways implement retry logic, circuit breakers, and provider switching to maintain availability.

Cost Control: Production applications can quickly accumulate significant LLM costs. Without centralized tracking and optimization, teams struggle to understand spending patterns and identify opportunities for reduction. Gateways provide granular cost visibility and optimization features like caching and routing to cheaper models for appropriate use cases.

Security and Compliance: Production systems require robust security controls including API key management, request sanitization, and audit logging. Gateways centralize these controls rather than requiring each application team to implement them independently.

Observability: Understanding LLM behavior in production requires detailed logging and tracing. Gateways capture request and response data, token usage, latency metrics, and error rates across all providers in a unified format.

Business Impact

Organizations deploying production LLM applications without gateways report:

• Higher operational costs due to inefficient provider usage and lack of caching
• Increased downtime when provider outages affect critical workflows
• Security vulnerabilities from inconsistent access controls across applications
• Limited ability to optimize model selection and routing based on usage patterns
• Delayed debugging and issue resolution due to fragmented logging

The integration of gateway capabilities with AI evaluation and monitoring creates a comprehensive approach to production AI management. While basic gateways handle routing and cost tracking, platforms that combine gateway features with quality assurance enable teams to maintain reliability at scale.

Maxim AI: AI Quality Platform with Gateway Capabilities

Platform Overview

Maxim AI provides comprehensive AI simulation, evaluation, and observability capabilities that include enterprise-grade gateway functionality. Unlike standalone gateways that focus solely on routing and cost management, Maxim integrates gateway capabilities within end-to-end quality assurance workflows.

The platform serves teams that need both operational control over LLM infrastructure and systematic quality management. Organizations switching to Maxim consistently cite the combination of gateway features with evaluation and monitoring as key to maintaining reliability while scaling production applications.

Key Gateway and Platform Features

Provider Management and Routing

Maxim's infrastructure layer provides:

• Multi-Provider Support: Unified access to OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, Azure OpenAI, and custom models
• Intelligent Routing: Route requests based on model performance, cost, latency, or custom evaluation metrics
• Fallback Logic: Automatic failover when providers experience outages or rate limits
• Load Balancing: Distribute requests across providers to optimize throughput

Cost Optimization

• Granular Tracking: Monitor spending across providers, models, and application features
• Budget Controls: Set spending limits and receive alerts before exceeding thresholds
• Caching: Reduce redundant API calls through intelligent response caching
• Cost-Performance Analysis: Evaluate quality versus cost tradeoffs across models

Production Observability

Maxim's observability suite extends beyond basic gateway logging:

• Distributed Tracing: Track requests across complex multi-agent workflows with full visibility
• Real-Time Monitoring: Monitor latency, error rates, and quality metrics in production
• Automated Evaluations: Run periodic quality checks on production traffic using custom evaluators
• Alert Management: Get notified of performance degradation, quality issues, or cost anomalies

Quality Assurance Integration

What distinguishes Maxim from standalone gateways is the integration of gateway capabilities with comprehensive quality workflows:

• Pre-Production Testing: Simulate production scenarios across different providers before deployment
• Evaluation Framework: Run systematic evaluations on gateway traffic to measure quality, accuracy, and safety
• Human-in-the-Loop: Enable product and QA teams to review and label gateway traffic for continuous improvement
• Data Curation: Extract production data from gateway logs for fine-tuning and evaluation datasets

Security and Compliance

• API Key Management: Centralized credential management with rotation and access controls
• Request Sanitization: Filter sensitive information from requests and responses
• Audit Trails: Comprehensive logging for compliance and debugging
• Enterprise Deployment: Managed deployments with robust SLAs and dedicated support

Developer Experience

• SDK Support: Python, TypeScript, Java, and Go SDKs for seamless integration
• No-Code Configuration: Enable product teams to configure routing and evaluations without engineering
• Custom Dashboards: Create insights across custom dimensions with a few clicks
• Integration Ecosystem: Connect with existing observability and development tools

Best For

Maxim AI is ideal for teams that need:

• Comprehensive quality assurance alongside gateway functionality
• Integration of evaluation, monitoring, and routing in unified workflows
• Cross-functional collaboration between engineering, product, and QA teams
• Enterprise-grade deployment with dedicated support
• Reliable AI applications that require both operational control and quality management

Organizations like Clinc, Thoughtful, and Atomicwork use Maxim to maintain quality and operational efficiency across conversational AI, workflow automation, and enterprise support applications.

Portkey: Unified API for Multi-Provider Management

Platform Overview

Portkey is a dedicated AI gateway that provides a unified API across multiple LLM providers. It focuses on simplifying multi-provider management through standardized interfaces and intelligent routing.

Key Features

• Provider Abstraction: Single API interface for 100+ LLMs across major providers
• Fallback and Retries: Automatic failover between providers with configurable retry logic
• Load Balancing: Distribute requests across multiple providers or models
• Observability Dashboard: Monitor requests, latency, and costs across providers
• Prompt Management: Version control and A/B testing for prompts
• Caching: Response caching to reduce costs and latency

Best For

Portkey suits teams focused primarily on provider management and routing. It provides strong gateway capabilities but lacks the comprehensive evaluation and quality assurance features needed for systematic AI reliability. Teams using Portkey often need separate tools for simulation, evaluation, and production quality monitoring.

LiteLLM: Open-Source Gateway with Extensive Provider Support

Platform Overview

LiteLLM is an open-source gateway that provides a unified interface for calling 100+ LLMs. It emphasizes simplicity and extensive provider support with a Python-first approach.

Key Features

• Extensive Provider Support: Works with OpenAI, Anthropic, Cohere, Replicate, HuggingFace, and many others
• OpenAI-Compatible API: Drop-in replacement for OpenAI API calls
• Cost Tracking: Basic logging of token usage and costs
• Proxy Server: Deploy as a standalone proxy for centralized access
• Open Source: Community-driven development with transparent roadmap

Best For

LiteLLM works well for teams that prefer open-source solutions and need basic provider abstraction. The open-source model provides flexibility and transparency but requires teams to self-manage deployment, scaling, and feature development. It lacks enterprise support, advanced observability, and quality evaluation capabilities found in commercial platforms.

Kong: Enterprise API Gateway with AI Features

Platform Overview

Kong is an established enterprise API gateway that has added AI-specific features to its platform. It extends traditional API management capabilities to handle LLM traffic with specialized plugins.

Key Features

• API Gateway Foundation: Mature platform with enterprise-grade security, scalability, and reliability
• AI Plugins: Specialized plugins for LLM rate limiting, prompt management, and cost tracking
• Multi-Cloud Support: Deploy across AWS, Azure, GCP, or on-premises infrastructure
• Enterprise Security: Advanced authentication, authorization, and compliance features
• Observability Integration: Connect with existing monitoring and logging infrastructure

Best For

Kong suits organizations already using Kong for API management who want to extend their existing infrastructure to handle LLM traffic. It provides robust gateway capabilities but requires significant configuration and lacks purpose-built AI evaluation and quality assurance features. Teams often need additional tools for systematic AI testing and monitoring.

Azure API Management: Cloud-Native AI Gateway

Platform Overview

Azure API Management is Microsoft's cloud-native API gateway that includes features for managing Azure OpenAI Service and other LLM providers within the Azure ecosystem.

Key Features

• Azure Integration: Native integration with Azure OpenAI Service and Microsoft's AI services
• Policy Engine: Configure routing, rate limiting, and transformations through policies
• Developer Portal: Self-service portal for API consumers
• Azure Monitor Integration: Connect with Azure's observability and logging services
• Enterprise Features: Advanced security, compliance, and governance capabilities

Best For

Azure API Management works well for organizations heavily invested in the Microsoft ecosystem. It provides strong integration with Azure services but has limited support for providers outside the Azure environment. Teams need additional tools for comprehensive AI evaluation, simulation, and quality monitoring beyond basic gateway functionality.

Comparison Table: Features and Use Cases

Choosing the Right AI Gateway

Selecting the appropriate AI gateway depends on several factors beyond basic routing and cost tracking.

Infrastructure and Ecosystem

• Cloud-native teams using Azure benefit from Azure API Management's native integration
• Multi-cloud organizations need gateways like Maxim, Kong, or Portkey that support diverse environments
• Open-source preference teams might start with LiteLLM for basic gateway functionality
• Existing API gateway users can extend Kong to handle LLM traffic

Quality and Reliability Requirements

• Production applications requiring systematic quality assurance need platforms that integrate evaluation with gateway capabilities
• Simple routing use cases can work with basic gateways focused on provider abstraction
• Regulated industries require comprehensive observability, audit trails, and quality monitoring
• High-stakes applications benefit from platforms that combine gateway functionality with simulation and testing

Team Structure and Collaboration

• Cross-functional teams benefit from platforms with both code and no-code interfaces for configuring routing and evaluations
• Engineering-only teams can work with developer-focused gateways
• Enterprise organizations need collaboration features that enable product, QA, and engineering teams to work together

Scale and Complexity

• Multi-agent systems require distributed tracing and granular observability beyond basic gateway logging
• Simple chatbots might need only provider abstraction and basic cost tracking
• Enterprise applications serving millions of users need robust monitoring, alerting, and quality assurance

Integration Requirements

Consider how gateways integrate with your existing infrastructure:

• Observability stack: Evaluate integration with current monitoring and logging systems
• Development workflow: Check compatibility with your frameworks and CI/CD pipelines
• Security infrastructure: Assess integration with existing access controls and compliance tools
• Data platforms: Consider integration with data warehouses and analytics tools

The distinction between operational control and quality assurance is crucial. Basic gateways provide routing, cost tracking, and observability. Comprehensive platforms like Maxim integrate these capabilities with systematic evaluation, simulation, and quality monitoring.

AI reliability requires both operational excellence and quality management. Teams that separate gateway functionality from evaluation often struggle to maintain quality as they scale, discovering issues only after they affect users.

For most production AI applications, a platform that combines gateway capabilities with comprehensive quality assurance provides the most value. Point solutions address specific operational needs but create gaps in quality management that emerge during scaling.

Further Reading

Maxim AI Resources

• AI Agent Quality Evaluation Guide
• LLM Observability Guide
• Agent Tracing for Multi-Agent Systems
• AI Model Monitoring in 2025
• What Are AI Evals?
• How to Ensure Reliability of AI Applications

External Resources

• OpenAI Production Best Practices
• Anthropic Guide to Building with Claude
• AWS Best Practices for Generative AI
• Google Cloud AI Gateway Documentation

Ready to combine enterprise-grade gateway capabilities with comprehensive AI quality assurance? Book a demo to see how Maxim helps teams manage production LLM applications with unified routing, evaluation, and observability.