Top 5 AI Gateways in 2026

This guide compares the five AI gateways enterprise teams are actually evaluating in 2026: Lunar.dev, Portkey, Kong AI Gateway, LiteLLM, and TrueFoundry.

Most "AI gateway" comparisons rank the same thing: which proxy routes LLM calls to the most providers with the least latency. That was right two years ago. Agents now invoke tools, hit MCP servers, and spend credentials downstream of every model response, mostly without a human in the loop. The useful question in 2026 is how much of that interaction the gateway can actually see and govern.

AI gateways have crossed from convenience to essential infrastructure, and the analysts are now saying so directly. Gartner notes that an AI gateway is not just an API gateway with a few added policies, and that most organizations won't have a single chokepoint for AI traffic. They'll need a distributed architecture governed by one control plane. That is the exact gap this guide is about, and Gartner names lunar.dev among the specialist AI gateway vendors building for it.

TL;DR

This guide ranks the five gateways enterprise security and IT teams are actually evaluating:

• Lunar.dev is best overall, and the only option here that governs the full AI interaction (LLM, MCP, and API traffic) from a self-hosted control plane.
• Portkey is best for shipping LLM applications fast across a broad model catalog.
• Kong AI Gateway is best if you already run Kong in Kubernetes.
• LiteLLM is best as an open-source, self-hosted model-access layer for developers.
• TrueFoundry is best for enterprises wanting AI ops and governance in one platform.

What an AI gateway actually does in 2026

An AI gateway is middleware between AI applications and the models, tools, and APIs they consume. It centralizes four jobs otherwise scattered across every app and agent: routing, security, access control, and observability.

The 2024 generation handled one hop: the model API call. The 2026 generation has to cover tool invocation, MCP servers, and the audit trail across both. MCP, the standard agents use to reach tools, has surpassed 97 million monthly downloads. Gateways that stop at the model API can no longer see most of what an agent actually does.

What to look for in an AI gateway

Five questions separate a real AI gateway from a model router with a dashboard.

1. Governance scope. LLM call only, or the full chain from user to agent to model to MCP server to tool?
2. Security controls. Can it inspect, redact, and filter traffic in flight? Does it map to the OWASP Top 10 for LLM applications?
3. Observability and lineage. Can you reconstruct who did what across user, agent, model, tool, and data?
4. Access control. Can you scope permissions per user, per agent, and per tool?
5. Deployment. Self-host within your boundary, or at the vendor edge?

The 5 best AI gateways in 2026

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1. Lunar.dev AI Gateway

Lunar.dev's AI Gateway provides granular control for AI-generated traffic, built for production. Every call, prompt, and tool action runs through one self-hosted plane with full observability into token usage, cost, latency, and errors. Security, IT, and platform teams can put AI in front of regulated workflows with confidence.

The AI Gateway is one layer of Lunar's broader platform. MCPX is the enterprise MCP Gateway, covering identity, access, audit, and credentials across every MCP server and tool an agent reaches. The API Gateway covers everything else. The three share a single policy, identity, and audit layer, so the same controls apply to the agent beyond the model call into MCP and API traffic. Every other gateway on this list governs the LLM call and stops there.

Strengths

• Granular control for AI traffic. Rate limiting, priority queues, data sanitation, LLM model routing, and prompt and payload transformation, all enforced per user, app, or agent.
• Governs the full chain. One control plane across LLM traffic, MCP and tool calls, and API traffic. Competitors cover only the model hop.
• Self-hosted, production-ready. Runs in your VPC with SOC 2 (Enterprise) and air-gapped options.

Limitations: AI Gateway is Enterprise tier only. MCPX has an open-source tier.

Best for: security, IT, and AI enablement teams governing AI across the org, not just inside one app.

Proof points: up to 90,000 requests per second at ~4ms p99 (per the MCPX metrics docs), Google Cloud Partner Advantage at Premier level, and recognized by Gartner as a Representative Vendor.

If you are evaluating Lunar.dev for enterprise deployment, book a demo or explore the AI Gateway product page.

2. Portkey

Portkey is an LLM-native gateway built to get AI applications to production fast. It puts a unified interface in front of a large model catalog and adds observability, guardrails, prompt management, and governance on top.

Strengths

• Broad model coverage behind one OpenAI-compatible interface.
• Strong developer experience: prompt versioning, request logging, and guardrails are first-class.

Limitations

• Centered on the LLM API hop. Tool and MCP governance is not the core design point.
• Self-hosting is reserved for the Enterprise tier.

Best for: application teams shipping LLM features who want routing and observability in one place.

Not the right fit if: you need to self-host inside a regulated environment without paying for the Enterprise tier.

3. Kong AI Gateway

Kong AI Gateway extends Kong's mature API gateway with plugins that route and secure LLM traffic. If you already run Kong in Kubernetes, it adds AI handling to infrastructure you operate.

Strengths

• Mature gateway foundation: RBAC, rate limiting, large plugin ecosystem.
• Natural fit for organizations standardized on Kong.

Limitations

• AI capabilities are plugins on a general-purpose gateway, so governance depth tracks the plugin set, and many advanced AI features sit in the Enterprise tier.
• Built around API requests, not user-to-agent-to-tool lineage.

Best for: platform teams already invested in Kong. See our full Lunar.dev vs Kong comparison.

Not the right fit if: you want agent and tool governance as a first-class model rather than a plugin layer.

4. LiteLLM

LiteLLM is the open-source default for normalizing many providers behind one OpenAI-compatible API. Widely self-hosted as a lightweight internal proxy.

Strengths

• Open source, simple to deploy, supports 100+ providers.
• Large community and broad adoption as a model-access layer.

Limitations

• The Python proxy can show elevated P95 latency under high concurrency.
• A model-access layer first. Security depth is lighter than purpose-built enterprise gateways. Supply-chain risk is also higher: in March 2026, two malicious LiteLLM versions on PyPI shipped a credential-stealing backdoor (we covered it in our LiteLLM compromise breakdown).

Best for: developers who need a free, self-hosted multi-provider layer and will add governance separately.

Not the right fit if: you need governance, audit, and access control built in rather than bolted on.

5. TrueFoundry

‍TrueFoundry's AI Gateway provides a unified control plane for AI traffic, with one OpenAI-compatible interface across 1,600+ models. It runs as managed SaaS, hybrid, or fully self-hosted on-prem or air-gapped, with SOC 2 Type II and HIPAA compliance.

Strengths

• Routing and reliability for AI traffic: latency-based load balancing, automatic fallbacks, semantic caching, and geo-aware routing.
• Token-level cost governance with per-team budgets and quotas.

Limitations

• Centered on the LLM and MCP layers, not a general outbound API gateway.
• Proprietary platform with no open-source tier.

Best for: platform and ML engineering teams standardizing many teams and providers behind one governed endpoint.

Not the right fit if: you need open-source MCP infrastructure or a fully agent-scoped governance model rather than RBAC.

Side-by-side comparison

When do you need an AI gateway?

Conclusion

Governance has to follow the agent past the model API. Routing across providers is table stakes. The gateways that win in 2026 are the ones that see and control what the agent does after the model answers. Teams that invest in that layer now will have the infrastructure in place when their agent deployments scale.

Learn more

• OWASP Top 10 for LLM Applications
• The best open source MCP gateways in 2026
• Lunar.dev AI Gateway and AI agent governance

Ready to start your journey?

Govern all agentic traffic in real time with enterprise-grade security and control. Deploy on-prem, in your VPC, or hybrid cloud. Get started.

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