AI gateway comparison (2026)
The AI gateway has hardened from a side project into budgeted infrastructure, and analyst coverage has followed it there. The trouble with a young category is that every vendor defines it to match whatever they already built.
The broad definition — an intermediary between your applications and AI services that centralizes access, security, governance, and observability — covers products with genuinely different centers of gravity. Here is how the main options actually differ.
The six options
- OpenRouter — a model marketplace first. One key, hundreds of models, minimal ops. Weakest on governance and policy.
- LiteLLM — an open-source, self-hosted proxy. Maximum control, MIT-licensed, but you own the uptime, and its Python layer adds latency at high concurrency.
- Portkey — managed, observability-led, with guardrails and semantic caching out of the box.
- Kong AI Gateway— an AI plugin layer on top of Kong's API mesh. The right answer if you already run Kong; overkill otherwise.
- Vercel AI Gateway— tightly coupled to Vercel deploys. Excellent if your app already lives there, limiting if it doesn't.
- Neural Router — routing-led. Optimizes each request on cost, quality, and latency, and returns a receipt explaining the decision.
What actually differentiates them
Every option on this list speaks the OpenAI API, proxies to many providers, and does some form of caching and failover. Those are table stakes now. The real differences are narrower than the marketing suggests:
| Capability | Marketplace-led | Routing-led |
|---|---|---|
| Model breadth | Very high | High |
| Per-request optimization | Manual model choice | Automatic on cost/quality |
| Decision explainability | Usually none | Per-request receipt |
| Policy & governance | Basic keys/limits | Budgets, residency, quality floors |
| Ops burden | None (managed) | None (managed) |
| Best fit | Prototyping, breadth | Production cost & compliance |
How to choose
- Already on Kong or Vercel? Use their gateway first. Integration beats features when the integration is already paid for.
- Need self-hosting for compliance? LiteLLM, and budget for the ops.
- Want breadth with zero decisions? OpenRouter.
- Spending enough that routing matters?Once inference is a real line item, the question shifts from "can I reach this model" to "should this request have used it." That is a routing problem.
For a deeper look at two specific options, see OpenRouter vs LiteLLM and the best LLM gateways in 2026.