7 Best AI Gateways in 2026

AI applications are moving beyond single-model integrations. Companies now use multiple large language models, AI providers, agents, and tools depending on their application requirements.

Managing all these connections separately can create problems with API keys, model routing, monitoring, security, reliability, and costs.

An AI gateway provides a centralized layer between applications and AI providers. It allows teams to manage AI traffic from one place while making it easier to switch between models and providers.

This guide covers eight AI gateways worth considering in 2026, along with their key features, ideal use cases, and what to consider before choosing one.

Comparison of the Best AI Gateways

AI Gateway	Best for	Key capabilities
TrueFoundry AI Gateway	Enterprise AI infrastructure	Routing, governance, security, observability, cost controls
Azure AI Gateway	Azure enterprises	AI traffic management, governance, model routing
Cloudflare AI Gateway	Cloudflare users	Analytics, caching, rate limiting, routing
Vercel AI Gateway	Developers	Multi-model access, routing, failover
Amazon Bedrock	AWS organizations	Foundation models and AI application development
Google Vertex AI	Google Cloud teams	Model access, development, evaluation
IBM watsonx.ai	Enterprise AI teams	AI development, governance, model management

1. TrueFoundry AI Gateway

Truefoundry is the best AI gateway for enterpises that need centralized control and governance as they scale their AI infrastructure. It helps enterprises securely manage, govern, and monitor access to multiple AI models through a single platform, supporting 2000+ AI models, MCP servers, and agent workflows. It also offers built-in observability, security, routing, and cost controls..

Why enterprises use TrueFoundry

The main advantage is centralized management.

Instead of maintaining separate API integrations, credentials, monitoring systems, and governance policies for every AI provider, teams can manage these requirements through one layer.

As AI applications move into production, organizations need to understand which models are being used, how much they cost, how they perform, and who has access to them.

TrueFoundry brings these capabilities together, making it a suitable option for organizations looking for a Best AI Gateway to manage multi-model and agentic AI workloads.

Best for

Enterprises running multiple AI models, applications, teams, MCP servers, and agent workflows.

Potential limitations

Smaller teams using only one AI provider may not need the full set of enterprise governance and infrastructure capabilities.

2. Azure AI Gateway

Azure AI Gateway is designed for organizations that want to manage AI traffic within Microsoft’s Azure ecosystem.

It works with Azure API Management and provides a centralized layer for managing AI models, agents, and tools.

Azure’s AI Gateway capabilities can provide policies for authentication, rate limits, token usage, routing, monitoring, and other controls.

Best for

Organizations are already using Microsoft Azure for their cloud and AI infrastructure.

Potential limitations

Teams that do not use Azure may find less value in its ecosystem-specific integrations.

3. Cloudflare AI Gateway

Cloudflare AI Gateway provides a centralized layer for managing AI requests and monitoring AI application traffic.

It supports multiple AI providers and includes features for analytics, caching, rate limiting, retries, and model fallbacks.

One of its notable capabilities is caching, which can help applications avoid sending identical requests repeatedly to an AI provider.

Best for

Teams already using Cloudflare that want to add AI traffic management to their existing infrastructure.

Potential limitations

Organizations looking for a broader enterprise AI control plane may need additional tools depending on their requirements.

4. Vercel AI Gateway

Vercel AI Gateway focuses on developers building AI-powered applications.

It provides a unified interface for working with multiple AI models and providers, allowing developers to change models without building an entirely separate integration for each provider.

Best for

Development teams building AI applications with Vercel and the AI SDK.

Potential limitations

Teams requiring extensive enterprise governance, infrastructure controls, or complex deployment models may need additional capabilities.

5. Amazon Bedrock

Amazon Bedrock provides access to foundation models through AWS.

Rather than functioning only as a gateway, Bedrock is a broader generative AI platform that provides model access and tools for building AI applications.

Organizations can work with models from different providers while using AWS infrastructure and services.

Best for

Organizations already building applications and AI workloads on AWS.

Potential limitations

Bedrock provides a much broader set of AI services than a dedicated AI gateway, so teams specifically looking for centralized AI traffic governance should compare its capabilities with dedicated gateway platforms.

6. Google Vertex AI

Google Vertex AI is Google’s broader AI development platform for Google Cloud customers.

It provides access to foundation models and tools for building, deploying, evaluating, and managing AI applications.

Best for

Organizations already using Google Cloud for AI and machine learning workloads.

Potential limitations

Vertex AI is a broader AI platform rather than a dedicated AI gateway. Organizations primarily looking for centralized multi-provider AI traffic management may need to evaluate additional gateway capabilities.

7. IBM watsonx.ai

IBM watsonx.ai is an enterprise AI development platform focused on building, deploying, and managing AI applications.

It provides access to foundation models and tools for working with generative AI and machine learning within an enterprise environment.

Best for

Large organizations looking for an enterprise AI platform with development and governance capabilities.

Potential limitations

watsonx.ai is broader than a dedicated AI gateway, so organizations focused specifically on model routing and centralized AI traffic management may need to compare additional solutions.

What Does an AI Gateway Do?

An AI gateway sits between an application and one or more AI providers.

For example:

Application → AI Gateway → OpenAI, Anthropic, Google, AWS, or other models

The gateway can determine where requests should go and apply policies before and after the request reaches the model.

Depending on the platform, this can include:

• Authentication
• Model selection
• Routing
• Rate limiting
• Cost tracking
• Logging
• Monitoring
• Security
• Failover
• Guardrails

This gives engineering and platform teams a central point for managing AI traffic.

What Features Should You Look for in an AI Gateway?

Before choosing an AI gateway, evaluate the platform against your actual AI infrastructure requirements.

Feature	What to consider
Model support	Does it support the providers and models you use?
Routing	Can requests be routed based on cost, latency, or availability?
Failover	Can traffic move to another provider when a model is unavailable?
Observability	Can you monitor tokens, latency, errors, and usage?
Security	Does it provide authentication, authorization, and key management?
Cost controls	Can you set budgets, quotas, or spending limits?
Guardrails	Can you enforce AI-specific policies?
MCP support	Can it manage access to tools and MCP servers?
Agent support	Can it work with agent workflows?
Deployment	Does it support your cloud, hybrid, or self-hosted environment?

 

Final Thoughts

AI gateways can provide an important control layer for organizations running AI applications across multiple models and providers.

The best choice depends on your existing infrastructure and what you need from the gateway. Some platforms focus on enterprise governance, while others are closely integrated with cloud platforms, developer ecosystems, or traditional API management.

For enterprises managing multiple models, applications, teams, and agentic workloads, TrueFoundry AI Gateway provides a centralized approach to AI infrastructure with routing, security, observability, governance, and cost management capabilities.

When evaluating the Best AI Gateway, focus on the capabilities that will matter as your AI infrastructure grows: model routing, reliability, observability, security, cost management, governance, and support for AI agents and MCP.