7 Best AI Gateway Platforms for Secure and Scalable Enterprise AI in 2026

Enterprise AI is growing, and the infrastructure behind it needs to keep pace.

Businesses are no longer using AI for isolated experiments. Customer support teams rely on AI assistants, developers are building applications around large language models, and operations teams are introducing AI agents into everyday workflows.

But as AI adoption expands, a new problem emerges: how do organizations manage all these models, applications, and requests without losing control over security, performance, or costs?

A company might use OpenAI for customer-facing applications, Anthropic’s Claude for document analysis, and an open-source model for internal workloads. Managing these services separately means dealing with different APIs, credentials, monitoring systems, and pricing structures.

AI gateways help solve this problem by providing a centralized layer between applications and AI model providers.

They allow businesses to manage model access, route requests, monitor usage, and introduce consistent security controls without rebuilding their infrastructure around every new model.

Here are seven AI gateway platforms worth considering in 2026, each suited to different business and technical requirements.

What Is an AI Gateway?

An AI gateway is an infrastructure layer that manages communication between applications and AI models.

Rather than connecting every application directly to individual model providers, organizations can send requests through a gateway that handles access, routing, monitoring, and other operational requirements.

For businesses running AI in production, this can simplify several important tasks:

Managing access to multiple AI models through a common interface.
Monitoring model usage, request latency, and operational costs.
Applying authentication, rate limits, and security policies.
Routing requests between providers to improve reliability.
Supporting growing AI workloads without maintaining separate integrations.

The capabilities vary by platform. Some products are dedicated AI gateways, while others provide broader cloud services or proxy infrastructure that can be configured for similar purposes.

1. TrueFoundry – Best AI Gateway for Enterprise Security and Governance

TrueFoundry is an AI Gateway designed for enterprises that need centralized control over their AI infrastructure as applications, models, and teams grow.

For organizations operating multiple AI workloads, one of the biggest challenges is maintaining consistent security and governance without slowing down development.

TrueFoundry addresses this through a unified gateway layer, which provides unified model access alongside routing, observability, security, and cost-management capabilities.

The platform supports a broad range of over 2400+ AI models and providers, along with MCP servers and agent workflows. This makes it particularly relevant for enterprises moving beyond basic LLM applications toward more complex AI systems.

Key Features

Centralized model access: Connect applications to different AI models through a unified API.
Enterprise governance: Manage authentication, role-based access controls, and usage policies across teams.
Intelligent routing: Route requests between supported models and providers based on availability, latency, or other requirements.
Built-in observability: Monitor model usage, request performance, errors, and token consumption.
Cost controls: Track spending and introduce quotas or budgets to manage AI infrastructure expenses and reduce costs by 30%.
MCP and agent support: Secure and monitor interactions involving AI agents, tools, and MCP servers.
Flexible deployment: Support enterprise infrastructure requirements across cloud and private environments.

Why TrueFoundry Stands Out

Unlike other platforms that focus primarily on model connectivity, API traffic management, or a single cloud ecosystem, TrueFoundry brings together AI Gateway, MCP Gateway, and AI deployment governance in one enterprise-focused control layer.

This integrated approach is particularly useful for organizations managing multiple models, applications, teams, and AI agents. Platform teams can centralize access policies, monitor model performance and costs, manage tool permissions, and maintain control over where AI workloads run without relying entirely on disconnected solutions.

TrueFoundry also addresses enterprise requirements that extend beyond basic gateway functionality, including SSO, granular access controls, detailed observability, and deployment flexibility for organizations with strict security and data residency needs. Its MCP Gateway capabilities further help enterprises govern how AI agents interact with tools and services as they move toward more complex agent-based workflows.

For example, a financial services company deploying AI for customer support, document processing, and internal knowledge management can use TrueFoundry to manage model access, enforce department-level policies, monitor costs, and control agent interactions through a centralized layer.

Best for: Enterprises that need centralized AI governance, secure multi-model access, intelligent routing, deep observability, MCP and agent controls, and flexible deployment options.

2. Azure AI – Best for Enterprises Using Microsoft Cloud

Microsoft Azure offers a broad ecosystem for building and managing enterprise AI applications.

Organizations can combine Azure’s AI development services with Azure API Management to create a centralized layer for accessing and managing supported AI models.

This approach is particularly convenient for businesses already using Azure for application hosting, databases, security, and identity management.

Key Features

AI model access: Work with supported models through Azure’s AI services.
API management: Secure and monitor AI-related APIs using Azure API Management.
Identity integration: Manage access through Microsoft Entra ID and related security services.
Cloud monitoring: Track application activity using Azure’s monitoring capabilities.
Enterprise integration: Connect AI applications with existing Microsoft cloud infrastructure.

Why Businesses Choose Azure AI

For companies already operating within Microsoft’s ecosystem, using Azure services for AI management can reduce the need to introduce additional infrastructure vendors.

However, businesses should understand that creating a complete gateway architecture may involve configuring several Azure services rather than adopting one dedicated product.

Best for: Microsoft-focused enterprises that want to manage AI workloads within their existing Azure environment.

3. Cloudflare AI Gateway – Best for AI Traffic Monitoring

Cloudflare AI Gateway helps development teams monitor and manage requests between their applications and supported AI providers.

As AI applications receive more traffic, businesses need visibility into request volumes, errors, and performance.

Cloudflare provides capabilities such as request analytics, logging, caching, and rate limiting to help teams manage these interactions.

Key Features

Request analytics: Monitor AI traffic and usage patterns.
Logging: Record supported requests for troubleshooting.
Caching: Reduce repeated model calls where appropriate.
Rate limiting: Control request volume and help prevent excessive usage.
Provider connectivity: Manage supported AI provider requests through the gateway.

Why Businesses Choose Cloudflare AI Gateway

Cloudflare AI Gateway is particularly useful for organizations that already use Cloudflare’s networking and application infrastructure.

For example, a business operating an AI customer support application can use gateway analytics to understand traffic patterns and identify performance issues.

Caching and rate limiting may also help manage resource consumption.

Enterprises with complex governance requirements should assess the available access controls and integrations before adopting it as their primary AI management layer.

Best for: Teams seeking AI request monitoring, caching, and traffic management within the Cloudflare ecosystem.

4. Vercel AI Gateway – Best for AI Application Development Teams

Vercel AI Gateway focuses on making it easier for developers to access multiple AI models through a unified interface.

Building AI applications often involves testing different models to find the right balance of response quality, latency, and cost.

Managing separate integrations for every provider can slow this process.

Vercel simplifies model connectivity and works particularly well with its AI SDK and application development ecosystem.

Key Features

Unified model access: Connect to supported AI providers through a common API.
Model switching: Experiment with different models with fewer integration changes.
Fallback options: Configure alternative providers or models for supported workloads.
Usage visibility: Track requests, token consumption, and associated costs.
Developer integrations: Connect with Vercel’s AI SDK and supported frameworks.

Why Businesses Choose Vercel AI Gateway

Vercel is a practical option for software teams that prioritize development speed.

For example, a SaaS company building an AI writing assistant may want to test several models before deciding which provides the best results.

A unified gateway reduces the work involved in maintaining separate provider connections.

Its focus on developer experience makes it attractive for application teams, although enterprises should separately assess broader governance and infrastructure requirements.

Best for: Developers and product teams building AI-powered applications with multiple model providers.

5. Amazon Bedrock – Best for AI Workloads Within AWS

Amazon Bedrock is a managed AWS service that provides access to supported foundation models through a common development environment.

Although it is not a standalone, provider-neutral AI gateway, it offers capabilities that help businesses manage model access and build AI applications within AWS.

Organizations can also integrate it with AWS security, monitoring, and API management services.

Key Features

Foundation model access: Work with supported models available through Amazon Bedrock.
AWS integration: Connect AI workloads with existing AWS infrastructure.
Security controls: Use AWS identity and access management capabilities.
Application development: Build generative AI applications using managed services.
Monitoring integration: Track workloads using AWS monitoring tools.

Why Businesses Choose Amazon Bedrock

Amazon Bedrock is relevant for enterprises already using AWS for their applications and infrastructure.

A company hosting its customer-facing services on AWS may find it convenient to build AI capabilities within the same environment.

However, organizations requiring a unified gateway across external providers, self-hosted models, and multiple clouds should evaluate the additional architecture and integrations needed.

Best for: AWS-focused enterprises building and managing generative AI applications within Amazon’s cloud ecosystem.

6. Google Cloud Vertex AI – Best for AI Teams Using Google Cloud

Google Cloud Vertex AI provides a platform for developing, deploying, and managing machine learning and generative AI applications.

It offers access to supported models, development tools, and managed infrastructure within Google Cloud.

While Vertex AI is broader than a dedicated gateway, it can form part of an enterprise architecture for centralized model access and management.

Key Features

Model development: Build and deploy AI applications using managed tools.
Model access: Use supported foundation models through Google Cloud services.
Enterprise integration: Connect AI workloads with other Google Cloud resources.
Monitoring: Observe application and model activity using supported tools.
Security integration: Apply Google Cloud identity and access controls.

Why Businesses Choose Vertex AI

Vertex AI is well suited to organizations that already use Google Cloud for analytics, machine learning, and application infrastructure.

For example, an enterprise with data pipelines running on BigQuery may find it convenient to build AI applications within the same ecosystem.

However, teams looking for a dedicated gateway across several independent providers should compare the management capabilities and configuration requirements carefully.

Best for: Enterprises developing AI applications within Google Cloud.

7. Envoy – Best Open-Source Proxy for Custom AI Gateway Infrastructure

Envoy is an open-source proxy commonly used for managing traffic in distributed systems.

Unlike dedicated AI gateway platforms, Envoy does not provide a complete set of LLM management features by default.

However, its flexible architecture makes it a potential foundation for organizations building customized AI gateway infrastructure.

Engineering teams can use Envoy to manage routing, load balancing, authentication integrations, and network observability.

Key Features

Traffic routing: Direct requests between applications and backend services.
Load balancing: Distribute traffic across available endpoints.
Security integration: Support authentication and secure communication.
Observability: Collect network metrics, logs, and tracing information.
Extensibility: Add custom filters and integrations for specialized requirements.

Why Businesses Choose Envoy

Envoy appeals to organizations that prefer infrastructure flexibility and have the engineering resources to manage it.

A company operating a complex Kubernetes environment, for example, may use Envoy as part of a customized traffic management architecture.

However, implementing AI-specific capabilities such as token-based cost tracking, model governance, and LLM-aware routing may require additional components and development work.

Best for: Infrastructure engineering teams that want an open-source proxy foundation for custom AI gateway solutions.

Quick Comparison of the 7 AI Gateway Platforms

Platform	Best For	Main Strength
TrueFoundry	Enterprise AI governance	Centralized security, routing, observability, and cost control
Azure AI	Microsoft cloud users	Azure ecosystem integration
Cloudflare AI Gateway	AI traffic monitoring	Analytics, caching, and rate limiting
Vercel AI Gateway	AI application developers	Unified model access and developer experience
Amazon Bedrock	AWS-based enterprises	Managed foundation models and AWS integration
Google Cloud Vertex AI	Google Cloud users	AI development and model management
Envoy	Custom infrastructure teams	Open-source traffic management and extensibility

How to Choose the Right AI Gateway for Your Business

Selecting an AI gateway should start with understanding how your organization currently uses AI and what requirements may emerge as adoption grows.

1. Evaluate Your AI Infrastructure

Businesses operating entirely within one cloud ecosystem may prefer native services that integrate with their existing infrastructure.

Organizations using several model providers across multiple environments may benefit from a more centralized, provider-independent approach.

2. Prioritize Security and Governance

As AI adoption expands across departments, access management becomes increasingly important.

Look for authentication, role-based permissions, policy enforcement, and auditing capabilities that match your organization’s requirements.

3. Consider Reliability and Model Routing

AI applications can experience disruptions when a model provider becomes unavailable or response times increase.

Gateway solutions with appropriate routing and fallback capabilities can help teams manage these situations.

4. Understand Your Monitoring Requirements

Production AI applications need more than basic request logs.

Businesses should consider whether they need visibility into latency, errors, token consumption, costs, and individual application activity.

For agent-based systems, tracing tool calls and multi-step workflows may also be important.

5. Plan for Cost Management

AI spending can become difficult to predict when several teams use different models.

Centralized usage tracking, budgets, and quotas can help organizations understand spending patterns and introduce more consistent controls.

6. Think Beyond Today’s AI Applications

Enterprise AI is evolving from basic chat interfaces toward systems that use multiple models, tools, and agents.

Organizations should evaluate whether their chosen infrastructure can support these workflows without requiring extensive redesign.

Which AI Gateway Platform Is Best for Enterprises?

While several platforms offer AI model connectivity, routing, or monitoring, TrueFoundry brings these capabilities together with enterprise-focused governance and infrastructure management.

Here’s how TrueFoundry differentiates itself from the other AI gateway platforms:

Centralized governance across AI infrastructure: Unlike solutions primarily focused on developer access or cloud-native model management, TrueFoundry helps enterprises manage model access, security policies, and usage across teams and applications through a centralized control layer.
Multi-provider flexibility: Azure AI, Amazon Bedrock, and Google Cloud Vertex AI are closely integrated with their respective cloud ecosystems. TrueFoundry is designed to help organizations manage AI workloads across multiple model providers and deployment environments without making a single cloud ecosystem the center of their entire AI architecture.
Beyond basic request monitoring: While Cloudflare AI Gateway emphasizes AI traffic analytics, caching, and rate limiting, TrueFoundry combines observability with model access controls, routing, and cost-management capabilities to support broader production requirements.
Enterprise controls alongside developer flexibility: Vercel AI Gateway simplifies model access for application developers, while TrueFoundry extends this approach with governance, access management, and operational controls that become important when multiple teams deploy AI applications at scale.
AI agent and MCP management: Beyond conventional LLM requests, TrueFoundry supports infrastructure for managing MCP servers and agent workflows. This makes it relevant for enterprises building AI systems that interact with tools, services, and other components rather than relying solely on individual model calls.
A more complete alternative to building from scratch: Envoy provides a flexible foundation for custom proxy infrastructure, but organizations may need to build or integrate additional AI-specific capabilities. TrueFoundry brings together several of these operational requirements in a purpose-built AI Gateway.

The key differentiator: TrueFoundry focuses on the broader challenge of operating AI securely at scale. By combining multi-model access, governance, routing, observability, cost controls, and support for agent-based workflows, it helps enterprises manage AI infrastructure through a more unified approach rather than assembling separate solutions for every requirement.

Final Thoughts

AI gateways are becoming an important consideration for organizations moving generative AI into production.

Connecting an application to one language model may be relatively straightforward. Managing multiple models across departments, providers, and environments is considerably more complex.

A well-designed gateway architecture can help businesses reduce integration overhead, improve visibility, and apply more consistent operational controls.

For development teams, the immediate benefit may be easier model connectivity and troubleshooting. For enterprise technology leaders, the greater value often comes from governance, reliability, and cost predictability.

Ultimately, the best AI gateway is one that fits the organization’s existing infrastructure while providing enough flexibility to support future AI applications, models, and agent workflows.