Once an LLM feature goes to production, the bill arrives. Enterprise spending on LLM APIs has climbed into the billions and keeps rising, and every unoptimized call compounds into wasted budget and slower responses. An AI gateway sits between your application and the model providers and gives you the controls that raw provider SDKs do not: routing, failover, caching, rate limiting, and per-team cost visibility.

The category has matured fast. Some gateways are open-source proxies you run yourself, some are managed edge services that add caching with almost no code, and some are full API-management platforms extended for AI traffic. The right one depends on whether you want control, simplicity, or to consolidate with an existing API gateway you already operate.

This guide compares the AI gateways developers actually deploy: LiteLLM, Portkey, Cloudflare AI Gateway, Kong AI Gateway, Helicone, and Vercel AI Gateway. We cover architecture, routing and failover, caching, governance, pricing shape, and which fits which stack. Details are sourced from vendor pages as of May 2026 and should be re-verified before you commit.

1What an AI Gateway Actually Does

A gateway is a single endpoint your app calls instead of talking to each provider directly. That one indirection unlocks a stack of controls:

• Multi-provider routing. Send traffic to OpenAI, Anthropic, Google, or open models behind one API, and switch without changing application code.
• Failover and load balancing. When a provider errors or rate-limits you, automatically retry against a fallback so users do not see an outage.
• Caching. Serve repeated or semantically similar prompts from cache to cut both cost and latency.
• Cost control and governance. Per-team budgets, rate limits, virtual keys, and spend visibility so one runaway job does not blow the monthly budget.
• Observability. Logging and metrics for every call, often the entry point to a fuller observability stack.

2Self-Hosted Proxy vs Managed Edge vs API Platform

The decision usually comes down to control versus operational burden. Greenfield AI-only deployments lean toward LiteLLM or Portkey; teams already running an API platform like Kong lean toward extending it; and teams that want results today with minimal effort lean toward a managed edge gateway.

3LiteLLM: The Open-Source Standard

LiteLLM is the most widely adopted open-source gateway. It exposes a consistent, OpenAI-compatible interface across a long list of providers, so your code calls one API and LiteLLM translates. It runs as a Python SDK or a standalone proxy server with virtual keys, budgets, and logging, and because it is open source there is no per-call vendor fee.

Best for: teams that want an open-source, multi-provider gateway they control, especially greenfield AI deployments without an existing API platform.

4Portkey: Production Routing and Guardrails

Portkey is built for production from the start. It adds edge caching, automatic failover, semantic caching, guardrails, and rich observability on top of multi-provider routing. Community comparisons report it cutting round-trip latency meaningfully under load versus a self-managed proxy, thanks to edge caching and automatic failover. It is available as a managed service with self-host options.

Best for: production teams that want routing, caching, guardrails, and observability in one managed gateway without building it on top of LiteLLM themselves.

5Cloudflare AI Gateway: Edge Caching, Near-Zero Setup

Cloudflare AI Gateway is the managed-edge option. Point your provider calls through it and you get caching, rate limiting, analytics, and logging at Cloudflare's edge with almost no code change. For teams already on Cloudflare, or anyone who wants cost and latency wins fast, it is the lowest-friction entry into the category.

Best for: teams that want caching, rate limiting, and metrics with minimal effort, especially those already on Cloudflare. See our Cloudflare Workers guide for the broader edge stack.

6Kong AI Gateway: For Existing Kong Shops

Kong AI Gateway extends the established Kong API management platform to LLM traffic. For enterprises already running Kong, this is the natural path: the same operational model, observability pipeline, and security controls you already use for the rest of your APIs, now applied to AI calls. For greenfield AI-only deployments without a Kong commitment, a dedicated gateway is usually a better fit.

Best for: enterprises already standardized on Kong that want to govern AI traffic the same way they govern everything else.

7Helicone: Observability-First Gateway

Helicone started as an observability proxy and works well as a lightweight gateway: a one-line base-URL change routes traffic through it and gives you logging, caching, and rate limiting plus per-prompt and per-user cost breakdowns. If your priority is seeing and controlling spend rather than complex multi-provider routing, it is a fast win.

Best for: teams whose first goal is cost visibility and basic caching, with routing as a secondary concern.

8Vercel AI Gateway: For the Vercel Stack

Vercel AI Gateway is the natural choice for teams building on Vercel and the AI SDK. It provides multi-provider access, failover, and spend visibility with tight integration into the Vercel deployment and AI SDK workflow, so adding it to an existing Next.js app is minimal effort.

Best for: teams already deploying on Vercel and using the AI SDK who want a gateway that fits their existing workflow.

9Head-to-Head Comparison Table

Feature sets and pricing move quickly here. Confirm specifics against each vendor's current documentation.

10Decision Framework

• Want full control and no per-call fee: LiteLLM, self-hosted.
• Want production routing, caching, and guardrails managed: Portkey.
• Want caching and metrics with near-zero setup: Cloudflare AI Gateway, especially if already on Cloudflare.
• Already run Kong for your APIs: Kong AI Gateway to keep one operational model.
• First priority is cost visibility: Helicone.
• Building on Vercel with the AI SDK: Vercel AI Gateway.

11Where the Gateway Sits in Your Stack

The gateway is a single chokepoint between your app and every model provider. That position is exactly what makes routing, caching, and cost control possible without touching application code.

Pair the gateway with an observability layer for the full picture. Our LLM observability comparison covers that side of the stack.

12Why Lushbinary for AI Gateways

We stand up AI gateways for clients to control LLM cost and reliability in production. We pick the gateway that fits your stack and scale, configure routing and failover across providers, and wire in caching and per-team budgets so spend stays predictable as usage grows.

What we typically deliver:

• Gateway selection matched to your stack, scale, and control needs
• Multi-provider routing and automatic failover configuration
• Prompt and semantic caching to cut cost and latency
• Per-team budgets, virtual keys, and rate limits
• Self-hosted LiteLLM or Portkey deployments for data-sensitive teams

Sources

• LiteLLM documentation
• Portkey pricing
• Cloudflare AI Gateway docs
• Kong AI Gateway
• Helicone pricing
• Vercel AI Gateway docs

Content was rephrased for compliance with licensing restrictions. Pricing, features, and latency claims sourced from official vendor pages and community comparisons as of May 2026 and may change. Latency figures vary by configuration and load. Always verify on the vendor's site and test against your own workload before committing.

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