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As businesses adopt more AI models, managing every provider separately can quickly become complicated. Different models have different APIs, pricing, rate limits, capabilities, and availability.
An AI gateway helps solve this problem by providing a centralized layer between AI applications and model providers. Instead of building and maintaining separate integrations for every model, teams can use a gateway to manage model access, routing, security, monitoring, and costs.
But not every AI gateway is built for the same type of organization.
Some are designed for enterprise governance, while others focus on developer experience, cloud infrastructure, or API management.
In this guide, we compare six of the best AI gateways in 2026, including their key features, use cases, advantages, and limitations.
| AI Gateway | Best for | Main focus |
|---|---|---|
| TrueFoundry AI Gateway | Enterprise AI infrastructure | Governance, routing, observability, security, and cost control |
| Azure AI Gateway | Azure-based enterprises | AI traffic management and governance |
| Cloudflare AI Gateway | Cloudflare users | AI observability, caching, routing, and reliability |
| Vercel AI Gateway | AI application developers | Multi-model access and developer experience |
| Amazon Bedrock | AWS organizations | Foundation model access and AI application development |
An AI gateway is an infrastructure layer that sits between an application and one or more AI model providers.
Without a gateway, an application might need separate integrations for every provider:
Application → OpenAI
Application → Anthropic
Application → Google
Application → AWS
With an AI gateway, the architecture can look more like:
Application → AI Gateway → Multiple AI Providers
This gives engineering and platform teams a centralized place to manage AI traffic.
Depending on the platform, an AI gateway can handle:
This becomes especially useful when an organization moves from testing one or two models to running multiple AI applications in production.
TrueFoundry AI Gateway is built for enterprises that need more control over how AI models, applications, tools, and agents are accessed and managed.
Rather than treating the gateway as simply a way to forward API requests, TrueFoundry brings together AI model access, routing, security, observability, governance, and cost management in a centralized layer.
The platform supports 1,600+ AI models, along with MCP and agent workflow integrations. For enterprises comparing platforms and looking for the Best AI Gateway, this broader approach can be useful when AI infrastructure extends beyond individual LLM API calls.
As organizations move from experimenting with individual AI models to operating production AI applications, they need more than a simple API connection. They need visibility into usage, control over access, and ways to manage cost and reliability.
TrueFoundry brings these capabilities together in one AI infrastructure layer, making it a strong option for enterprises evaluating the Best AI Gateway for managing multi-model and agentic AI workloads.
Azure AI Gateway is part of Azure API Management and is designed to help teams manage AI models, agents, and tools through a governed gateway layer.
Microsoft's current AI Gateway tier is in public preview and provides a managed gateway where applications can call models and tools through a common endpoint. It can authenticate requests, apply policies, route traffic to configured backends, and emit telemetry such as token counts and latency.
Azure's AI Gateway can work with models from Microsoft Foundry, Azure OpenAI, AWS Bedrock, Google Vertex, OpenAI, Anthropic, and other providers.
For companies already invested in Azure, the gateway can fit into an existing Microsoft cloud and API management environment.
Instead of introducing another infrastructure layer solely for AI traffic, teams can use Azure's existing identity, networking, API management, and governance capabilities.
Cloudflare AI Gateway focuses on giving teams visibility and control over AI applications.
It supports AI providers including OpenAI, Anthropic, Google, and others, while providing features such as analytics, logging, caching, rate limiting, retries, and model fallbacks.
Cloudflare's gateway also supports spend limits and custom cost tracking, allowing teams to monitor AI spending by model, provider, user, team, or application.
Cloudflare can be particularly useful for teams already using its network and application infrastructure.
Caching is another notable capability. Repeated requests can potentially be served from cache rather than sent to the model provider again, which can reduce latency and AI API usage.
Vercel AI Gateway is designed for teams building AI applications and agents.
The gateway provides a centralized interface for accessing hundreds of AI models, allowing developers to switch between models without managing individual provider integrations in application code.
Vercel also provides cost-aware routing, budgets, spend monitoring, and model selection capabilities through its AI Gateway.
Developers often want to experiment with new models without rewriting their application every time a provider changes.
A gateway provides an abstraction layer between the application and model provider, making it easier to switch models and providers.
Vercel's AI Gateway is particularly relevant for teams already using Vercel and the AI SDK.
Amazon Bedrock gives organizations access to foundation models through AWS.
The platform provides a model catalog containing foundation models that can be used for different generative AI workloads. (AWS Documentation)
Unlike a dedicated AI gateway, Bedrock is a broader AI platform that combines model access with other services for building generative AI applications.
For companies already running applications on AWS, Bedrock provides a way to access and experiment with different foundation models while keeping AI workloads within the AWS ecosystem.
This can simplify infrastructure management for teams that already use AWS identity, networking, security, and monitoring services.
If you're evaluating an AI gateway, these are some of the most important capabilities to compare.
| Feature | Why it matters |
|---|---|
| Multi-model support | Lets teams work with different providers |
| Routing | Directs requests based on cost, latency, or availability |
| Failover | Helps maintain availability when providers fail |
| Observability | Tracks requests, tokens, latency, errors, and costs |
| Security | Protects model access and provider credentials |
| Rate limiting | Controls traffic and prevents excessive usage |
| Cost controls | Helps teams monitor and manage AI spending |
| Guardrails | Adds policy and safety controls |
| MCP support | Helps manage access to tools used by AI agents |
| Deployment options | Determines whether the platform fits your cloud or infrastructure model |
AI gateways are becoming an important layer in production AI infrastructure. They can help organizations manage the growing complexity of multiple models, providers, applications, and AI agents.
The right choice depends on your infrastructure and requirements. TrueFoundry focuses on enterprise AI governance and centralized AI infrastructure, while Azure AI Gateway is closely aligned with Azure, Cloudflare AI Gateway focuses on traffic management and observability, Vercel AI Gateway emphasizes AI application development, Kong brings AI capabilities into broader API management, and Amazon Bedrock provides foundation model access within AWS.
When comparing an AI gateway, look beyond the number of models supported. Consider routing, reliability, observability, security, cost management, governance, deployment options, and support for emerging agentic workloads.
For enterprises moving toward multi-model and agentic AI infrastructure, these capabilities can determine how easily AI applications can be managed as they scale.
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