Orq.ai vs Kong AI Gateway
Which AI Gateway Fits Production AI Best?
Kong AI Gateway extends Kong's API gateway to AI traffic. It gives teams one endpoint for LLM, MCP and agent-to-agent traffic, with routing, caching and guardrails delivered as gateway policies. For an organization that already runs Kong, it is a natural next step.
The Orq.ai AI Gateway sits in the same place in the stack but comes from a different starting point. It is built for AI traffic first and sits inside a platform that also covers observability, evaluation, governance and managed agents.
Both products route AI requests across providers and apply policies on the way through. The difference shows up in what happens around the request: how spend is stopped, how MCP tool calls are guarded, how quality is measured and which jurisdiction your vendor sits under.
Quick verdict
Choose Kong AI Gateway if you already run Kong for your APIs and want AI traffic on the same gateway, under the same operating model and the same team.
Choose Orq.ai if you want an AI-native control plane with hard budget limits, guardrails on MCP tool calls, built-in evaluation and an EU vendor, without running a general-purpose API gateway to get there.
Orq.ai vs Kong AI Gateway at a glance
Capability | Orq.ai | Kong AI Gateway |
Model provider access | 500+ models from 30+ providers behind one OpenAI and Anthropic compatible API | One endpoint across multiple LLM providers, configured as AI Models and policies |
Routing and reliability | Smart Router, fallbacks and retries, load balancing, routing rules and response caching | Load balancing and failover across providers and models, plus semantic caching. Strong routing depth from its API gateway roots |
Budget enforcement | Budgets per workspace, project, user, API key, provider or model, with alerts or a hard block | AI rate limiting by consumer, group, model and provider. Kong's docs state that cost is "only reflected during the next request" |
Guardrails and PII | Guardrail rules and PII redaction at the gateway, including external policy engines | Prompt and response guards, PII sanitizing, and connectors to AWS, Azure and GCP safety services |
MCP governance | MCP Gateway with team-scoped tools, guardrails on every tool call, kill switches and a tool-level audit trail | MCP traffic through the AI MCP Proxy. Kong's docs list applying guardrails to MCP requests and responses as not supported |
Observability and tracing | Built-in traces with cost, tokens and latency per request, native OpenTelemetry and MCP tool tracing | Analytics in Konnect, with traffic metrics and integrations into your existing monitoring stack |
Evaluation and experiments | Experiments, datasets, 40+ prebuilt evaluators, online scoring and red teaming | Not part of Kong's AI Gateway documentation. Teams add separate tooling |
Deployment options | EU SaaS, your own VPC, or on-premises including air-gapped (VPC and on-premises on Enterprise) | Konnect hosted, hybrid or self-hosted. Some AI Gateway 2.x entities, such as MCP upstream-server mode, have no self-hosted equivalent |
Vendor jurisdiction | EU entity with an EU cap table and no US parent company | US-headquartered vendor. EU data handling depends on the region and deployment you choose |
Best fit | Teams that want one AI control plane for models, tools, quality and spend, especially in regulated European organizations | Platform teams already standardized on Kong that want AI traffic on their existing API gateway |
Where teams start to hit limits with Kong AI Gateway
Kong AI Gateway works well when the goal is to bring AI traffic under an existing gateway practice. The questions tend to change once AI spend and agents grow beyond what a traffic policy can answer.
Spend is often the first pressure point. Kong's rate limiting documentation says the cost of a request is only reflected during the next request, so a limit takes effect after the request that crossed it. For a team trying to stop a runaway agent, the moment of enforcement matters.
MCP is the next one. Kong can proxy MCP traffic, but its own scope of support for the AI MCP Proxy lists guardrails on MCP requests and responses as not supported. As agents move from answering questions to calling tools, that gap sits on the riskiest path.
The third is quality. A gateway can tell you what was sent, what it cost and how long it took. It does not tell you whether the answer was any good.
Teams in that position often find themselves asking:
What stops this agent before it overspends, rather than after?
Which guardrails apply when an agent calls a tool?
Did the new model or prompt actually improve our results?
Which of these features work in the deployment model we are required to use?
The biggest difference: API gateway extended to AI vs AI control plane
The biggest difference between Orq.ai and Kong AI Gateway is what each product treats as its center.
Kong starts from API traffic management. AI capabilities arrive as policies on a gateway, which is a real strength if your platform team already operates Kong and wants one way of working across APIs and AI.
Orq.ai starts from the AI workload. Routing, budgets, guardrails, MCP tool access, traces and evaluation are parts of one product, so a policy, a trace and an evaluation score all refer to the same request.
In practice, a team on Kong can govern AI traffic very well and then add separate tools for evaluation and experimentation. A team on Orq.ai gets those in one place, and the gateway keeps working on its own if that is all they need at first.
Why Orq.ai is the better fit
Orq.ai becomes the better fit when AI is a production system of its own rather than one more kind of API traffic.
It is a strong choice for teams that:
want budgets with a hard block per workspace, project, user, API key, provider or model
are putting agents in front of real tools and need guardrails on every MCP tool call, kill switches and a tool-level audit trail
want to measure quality with experiments and evaluators in the same platform that routes the traffic
need an EU vendor as well as EU data handling
do not already run Kong and would rather not adopt a general-purpose API gateway to get AI governance
Final thoughts
Kong AI Gateway is a serious product, and for organizations already standardized on Kong it is a sensible way to bring AI traffic under existing practice. Its pedigree in gateway operations is real.
Orq.ai is designed for teams whose main question has moved from how to connect providers to how to control spend, tools and quality across AI in production. Moving a workload is usually a base URL change on an OpenAI-compatible API, so it is easy to test on one application first.
Want to see what that looks like for your setup?
Frequently Asked Questions
Is Kong AI Gateway the same as Kong Gateway?
No. Kong Gateway is Kong's API gateway. Kong AI Gateway is its product for LLM, MCP and agent-to-agent traffic, with its own policies and configuration model.
Can I use Orq.ai if we already run Kong for our APIs?
Yes. Kong can keep managing your API traffic while AI requests go through the Orq.ai AI Gateway, which exposes an OpenAI and Anthropic compatible API.
Does Orq.ai support on-premises deployment like Kong?
Yes, on the Enterprise plan. Orq.ai can run in your own VPC or fully on-premises, including air-gapped environments.