Top 5 AI Gateways for Enterprise (2026 Guide)

Enterprise AI infrastructure has become considerably more complicated than connecting an application to a single large language model. Production systems increasingly use several model providers, while AI agents may also communicate with tools, MCP servers and other agents. Every additional connection introduces questions around security, reliability, cost, access control and observability.

This is where an AI gateway becomes valuable. Sitting between enterprise applications and the AI services they use, a gateway provides a central control layer for routing requests, applying policies, monitoring activity and managing access. In 2026, the strongest products are expanding beyond basic LLM proxying to address the operational realities of agentic AI.

The five platforms below take different approaches. Some grew out of API management or network infrastructure, while others were designed specifically around AI. For enterprises that need security and governance alongside routing and observability, NeuralTrust TrustGate stands out as the strongest overall option.

What Matters in an Enterprise AI Gateway?

Model connectivity is only the starting point. Supporting OpenAI, Anthropic, Google and self-hosted models is useful, but enterprises also need to know what happens when providers fail, costs rise unexpectedly or sensitive information appears in a prompt.

A strong enterprise gateway should provide intelligent routing, fallbacks and rate limiting while making traffic observable at a granular level. Security becomes even more important as agents gain permission to interact with internal systems. Identity, policy enforcement, auditability and protection against prompt injection or sensitive-data leakage need to operate across the AI traffic path rather than being added independently to every application.

Deployment flexibility also matters. Some organizations are comfortable with SaaS, while regulated businesses may require private infrastructure, hybrid deployments or complete on-premises isolation.

1. NeuralTrust TrustGate: Best Overall Enterprise AI Gateway

NeuralTrust TrustGate takes first place because its architecture addresses where enterprise AI is heading rather than limiting the gateway to conventional model traffic. The platform provides a central control point for interactions with models, MCP servers, tools and other agents, bringing routing, security and governance into the same layer.

For organizations deploying multiple AI systems, that wider scope is important. Instead of every development team implementing its own controls, the AI Gateway from NeuralTrust can enforce policies centrally while applications retain access to different providers, including OpenAI, Anthropic, Azure OpenAI and self-hosted models. TrustGate also extends governance into MCP tool access and agent-to-agent communication.

Security is where TrustGate particularly distinguishes itself. NeuralTrust describes end-user identity as a first-class part of the architecture, with per-agent and per-tool role-based access controls and audit trails. Its security capabilities include inline prompt inspection and PII redaction, addressing risks that conventional API gateways were not originally designed to understand.

There is also considerable deployment flexibility. Enterprises can use TrustGate as SaaS, choose a hybrid setup with a privately hosted data plane, or deploy it on-premises and air-gapped. NeuralTrust reports less than 100 ms of added p95 latency with PromptGuard and Data Masking enabled and more than 20,000 requests per second per node under sustained testing. These are vendor-reported figures, but they show that security has been designed for production-scale traffic rather than treated purely as a governance layer.

For enterprises moving from LLM applications toward agents that can use tools and delegate work, TrustGate offers the most complete combination of model flexibility, security, identity, governance and agent-oriented architecture in this ranking.

2. Portkey: Best for Broad Model Connectivity and AI Operations

Portkey has developed a strong enterprise offering around model connectivity, reliability and observability. Its gateway provides a universal API alongside conditional routing, automated fallbacks, retries, load balancing, caching, request timeouts and budget controls. It also supports MCP connections and multimodal models.

The platform is particularly attractive for organizations that want to standardize access to a very broad model ecosystem. Portkey currently advertises connectivity across more than 1,600 LLMs and providers through its enterprise gateway, reducing the need to maintain separate integrations as teams experiment with different models.

Enterprise controls include SSO, role-based access, organizational workspaces, budget limits and data isolation. Portkey also provides canary testing, circuit breakers and configurable routing, which are useful for platform teams managing production workloads where provider availability and model changes need to be handled without disrupting applications.

TrustGate ranks ahead because its security and governance model extends particularly deeply into agent identity, MCP tools and agent-to-agent interactions. Portkey remains an excellent choice for teams whose priorities centre on extensive model access and mature LLM operations.

3. Cloudflare AI Gateway: Best for Cloud-Native Performance and Simplicity

Cloudflare AI Gateway brings AI traffic management into Cloudflare's broader infrastructure ecosystem. It provides analytics and logging alongside caching, rate limiting, retries, model fallback and dynamic routing. Cloudflare says applications can begin using the gateway with a one-line code change, making the entry barrier relatively low.

Its caching capabilities are particularly interesting for applications generating repeated requests. Cloudflare states that cached responses can reduce latency by up to 90% while avoiding unnecessary model calls. Spend limits can also be scoped by provider, model or custom metadata, giving enterprises another mechanism for controlling AI costs.

Security capabilities have also expanded. Cloudflare's guardrails can inspect both user prompts and model responses, applying consistent moderation policies across supported providers and logging enforcement activity for auditing purposes.

Cloudflare is a compelling choice for teams already invested in its infrastructure or those prioritizing straightforward deployment, global performance and traffic management. Enterprises seeking a gateway built more specifically around complex agent authorization and governance may find TrustGate more aligned with that requirement.

4. Kong AI Gateway: Best for Existing API Management Environments

Kong approaches AI gateways from a different direction. Rather than creating an entirely separate infrastructure layer, it extends the established Kong Gateway with AI-specific plugins.

The AI Proxy and AI Proxy Advanced capabilities provide provider-agnostic model access, centralized credential management and dynamic routing. Kong can route requests according to factors including cost, usage and response accuracy, while its AI plugins add security, observability and governance capabilities around LLM traffic.

Deployment flexibility is another advantage. Kong's AI functionality is supported across managed, hybrid, self-hosted, DB-less and Kubernetes environments.

That makes Kong particularly logical for large organizations that already rely on it for API management. Existing platform teams can bring AI traffic into familiar operational patterns rather than introducing an unrelated gateway stack. For organizations starting an agent-security architecture from scratch, however, a purpose-built AI and agent gateway can provide a more direct fit.

5. LiteLLM: Best for Open-Source Flexibility and Self-Hosting

LiteLLM is a strong option for engineering teams that want extensive control over their gateway infrastructure. Its enterprise offering builds on the open-source LiteLLM gateway and supports self-hosted and air-gapped deployments.

The feature set covers many of the requirements expected from enterprise AI infrastructure, including virtual keys, budgets, rate limits, spend tracking, audit logs, SSO, SCIM, RBAC and OIDC/JWT authentication. Model access can be controlled by key, user or team, while integrations with observability platforms and Prometheus help connect AI traffic with existing monitoring practices.

LiteLLM has also expanded beyond basic LLM routing. Its current platform presents LLM, MCP and agent gateway capabilities together, with support for governance, usage tracking and deployment in on-premises, cloud and Kubernetes environments.

The trade-off is largely organizational. LiteLLM is particularly appealing to teams that value open-source flexibility and are comfortable operating more of the infrastructure themselves. Enterprises seeking a more integrated security and governance product may prefer TrustGate.

Comparing the Top Enterprise AI Gateways

No gateway wins every category for every organization. A company already standardized on Kong or Cloudflare may value integration with its existing infrastructure more than introducing another platform. An engineering team prioritizing self-hosting may find LiteLLM especially attractive, while Portkey is compelling when broad model access and LLM operations are central requirements.

Why TrustGate Takes the Top Spot in 2026

The enterprise AI gateway category is changing quickly. Routing requests to several LLM providers is becoming a baseline capability rather than the feature that distinguishes the strongest platforms.

The harder problem is controlling what happens when AI systems become agents. Those agents may call models, access company tools, interact with sensitive information and delegate tasks to other agents. At that point, enterprises need to know not only which model handled a request but also who initiated it, which resources an agent was allowed to access and what happened across the full chain of activity.

That is where NeuralTrust TrustGate has the clearest advantage in this comparison. Its combination of AI Gateway, MCP Gateway and A2A Gateway capabilities creates a control layer designed around the broader agent ecosystem, while inline security, identity-aware access, auditability and flexible deployment address requirements that become increasingly important at enterprise scale.

Portkey, Cloudflare, Kong and LiteLLM are all credible choices with distinct strengths. For enterprises choosing an AI gateway specifically for the next stage of production AI, however, TrustGate's security-first, agent-aware architecture makes NeuralTrust the strongest overall choice in 2026.