What are the best AI gateway solutions for enterprise? (March 2026)

When you're building enterprise AI infrastructure, you're managing failover, enforcing compliance, tracking token usage, and making sure a bad prompt doesn't ship to customers. Some gateways are lightweight proxies. Others include full development environments with testing and versioning. Either way, you need to know what breaks when you're running agents at scale.

TLDR:

• AI gateways route requests across model providers and add observability, but most stop there.

• Logic combines gateway routing with typed APIs, automated testing, and version control.

• Portkey and Kong offer routing and observability without built-in testing infrastructure.

• Logic is a spec-driven system that turns natural language into production-ready agents with full lifecycle management.

What is an AI gateway?

An AI gateway is middleware between your application and AI model providers. It handles prompt management, token counting, cost tracking, and routing across multiple LLMs. When your app calls Claude or Gemini, the gateway intercepts the request. It can route to the cheapest model, enforce rate limits, log inputs and outputs, cache responses, and fail over if the primary provider is down.

When you need to switch providers, track spending, add caching, or meet compliance requirements, you can either build it yourself or use an existing gateway. AI gateways range from lightweight proxies that unify API calls to full agent development environments with built-in testing, versioning, and observability.

How we ranked AI gateway solutions

We ranked each solution on six criteria:

• Multi-provider support routes requests across OpenAI, Anthropic, Google, and other providers without rewriting code. Vendor lock-in becomes expensive when a provider raises prices or has an outage.

• Intelligent routing covers cost optimization, latency-based model selection, and automatic failover. Most gateways proxy requests. The better ones make intelligent routing decisions that save money and keep things running when a provider goes down.

• Observability includes logging, execution history, and request tracing. If you can't see what your AI calls are doing, you're debugging blind.

• Security and governance include rate limiting, access controls, and compliance certifications. Enterprise buyers need SOC 2, HIPAA support, and audit trails.

• Deployment flexibility determines whether you can self-host, use a managed cloud, or both. Some teams have data residency requirements that rule out SaaS-only options.

• Production readiness covers uptime guarantees, documentation quality, and whether real companies are running it at scale.

Best overall AI gateway: Logic

Logic routes AI requests across OpenAI, Anthropic, Google, and Perplexity based on task complexity and cost. When a provider has an outage, requests fail over automatically. You write a spec describing what the agent should do, and Logic generates a REST API with strict input/output schemas, synthetic test cases, and version control.

The gateway layer handles execution caching, token tracking, and request logging. When you send identical inputs, Logic returns the cached result instantly without making a new LLM call. Every execution stores the full audit trail: inputs, outputs, reasoning, latency, token count, and which model ran it. Schema validation runs at the boundary.

Output always matches your defined structure. If you need HIPAA compliance, Logic restricts routing to BAA-covered models only.

You get observability, routing, testing, and deployment infrastructure without building any of it yourself. The API works like any REST endpoint your team already calls.

Portkey

Portkey is an AI gateway and control plane for teams running multi-provider AI workloads. It connects to over 2,000 models across providers and adds observability, guardrails, and governance on top of the routing layer.

Portkey unifies API calls across OpenAI, Anthropic, Google, and hundreds of other providers. The gateway handles request routing with fallback logic, load balancing, and conditional rules. You get detailed logs, latency metrics, and cost analytics for every request. Guardrails include content filtering, PII redaction, and policy enforcement.

It's good for teams who need deep observability and compliance controls in production. Early-stage prototypes may find it heavier than needed, and enterprise governance features require higher-tier plans.

Portkey handles routing and observability, but you're still responsible for prompt engineering, testing, and API design.

Kong AI Gateway

Kong Gateway is an API gateway that extends to AI traffic management. It connects to existing API infrastructure and adds multi-model routing, semantic security, and analytics for LLM requests.

Kong routes requests across multiple model providers. The gateway includes semantic caching to reduce redundant calls and prompt guards for content filtering. Pre-configured dashboards give you observability into request patterns, latency, and costs. MCP server support lets AI agents access tools through Kong's routing layer.

This works well for teams already running Kong for API management who want to route AI traffic through the same infrastructure. It requires deploying and configuring Kong, which means someone on your team needs to know how to manage it. If you're not already using Kong, you're adopting an entire API management layer just to get AI gateway features.

Kong makes sense if you're already using it for API management. Starting from scratch means choosing between an API gateway with AI features and an AI gateway with broader API management.

LiteLLM

As a Python library and proxy server, LiteLLM unifies access to 100+ models using an OpenAI-compatible format. It handles authentication, load balancing, and spend tracking across providers with virtual keys and per-team cost limits.

The proxy translates requests into provider-specific formats while keeping your code OpenAI-compatible. It tracks spending per key or team, routes requests with fallback rules, and logs every call. Setup is fast when you're working in Python and want to test different models without rewriting integrations.

Teams prototyping across multiple providers who need a way to compare models without building conversion layers will find this approach efficient. The Python-first design and OpenAI-compatible interface reduce integration friction during exploration.

LiteLLM provides multi-provider access quickly but lacks the performance and governance infrastructure most teams need at scale.

Azure API Management

Azure API Management applies traditional API gateway features to AI traffic. It's an API management layer that extends to LLM requests when you're running Azure OpenAI endpoints.

Azure API Management adds token-based rate limiting, semantic caching, and load balancing across Azure OpenAI deployments. Policies handle content safety checks before requests hit the model. You get metrics, logs, and cost tracking through Azure Monitor.

It works well for teams already running Azure infrastructure who want AI traffic routed through the same management layer handling their other APIs.

The AI features require Azure OpenAI Service, which means switching to Anthropic, Google, or self-hosted models requires replacing the gateway entirely. AI-specific capabilities also require higher-tier plans.

Azure API Management works if you're committed to staying in Azure. If you need multi-cloud flexibility or want to route across non-Azure providers, you'll be building around its constraints instead of working with them.

Feature comparison table of AI gateway solutions

Why Logic is the best AI gateway for building production agents

Give Logic a spec, get a production agent. Logic is the only spec-driven AI gateway that handles the complete agent lifecycle. Where other gateways stop at traffic management and observability, Logic delivers all six properties of production agents: reliable responses via typed API contracts, automated testing with synthetic test generation, immutable version control with one-click rollback, full observability of every execution, intelligent model routing across providers, and production-ready deployments that update agent behavior without redeploying code.

You describe what your agent should do in a spec. Logic generates a production API with typed schemas, auto-generated tests, prompt versioning, rollback capabilities, and execution logging. Tests run automatically before spec changes go live. When a request fails, you see the full execution trail. Rollbacks take one click.

Teams using Portkey or Kong still write orchestration code, manage their own testing, and manually version prompts. Logic handles those tasks so you can focus on what the agent should do instead of how to deploy it reliably. If you need a lightweight proxy for multi-provider routing, LiteLLM or Kong works. If you're building agents that need to work consistently in production, Logic gives you the complete stack without assembling it from separate tools.

Final thoughts on selecting an AI gateway

Most AI gateways handle routing and observability. Logic delivers the complete production stack. Describe what your agent should do in a spec, and Logic generates a production-ready endpoint with typed schemas, automated tests, versioning, rollbacks, and execution logging. The resulting API works like any REST endpoint your team already calls, but with agent intelligence built in.

FAQ

Which AI gateway should I choose if I need a self-hosted deployment?

Kong AI Gateway and LiteLLM both support self-hosted deployment, while Logic, Portkey, and Azure API Management are cloud-only solutions. If data residency or on-premise requirements are non-negotiable, start with Kong or LiteLLM.

How do I decide between a lightweight proxy and a complete agent development environment?

If you just need multi-provider routing and basic observability, LiteLLM or Kong works fine. If you're building agents that need testing, versioning, and production reliability, Logic provides the complete development lifecycle without assembling separate tools.

What's the difference between traditional API gateways and AI-specific gateways?

Traditional API gateways like Azure API Management or Kong were built for REST traffic and extended to handle AI requests. AI-native gateways like Logic and Portkey were designed from scratch to meet LLM-specific needs: prompt versioning, non-deterministic output testing, and token-based cost tracking.

Can I switch AI gateway providers later without rewriting my integration?

It depends on how tightly your code is coupled to the gateway's API. Logic generates standard REST endpoints that work like any API your team already calls, making migration straightforward. LiteLLM keeps your code OpenAI-compatible, so switching to another OpenAI-compatible gateway is easy. Azure API Management locks you into Azure-specific patterns, making migration more difficult.

Which gateway is best for teams without AI infrastructure experience?

Logic requires the least infrastructure knowledge because it handles API generation, testing, and deployment automatically from specs. Kong and Azure API Management assume you already know how to configure API gateways. LiteLLM requires Python expertise and manual scaling setup.