Los 11 mejores AI Gateways para la seguridad de la IA empresarial en 2026

AI is now in production inside the enterprise, and the traffic it generates (calls to LLM providers, tool invocations through the Model Context Protocol, and agent-to-agent delegations) has to pass through something that can route it, govern it, secure it, and record it. That something is the AI gateway.

As organizations move from pilot projects to agents that read databases, send messages, and act on a user's behalf, the gateway becomes the one place where reliability, cost control, observability, and above all security can attach.

This guide compares eleven of the most relevant AI gateways for enterprise AI security in 2026, starting with NeuralTrust TrustGate and then covering ten other platforms, so security and platform leaders can understand where each one fits.

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TL;DR

• An AI gateway is the control layer between your applications or agents and the AI services they call. It centralizes routing, policy, security enforcement, cost control, and observability so every team does not rebuild the same plumbing.
• The eleven gateways in this guide occupy very different niches, from security-first control planes to open-source infrastructure proxies, LLM-observability gateways, and vertically integrated enterprise platforms.
• NeuralTrust TrustGate is the security-first option, built by a security company, with a Security Engine that attaches to every route and coverage of LLM, MCP, and A2A traffic in one model.
• Infrastructure-first open-source gateways (Solo.io agentgateway, LiteLLM, Kong, Apache APISIX, HAProxy) excel at routing, performance, and control, with security delivered largely through integrations, plugins, or a WAF.
• LLM-observability gateways (Portkey, Helicone) shine on analytics, cost tracking, and developer experience, though runtime security depth varies and one of them is now in maintenance mode.
• Broad platforms (TrueFoundry, MLflow) place the gateway inside a larger system for model serving, the ML lifecycle, or regulated-industry governance.
• There is no single best gateway, only the best fit for your primary problem: security enforcement, open infrastructure, observability, or a managed platform.

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What Is an AI Gateway?

An AI gateway is a specialized control layer that sits between your applications and agents on one side and the AI services they consume on the other, most commonly LLM providers such as OpenAI, Anthropic, and Google, but increasingly also MCP tool servers and other agents. Conceptually it is the AI-native cousin of the API gateway: where an API gateway is the checkpoint for ordinary service traffic, an AI gateway is the checkpoint for AI traffic, which behaves differently (token-based costs, non-deterministic outputs, prompt-level risks, and multi-step agent workflows) and therefore needs its own control point.

A capable AI gateway typically does four things. It makes AI delivery reliable, by centralizing provider connections, routing, failover, retries, and caching so individual applications do not each re-implement them. It controls cost, through token-based rate limiting, budgets, and usage attribution by team or key. It provides observability, by logging and tracing every request so cost, latency, and behavior are visible in one place. And, most important for this guide, it gives security and compliance somewhere to attach, a single point where policy is enforced and where what an agent actually did can be audited.

The gateways in this comparison differ most in that last dimension. Some treat security enforcement as the reason the gateway exists; others focus on routing, observability, or platform breadth and treat security as an integration or an add-on. Understanding that difference is the key to choosing well, and it is the lens this guide uses throughout.

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Comparison at a Glance

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The 11 Best AI Gateways in 2026

1. NeuralTrust TrustGate

NeuralTrust TrustGate is an AI gateway built by a security company, and that origin is its organizing principle. It sits between agents and the services they call (LLM providers, MCP servers, and other agents) and is designed to be the one place where routing, policy, security, and observability attach across all three kinds of AI traffic. Its core abstractions are Consumers, Providers, Routes, and Policies: provider connections are configured once and reused, while routing, failover, retries, and caching live in the gateway rather than in each application's code.

What sets TrustGate apart is that a Security Engine attaches to every Route. When one is attached, NeuralTrust's own runtime security product (TrustGuard) inspects each request inline and executes an allow, block, or transform decision before the request reaches its target, with conversation-level and multi-turn analysis positioned as a core capability rather than isolated per-request filtering. Security findings then render as first-class spans in the same trace tree as operational telemetry, so the platform team and the security team read the same data.

• Runtime security: First-party engine (TrustGuard) attached inline to every route; multi-turn analysis as a core concept.
• Protocol coverage: LLM, MCP, and A2A traffic governed in one model.
• Posture: A companion product, TrustLens, discovers and assesses AI running outside the gateway's path across the estate.
• Deployment: Open-source (Apache 2.0) self-hostable core, with managed and hybrid (VPC, air-gapped) options at the enterprise tier.
• Best for: Security-led and platform buyers who want an enforcement substrate, not just an operational convenience.

TrustGate is designed to layer alongside existing API gateways rather than replace them, governing AI-specific traffic while general microservice traffic stays on whatever is already in place. It is worth noting that the deepest content detection is TrustGuard (a separate attachable engine), not the bare gateway, so buyers should validate specific detections against TrustGuard's documentation.

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2. Solo.io agentgateway

Solo.io's agentgateway is a Kubernetes-native, high-performance data plane written in Rust, and it is now a Linux Foundation project with broad industry backing. It is the most technically rigorous multi-protocol open-source competitor, built for platform engineers who own the deployment layer and want AI traffic governed with the same rigor as service-mesh traffic.

Its standout capability for security teams is MCP tool governance: a CEL-based policy engine that enforces tool-level access control keyed on JWT claims, plus tool-list filtering so an agent only sees the tools it is authorized to use. Content security, by contrast, is delivered through external integrations (OpenAI Moderation, AWS Bedrock Guardrails, Google Model Armor) and regex rather than a first-party engine.

• Runtime security: External guardrail integrations plus regex; no first-party detection engine.
• Protocol coverage: Strong MCP tool RBAC; A2A supported.
• Foundation: Apache 2.0, Linux Foundation project with wide backing.
• Best for: Kubernetes-native platform teams that value open governance and infrastructure rigor.

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3. Portkey

Portkey is a widely adopted LLM gateway and observability platform positioned as a control plane for AI, with guardrails, PII redaction, and audit trails built into the gateway layer. It routes to a very large catalog of models (it cites 1,600+ models across 40+ providers), and its gateway core was open-sourced under Apache 2.0.

The defining fact for enterprise buyers is corporate: Portkey was acquired by Palo Alto Networks, with the deal closing in May 2026, and it now serves as the AI Gateway for Prisma AIRS. Its runtime security today centers on guardrails and PII redaction, with deeper security capabilities converging into Palo Alto's platform.

• Runtime security: Guardrails and PII redaction; deeper detection moving to Prisma AIRS.
• Protocol coverage: MCP Gateway with OAuth 2.1.
• Licensing: Apache 2.0 gateway core plus commercial platform, now part of Palo Alto Networks.
• Best for: Teams wanting a hosted, enterprise gateway with built-in guardrails, especially Palo Alto customers.

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4. LiteLLM

LiteLLM is one of the most popular open-source AI gateways, beloved for wrapping 100+ providers behind a single OpenAI-compatible endpoint with minimal friction. It is MIT-licensed at the core, has a large community, and processes enormous request volumes, making it a default choice for developers who want provider unification fast.

For security and governance, LiteLLM offers a guardrails framework that wraps external services (LLM Guard, Llama Guard, Lakera, Aporia, and the OpenAI and Google moderation APIs), along with a real MCP gateway that supports per-key, per-team, and tool-level permissions. Several of the strongest integrations and enterprise controls (SSO beyond a small user count, audit-log retention, Prometheus metrics) require the Enterprise license.

• Runtime security: Guardrails framework wrapping external services; top integrations are Enterprise.
• Protocol coverage: MCP gateway with per-key/team and tool-level permissions.
• Licensing: MIT core plus Enterprise tier.
• Best for: Developers who want the simplest path to many providers and will layer security on top.

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5. Kong AI Gateway

Kong AI Gateway is not a standalone product but a set of AI plugins on Kong Gateway, the mature, high-performance API gateway built on Nginx/OpenResty. For organizations already running Kong, adding AI routing and governance is a plugin install rather than a new platform, which is its central appeal.

Its AI plugins include AI Proxy and AI Proxy Advanced, semantic caching, a RAG injector, prompt decoration and compression, advanced rate limiting, and a PII sanitizer. Security-oriented plugins such as AI Prompt Guard (regex), semantic prompt and response guards, and the PII sanitizer, along with audit logging and guardrail integrations (AWS Guardrails, Google Model Armor, Lakera, Mistral), are largely Enterprise or Konnect features, while the base AI Proxy and logging are open source.

• Runtime security: AI Prompt Guard (regex), PII sanitizer, and guardrail integrations, mostly Enterprise.
• Protocol coverage: Can generate MCP tools from Kong-managed APIs; MCP Registry in Konnect.
• Licensing: Apache 2.0 core; AI security features require Enterprise/Konnect.
• Best for: Enterprises already invested in Kong for API management.

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6. TrueFoundry

TrueFoundry is a broad, Kubernetes-native enterprise AI platform in which the AI gateway is one component among model serving (vLLM, SGLang, Triton, KServe), an MCP gateway, an agent gateway, and prompt management. It recently acquired the open-source MLOps platform Seldon AI, adding a mature real-time ML serving foundation. Its model catalog is very large (the platform cites 1,000+ to 1,600+ models).

Its strongest dimension is deployment and data sovereignty: fully self-hosted, cloud-agnostic, Kubernetes-native, with tenant isolation physically backed by Kubernetes namespaces. Runtime security is a configurable guardrails framework combining built-in PII and toxicity checks with external integrations (OpenAI Moderation, AWS Guardrails, Azure Content Safety) and custom rules, rather than a dedicated first-party detection engine. Its MCP gateway (registry, tool-level RBAC, OAuth 2.0, federated identity, prebuilt connectors) is a genuine strength.

• Runtime security: Configurable guardrails (built-in plus external plus custom code).
• Protocol coverage: Strong MCP gateway; agent gateway covers A2A.
• Deployment: Kubernetes-native; VPC, on-prem, air-gapped; physical namespace isolation.
• Best for: Kubernetes-native enterprises consolidating models, agents, and tools on one platform.

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7. Apache APISIX

Apache APISIX is a high-performance, fully open-source API gateway and a top-level project of the Apache Software Foundation, built on Nginx/OpenResty with etcd for dynamic configuration. Its AI Gateway is a set of AI plugins that let teams manage AI traffic alongside traditional API traffic on the same platform. A notable differentiator is that all of its AI plugins, including the security-oriented ones, are open source, with no feature gate.

Its plugins include ai-proxy and ai-proxy-multi for multi-LLM routing, ai-rate-limiting, ai-prompt-guard (regex allow/deny that can scan full history), prompt decorators and templates, ai-rag, and content moderation via AWS Comprehend or Alibaba Cloud. For MCP, it offers MCP-to-HTTP bridging over Streamable HTTP, an openapi-to-mcp plugin, and per-consumer MCP tool access control. The commercial company API7.ai provides enterprise distributions and support.

• Runtime security: Regex prompt guarding plus external moderation; all AI plugins open source.
• Protocol coverage: MCP-to-HTTP proxy and per-consumer MCP tool ACL; A2A not documented.
• Licensing: Apache 2.0 (ASF project); commercial support via API7.
• Best for: Teams wanting a high-performance, vendor-neutral, fully open gateway.

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8. Zuplo

Zuplo is a programmable API gateway and API-management platform whose AI Gateway runs on the same policy engine, auth modules, and GitOps pipeline as its REST APIs and its dedicated MCP Gateway. The pitch is unification: govern every LLM call, agent, and MCP server on the same platform as your APIs, with one bill and one control plane.

Its standout feature is hierarchical dollar budgets, with nested organizations, teams, sub-teams, and apps each capped in USD and cascading hard stops before overspend. Policies are written in TypeScript with the full npm ecosystem and stored in Git. Runtime security is delivered through built-in pattern policies (prompt-injection, secret masking) plus an optional Akamai AI Firewall integration for deeper detection, and Zuplo is candid that prompt injection is mitigated, not eliminated.

• Runtime security: Pattern policies plus optional Akamai AI Firewall.
• Protocol coverage: Dedicated MCP Gateway with virtual per-team servers and tool allowlists.
• Deployment: Managed edge (300+ locations), managed dedicated single-tenant, or self-hosted; SOC 2 Type II.
• Best for: Teams governing AI on the same programmable platform as their APIs, with strong cost controls.

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9. MLflow AI Gateway

The MLflow AI Gateway is a component of MLflow, the widely adopted open-source platform for the machine learning and GenAI lifecycle (experiment tracking, model registry, prompt registry, evaluation, tracing). It provides unified access to 50+ providers, centralized encrypted API-key management, per-endpoint rate limiting, traffic splitting and fallbacks, and usage tracking where every request becomes an MLflow trace. It added RBAC over gateway resources in version 3.13.0.

The key context is that its center of gravity is the ML/GenAI engineering lifecycle, not runtime security. Content-level guardrails are described as an emerging area rather than a dedicated engine, and MLflow's own guidance recommends a reverse proxy plus an external auth layer for production. Observability is its genuine strength, because gateway traffic lives alongside evaluations, experiments, and model lineage in one system.

• Runtime security: Emerging guardrails; credential management, rate limiting, RBAC, and audit logging.
• Protocol coverage: MCP access emerging; A2A not a gateway feature.
• Licensing: Apache 2.0 (LF Projects); managed on Databricks and AWS. Does not run on Windows.
• Best for: Teams standardized on MLflow that mainly need governed model access and unified observability.

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10. HAProxy AI Gateway

HAProxy is one of the world's fastest and most widely used software load balancers and security ADCs, and its AI Gateway is a capability of the HAProxy One platform (HAProxy Enterprise as the data plane, HAProxy Fusion as the control plane) rather than a separate AI-native product. For teams already running HAProxy, extending it to AI traffic keeps the same proven performance, configurability, and observability.

Its documented AI capabilities center on cost control and WAF-based security: token-based rate limiting enforced consistently across clusters via the Global Profiling Engine, prompt-based routing where the HAProxy Enterprise WAF inspects each prompt for safety and data loss, strong API-key protection (hashing, intermediate keys, instant revocation), and 150+ metrics in HAProxy Fusion. Security is WAF-grounded rather than a first-party AI detection engine, and MCP and A2A are not documented AI gateway features today.

• Runtime security: HAProxy Enterprise WAF inspects each prompt; no first-party AI detection engine.
• Protocol coverage: MCP and A2A not documented AI gateway features.
• Licensing: Open-source community edition; AI gateway capabilities via HAProxy Enterprise.
• Best for: Organizations standardized on HAProxy that prize performance and deploy-anywhere flexibility.

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11. Helicone

Helicone is an open-source LLM observability platform and AI gateway, launched out of Y Combinator and famous for a single-URL integration: change the base URL and immediately get logging, cost tracking, and gateway features. Its gateway is a fast Rust proxy ("the NGINX of LLMs") providing access to 100+ models with smart routing, automatic fallbacks, response caching, and rate limiting, and its observability dashboard is polished and developer-loved.

The essential current context is that Mintlify acquired Helicone on March 3, 2026, and the product is now in maintenance mode: security updates, bug fixes, and new models continue, but new feature development has stopped. Its center of gravity is observability and cost control, not runtime security. It has no first-party detection engine or inline guardrail layer, MCP appears only as a data-export server, and A2A is not a gateway feature.

• Runtime security: Operational controls only; no first-party detection or guardrail layer.
• Protocol coverage: MCP as a data-export server; A2A not a feature.
• Licensing: Apache 2.0, self-hostable; in maintenance mode after the Mintlify acquisition.
• Best for: Teams wanting a simple, mature open-source observability and routing gateway for LLM apps.

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How to Choose the Right AI Gateway

No single gateway wins on every axis, so the practical approach is to name your primary problem first and let that narrow the field.

If your driver is a security mandate, the question is whether the gateway treats security enforcement as its reason for existing or as an add-on. NeuralTrust TrustGate is built around a first-party engine attached inline with multi-turn analysis; most infrastructure-first gateways deliver security through integrations, plugins, or a WAF, which can be strong but is a different model.

If your driver is open infrastructure and performance, the open-source proxies stand out: Apache APISIX (fully open, ASF-governed), Solo.io agentgateway (Kubernetes-native, Linux Foundation), LiteLLM (simplest provider unification), Kong and HAProxy (extend a battle-tested gateway or load balancer you may already run).

If your driver is observability and cost control, Portkey and Helicone lead on analytics and developer experience, with the caveat that Helicone is in maintenance mode and Portkey is now part of Palo Alto Networks.

If your driver is a broader platform, TrueFoundry consolidates models, agents, and tools on Kubernetes; MLflow places the gateway inside the ML/GenAI lifecycle.

Finally, weigh MCP and A2A governance if your roadmap is agentic. Coverage varies widely: some gateways govern MCP tool calls and agent-to-agent traffic as first-class concerns, while others treat MCP lightly or not at all.

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Key Takeaways

• An AI gateway is the control layer between applications or agents and the AI services they call, centralizing routing, cost control, observability, and security enforcement.
• The eleven gateways here fall into distinct categories: security-first control planes, open-source infrastructure gateways, LLM-observability gateways, and broad or vertically integrated platforms.
• NeuralTrust TrustGate is the security-first option, with a first-party Security Engine attached to every route and coverage of LLM, MCP, and A2A traffic in one model.
• Infrastructure-first gateways (Solo.io, LiteLLM, Kong, Apache APISIX, HAProxy) excel at routing, performance, and openness, with security typically delivered via integrations, plugins, or a WAF.
• Observability-first gateways (Portkey, Helicone) lead on analytics and developer experience, but note Portkey's Palo Alto acquisition and Helicone's maintenance-mode status.
• Platform plays (TrueFoundry, MLflow) place the gateway inside a larger system for model serving, the ML lifecycle, or regulated-industry governance.
• The right choice follows from your primary problem: security enforcement, open infrastructure, observability, or a managed platform, plus how much MCP and A2A governance your roadmap needs.

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Frequently Asked Questions

1. What is an AI gateway?

An AI gateway is a control layer that sits between your applications and agents and the AI services they call (LLM providers, MCP tool servers, and other agents). It centralizes routing, failover, and caching for reliability, enforces cost controls and policy, provides observability over every request, and gives security and compliance a single point to attach. It is the AI-native counterpart to an API gateway, designed for the token-based costs, non-deterministic outputs, and multi-step agent workflows that AI traffic involves.

2. How is an AI gateway different from an API gateway?

An API gateway manages fixed request-response traffic by URL path, rate-limits by request count, and secures conventional API calls. An AI gateway is built for LLM and agent traffic: it rate-limits by tokens rather than requests, tracks cost per team or key, can inspect prompts and responses for sensitive or adversarial content, and routes by prompt characteristics or model rather than by path. Many organizations run both, layering an AI gateway alongside their existing API gateway for AI-specific traffic.

3. Which AI gateway is best for enterprise AI security?

For a security-first mandate, NeuralTrust TrustGate is purpose-built around a first-party Security Engine attached inline to every route with multi-turn analysis, and covers LLM, MCP, and A2A traffic. Infrastructure-first gateways can be secured well through integrations, plugins, or a WAF, but that is a different model from a dedicated detection engine. The best fit depends on whether you want security enforcement built in or assembled.

4. Do I need an AI gateway if I only use one LLM provider?

Often yes. Even with a single provider, a gateway centralizes API-key management so keys are not scattered across code, enforces rate limits and cost controls, provides a complete audit trail, and gives you a single point to add security policy. It also future-proofs you: adding a second provider, an MCP tool server, or agent-to-agent traffic later becomes a gateway change rather than an application rewrite.

5. What is MCP and why does it matter for AI gateways?

MCP (Model Context Protocol) is a standard way for AI agents to call external tools and data sources. It matters because agentic systems increasingly act through tools, and each tool call is a security and audit event: which agent called which tool, with what arguments, and what came back. A gateway with MCP governance can enforce per-consumer tool access, limit which tools an agent can even see, and record every invocation, which reduces the blast radius of prompt injection and provides an audit trail.

6. What is A2A (agent-to-agent) traffic?

A2A refers to communication where one agent delegates work to another agent. As multi-agent architectures mature, this traffic needs the same governance as model and tool calls: policy enforcement and a trace of which agent delegated what to whom. Enterprise A2A adoption is still early, so several gateways do not yet treat it as a first-class feature, while a few govern it alongside LLM and MCP traffic so the control layer is already in place when agent-to-agent traffic becomes routine.

7. Are open-source AI gateways secure enough for enterprises?

They can be, but security depth varies. Some open-source gateways deliver strong operational controls and tool governance, with content security provided through external integrations or plugins. Others gate their security features behind a commercial tier. Open source gives you transparency, self-hosting, and no license cost, but you own the operational burden and often assemble the security layer yourself. Evaluate whether the runtime detection you need is native, an integration, or an enterprise add-on.

8. What is the difference between a runtime security engine and guardrails?

Guardrails are typically configurable checks (regex patterns, PII filters, calls to external moderation services) applied to inputs and outputs. A first-party runtime security engine is a dedicated detection system engineered as the platform's core purpose, often with conversation-level and multi-turn analysis rather than isolated per-request filtering. Both aim to block unsafe content, but a dedicated engine is designed for LLM-specific and agentic threats and is maintained as the product's reason for existing.

9. Should the AI gateway replace my existing API gateway?

Usually not. The common pattern is to layer an AI gateway alongside an existing API gateway: general microservice traffic stays on Kong, Apigee, AWS API Gateway, or whatever is already in place, while AI-specific traffic (LLM, MCP, A2A) flows through the AI gateway. This gives you an AI-native control plane without displacing existing investment, and it cleanly separates the two kinds of traffic by their very different requirements.

10. How do AI gateways help control AI costs?

AI gateways control cost through token-based rate limiting (capping token consumption rather than request counts), budgets and quotas attributed to teams, users, or keys, response caching to avoid paying for repeated computations, and routing that can send simpler queries to cheaper models. Because every request passes through one point, the gateway also gives finance and platform teams accurate, centralized usage and cost attribution instead of scattered provider dashboards.

11. Can I use more than one AI gateway together?

Yes, and some teams do. For example, an observability-focused gateway can sit alongside a security-first control plane, or a broad platform can handle model serving while a dedicated gateway handles runtime enforcement. The main caution is added latency and operational complexity from stacking proxies, so it is worth being deliberate about which layer owns routing, which owns security, and which owns observability.

12. What should I evaluate first when choosing an AI gateway?

Start with your primary problem: is it security enforcement, open infrastructure and performance, observability and cost control, or a broader platform? Then check protocol coverage (LLM only, or MCP and A2A too), the security model (first-party engine versus integrations or WAF), deployment and data-residency needs (self-hosted, VPC, air-gapped), licensing and cost, and product trajectory (actively developed, acquired, or in maintenance mode). Matching those to your roadmap matters more than any single feature count.

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About the Author

Alessandro Pignati is Lead AI Security Researcher at NeuralTrust, where he leads research on AI and agentic security, advancing techniques to evaluate and secure large language models and autonomous AI systems. He specializes in adversarial machine learning, AI red teaming, LLM security, and AI safety, contributing to the development of secure and trustworthy AI.

NeuralTrust is an AI agent security platform, recognized in the Gartner 2025 Market Guide for Guardian Agents. Headquartered in Barcelona with ISO 27001 certification.